CN114537197A - V2V charging optimal matching method and system based on weighted bipartite graph - Google Patents
V2V charging optimal matching method and system based on weighted bipartite graph Download PDFInfo
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- 238000010276 construction Methods 0.000 claims description 4
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/66—Data transfer between charging stations and vehicles
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/00032—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries characterised by data exchange
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
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- Engineering & Computer Science (AREA)
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- Transportation (AREA)
- Mechanical Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention discloses a V2V charging optimal matching method and a system based on weighted bipartite graph, wherein the method comprises the following steps: acquiring information such as electric quantity required by a charging vehicle, electric quantity provided by a discharging vehicle, estimated arrival time of the charging and discharging vehicle and the like; calculating the matching degree between charging and discharging vehicles; constructing a bipartite graph based on the matching degree of the charge-discharge vehicle; a matching mechanism based on a KM (Kuhn-Munkres) algorithm is proposed to obtain the best matching result of the weighted bipartite graph. Compared with the traditional matching method, the algorithm provided by the invention comprehensively considers the influence factors such as the electric quantity required by the charging vehicle, the electric quantity provided by the discharging vehicle, the charging waiting time and the like, can realize the optimal matching of the charging and discharging vehicles, and improves the charging experience of users; in addition, the algorithm has the advantages of small calculation complexity, high robustness, good expansibility, high convergence speed and the like, can quickly feed back the matching result to the user, and can efficiently adapt to large-scale charging and discharging matching demand scenes.
Description
Technical Field
The invention belongs to the field of vehicle-to-vehicle charging, and particularly relates to a V2V charging optimal matching method and system based on weighted bipartite graph.
Background
With the increasing energy shortage and environmental pollution, new energy vehicles are receiving more and more attention due to their advantages of zero emission, low noise, high efficiency energy conversion, etc., and are gradually becoming the mainstream trend of the current automobile industry development.
The charging mode of the new energy automobile is divided into charging a Vehicle (G2V) by a Grid-to-Vehicle, charging the Vehicle (V2V) by the Vehicle and replacing a Battery (BS). The G2V charging mechanism is difficult to adapt to the vigorous development of the new energy automobile industry, and cannot effectively meet the charging requirement of a huge number of new energy automobiles. The BS mechanism has various problems such as uneven quality of the replaced battery, and is difficult to be popularized in a large scale. The V2V charging mechanism can be disconnected from the power grid to carry out charging service, and the charging mode is flexible and convenient, does not need expensive charging facility support, and has attracted extensive attention.
In the V2V charging, the matching of the charging and discharging vehicles is very important, but in the conventional method, information such as the arrival time interval of the charging and discharging vehicles is not considered, so that the charging and discharging vehicles cannot complete the optimal matching, and the time required for matching is longer and the user experience is poorer as the number of the charging and discharging vehicles at a parking spot increases. Therefore, the research of an efficient V2V charging optimal matching method is of great significance.
Disclosure of Invention
The invention aims to provide a V2V charging optimal matching method and system based on weighted bipartite graph, which solve the problems of poor user experience, long matching time and the like in the background art, exchange energy between vehicles as much as possible, minimize the arrival time interval of charging and discharging vehicles, and minimize the difference between the required energy of the charging vehicle and the supplied energy of the discharging vehicle.
The technical solution for realizing the purpose of the invention is as follows:
a V2V charging optimal matching method based on weighted bipartite graph comprises the following steps:
s1, acquiring the electric quantity required by the charging vehicle, the electric quantity provided by the discharging vehicle and the estimated arrival time of the charging and discharging vehicle;
s2, determining the matching degree between the charging and discharging vehicles;
s3, constructing a weighted bipartite graph taking the matching degree of the charge and discharge vehicles as weight;
and S4, determining the best matching result of the weighted bipartite graph through a KM algorithm.
A V2V optimal charging matching system based on weighted bipartite graph comprises a charging information acquisition module, a matching degree calculation module, a weighted bipartite graph construction module and a matching module; wherein:
the charging information acquisition module is used for acquiring the electric quantity required by the charging vehicle, the electric quantity provided by the discharging vehicle and the estimated arrival time of the charging and discharging vehicle;
the matching degree calculation module is used for determining the matching degree between charging and discharging vehicles;
the weighted bipartite graph construction module is used for constructing a weighted bipartite graph taking the matching degree of the charging and discharging vehicles as weight;
the matching module is used for determining the best matching result of the weighted bipartite graph through a KM algorithm.
Compared with the prior art, the invention has the following remarkable effects:
(1) according to the charging and discharging vehicle information, the conditions of electric quantity required by the charging vehicle, electric quantity provided by the discharging vehicle, charging waiting time and the like are comprehensively considered, the matching degree of the charging and discharging vehicles is calculated, and energy exchange between the charging and discharging vehicles is realized as much as possible; the arrival time interval of the charging and discharging vehicles is minimized, and the waiting time of a user is reduced; the difference between the required energy of the charging vehicle and the supplied energy of the discharging vehicle is minimized, and the benefit of a discharging vehicle owner is guaranteed;
(2) the weighted bipartite graph is established based on the calculated matching degree of the charge and discharge vehicles, the matching problem of the charge and discharge new energy vehicles is abstracted into the maximum weight matching problem based on the bipartite graph, and the optimal matching method of the weighted bipartite graph based on the KM (Kuhn-Munkres) algorithm has the advantages of small calculation complexity, high robustness, good expansibility, high convergence speed and the like, can quickly feed back the matching result to a user, and can efficiently adapt to large-scale charge and discharge matching demand scenes.
Drawings
Fig. 1 is a work flow chart of the optimal matching method for charging V2V based on weighted bipartite graph according to the present invention.
Fig. 2 is a schematic diagram of a specific process for calculating the optimal matching according to the present invention.
Fig. 3 is a schematic diagram illustrating a specific process for updating the tag according to the present invention.
Fig. 4 is a schematic diagram of the invention for realizing optimal matching of V2V charging.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment provides a V2V optimal charging matching method based on weighted bipartite graph, and the method comprises the steps that a vehicle with a charging demand can tell a cloud server of the required electric quantity and the estimated time of the vehicle reaching a parking lot through an internet of vehicles, then the cloud server can match charging and discharging vehicles according to the information, and finally the matching information is sent to the parking lot and the matched charging and discharging vehicles through the internet of vehicles.
With reference to fig. 1, the optimal matching method for charging V2V based on weighted bipartite graph provided by the present invention includes the following steps:
s1, acquiring information such as electric quantity required by the charging vehicle, electric quantity provided by the discharging vehicle, estimated arrival time of the charging and discharging vehicle and the like;
s2, calculating the matching degree between the charging and discharging vehicles;
s3, designing a bipartite graph with the matching degree of the charging and discharging vehicles as a weight;
s4, calculating the best matching result of the weighted bipartite graph through a KMKMKMKM (Kuhn-Munkres) algorithm.
In the step S2 of the present embodiment, the matching degree of the charge and discharge vehicle is divided into the following two cases according to the information such as the amount of electricity required by the charge vehicle, the amount of electricity provided by the discharge vehicle, the expected arrival time of the charge and discharge vehicle, and the like.
Case 1: when the energy required by the charging vehicle u is larger than the energy provided by the discharging vehicle v, or the time difference between the arrival time of the charging vehicle u and the arrival time of the discharging vehicle v is larger than the set maximum value, which means that the matching of the charging and discharging vehicles is invalid, the matching degree is defined as follows:
ω(u，v)＝0
where ω (u, v) represents the degree of matching between the charging vehicle u and the discharging vehicle v, and 0 represents the charging and discharging vehicle mismatch.
Case 2: when the energy required by the charging vehicle u is less than the energy that can be provided by the discharging vehicle v, and the time difference between the arrival of the charging vehicle u and the arrival of the discharging vehicle v is less than the set maximum value, which means that the matching of the charging and discharging vehicles is valid, the matching degree is defined as follows:
wherein
Representing the electric quantity required by a vehicle u to be charged, C representing the battery capacity of the new energy automobile, TG representing the maximum value of the arrival time difference of the charging and discharging vehicles allowed by the system, TG (u, v) representing the arrival time difference of the charging vehicle u and the discharging vehicle v, EGmaxRepresenting the maximum of the energy difference, EG, between all charged and discharged vehiclesminRepresents the minimum value of the energy difference between all the charging and discharging vehicles, and EG (u, v) is the difference between the energy required to charge the vehicle u and the energy that can be provided by the discharging vehicle v. Alpha is alpha1、α2、α3Is a weight coefficient for each object, each of which is greater than 0, and alpha1+α2+α31.ω (u, v) represents the degree of matching between the charging vehicle u and the discharging vehicle v, andand the larger ω (u, v) is, the more matched the charging vehicle u and the discharging vehicle v are.
In the step S3 of the present embodiment, a bipartite graph G ═ (U, V, E) and a matching subgraph graph M ═ (U, V, E ') are established according to the matching degree of the charge-discharge vehicle't) Where U denotes a charging vehicle set, V denotes a discharging vehicle set, and E denotes a set that can be matched between charging and discharging vehicles, that is, an edge E ═ U, V ∈ E, U ∈ U, V ∈ V in a bipartite graph. Each side has a weight, which is the degree of matching between charging and discharging vehicles. E'tRepresents the set of edges that have been matched, and E'tE, M represents a sub-graph of the bipartite graph G containing only the edges that have been matched.
The step S4 in this embodiment includes the following steps:
s4.1, initializing a label y for each vehicle;
s4.2, judging whether the maximum matching is found, if not, executing S4.3, otherwise, ending the algorithm;
s4.3, judging whether an augmentation road exists or not, if so, executing S4.4, otherwise, executing S4.5;
s4.4, searching an augmentation road from the unmatched charging vehicle, and updating the matching set E'tAnd matching subgraph M, then performing S4.2;
s4.5, updating the label, and adding the found new edge into the matching set E'tAnd matching sub-graph M, then S4.2 is performed.
In the step S4.1 of this embodiment, the tag of the vehicle to be charged is initialized to the maximum matching degree with all discharging vehicles, and the tag of the discharging vehicle is initialized to zero, which is expressed as follows:
where y (U) denotes a tag of the charging vehicle U, y (V) denotes a tag of the discharging vehicle V, U denotes the charging vehicle set, V denotes the discharging vehicle set, and ω (U, V) denotes the degree of matching between the charging vehicle U and the discharging vehicle V calculated in claim 2. Definitions e (u, v) is a candidate when y (u) + y (v) ═ ω (u, v).
In the step S4.5 of the present embodiment, as shown in fig. 3, in the case of finding the passing charging vehicle and the non-passing discharging vehicle in the course of the augmented road, the minimum value of the label y (u) of the charging vehicle, plus the label y (v) of the discharging vehicle, minus the weight ω (u, v) therebetween is calculated. Then, the minimum value is subtracted from the label y (u) of the charging vehicle which arrives in the process of finding the augmented road, and the minimum value is added to the label y (v) of the discharging vehicle which arrives in the process of finding the augmented road, and the label updating mode is as follows:
ψ＝min{y(u)+y(v)-ω(u，v)|u∈U′，v∈V/V′}
where U 'denotes a set of charged vehicles arrived during the search for an extended road, V' denotes a set of discharged vehicles arrived during the search for an extended road, ψ denotes a minimum value calculated in the set, and y (U) denotes a tag y (V) of the charged vehicle U denotes a tag of the discharged vehicle V. Finally add the newly found side of the minimum to E'tAnd M.
According to the implementation method, the optimal matching effect of the charging of V2V is shown in FIG. 4, wherein there are 3 charging vehicles and 4 discharging vehicles, and the numbers beside the connecting line represent the matching degree. By the above method, the optimal matching is realized, and the matching result is represented by thick lines in the figure, u1 and v4 are matched, u2 and v3 are matched, and u3 and v1 are matched.
Compared with the traditional matching method, the algorithm provided by the invention comprehensively considers the influence factors such as the electric quantity required by the charging vehicle, the electric quantity provided by the discharging vehicle, the charging waiting time and the like, can realize the optimal matching of the charging and discharging vehicles, and improves the charging experience of users; in addition, the algorithm has the advantages of small calculation complexity, high robustness, good expansibility, high convergence speed and the like, can quickly feed back the matching result to the user, and can efficiently adapt to large-scale charging and discharging matching demand scenes.
Of course, those skilled in the art will recognize that the above-described embodiments are illustrative only, and not intended to be limiting, and that changes and modifications may be made thereto without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A V2V charging optimal matching method based on weighted bipartite graph is characterized by comprising the following steps:
s1, acquiring the electric quantity required by the charging vehicle, the electric quantity provided by the discharging vehicle and the estimated arrival time of the charging and discharging vehicle;
s2, determining the matching degree between the charging and discharging vehicles;
s3, constructing a weighted bipartite graph taking the matching degree of the charge and discharge vehicles as weight;
and S4, determining the best matching result of the weighted bipartite graph through a KM algorithm.
2. The V2V optimal charging matching method based on weighted bipartite graph according to claim 1, wherein the matching degree of charging and discharging vehicles in step S2 is determined in two cases:
in case 1, when the energy required by the charging vehicle u is greater than the energy provided by the discharging vehicle v, or the time difference between the predicted arrival of the charging vehicle u and the predicted arrival of the discharging vehicle v is greater than a set maximum value;
in case 2, when the energy required by the charging vehicle u is less than the energy that can be provided by the discharging vehicle v, and the time difference between the expected arrival of the charging vehicle u and the discharging vehicle v is less than the set maximum value.
3. The V2V charging optimal matching method based on weighted bipartite graph according to claim 2, wherein the matching degree of case 1 is as follows:
ω(u,v)＝0
where ω (u, v) represents the degree of matching between the charging vehicle u and the discharging vehicle v, and 0 represents the charging and discharging vehicle mismatch.
4. The V2V charging optimal matching method based on weighted bipartite graph according to claim 2, wherein the matching degree of case 2 is as follows:
wherein
Representing the amount of electricity required by a vehicle u to be charged, C representing the battery capacity of the new energy automobile, TG representing the maximum value of the allowable charge-discharge vehicle arrival time difference, TG (u, v) representing the time difference between the predicted arrival of the charging vehicle u and the predicted arrival of the discharging vehicle v, EGmaxRepresenting the maximum of the energy difference, EG, between all charged and discharged vehiclesminRepresents the minimum value of the energy difference between all the charging and discharging vehicles, EG (u, v) is the difference between the energy required to charge vehicle u and the energy that can be provided by discharging vehicle v; alpha is alpha1、α2、α3Is a weight coefficient; ω (u, v) represents the degree of matching between the charging vehicle u and the discharging vehicle v, and the larger ω (u, v) represents the more matching between the charging vehicle u and the discharging vehicle v.
5. The weighted bipartite graph-based V2V optimal matching method for charging according to claim 4, wherein a1、α2、α3Are all greater than 0, and α1+α2+α3＝1。
6. The V2V charging optimal matching method according to claim 1, wherein the step S3 is implemented by charging and dischargingThe weighted bipartite graph taking the matching degree of the electric vehicle as the weight specifically comprises the following steps: establishing a bipartite graph G ═ U, V, E and a matching subgraph M ═ U, V, E't) Wherein U represents a charging vehicle set, V represents a discharging vehicle set, and E represents a set that can be matched between charging and discharging vehicles, that is, a side E ═ U, V ∈ E, U ∈ U, V ∈ V in a bipartite graph, each side has a weight, the weight is a matching degree between charging and discharging vehicles, and E'tRepresents the set of edges that have been matched, and E'tE, M represents a sub-graph of the bipartite graph G containing only the edges that have been matched.
7. The V2V charging optimal matching method based on weighted bipartite graph according to claim 1, wherein the step S4 specifically comprises:
s4.1, initializing a label y for each vehicle;
s4.2, judging whether the maximum matching is found, if not, executing S4.3, otherwise, finishing the algorithm and obtaining the best matching result;
s4.3, judging whether an augmentation road exists or not, if so, executing S4.4, otherwise, executing S4.5;
s4.4, searching an augmentation road from the unmatched charging vehicle, and updating the matching set E'tAnd matching subgraph M, then performing S4.2;
s4.5, updating the label, and adding the found new edge into the matching set E'tAnd matching sub-graph M, then S4.2 is performed.
8. The weighted bipartite graph-based V2V optimal matching method for charging according to claim 7, wherein the initializing a tag y for each vehicle in step S4.1 comprises: initializing the label of the vehicle to be charged to the maximum value of the matching degree between the label and all discharged vehicles, and initializing the label of the discharged vehicle to zero, wherein the label is expressed as:
wherein y (U) denotes a tag of a charging vehicle U, y (V) denotes a tag of a discharging vehicle V, U denotes a charging vehicle set, and V denotes a discharging vehicle set;
definitions e (u, v) is a candidate when y (u) + y (v) ═ ω (u, v).
9. The method for optimally matching V2V charging based on weighted bipartite graph according to claim 7, wherein the label updating method in step S4.5 comprises:
ψ＝min{y(u)+y(v)-ω(u，v)|u∈U′，v∈V/V′}
where U 'denotes a set of charged vehicles arrived during the search for an extended road, V' denotes a set of discharged vehicles arrived during the search for an extended road, ψ denotes a minimum value calculated in the set, y (U) denotes a tag of the charged vehicle U, and y (V) denotes a tag of the discharged vehicle V.
10. The V2V charging optimal matching system based on the weighted bipartite graph based on the method of any one of claims 1 to 9 is characterized by comprising a charging information acquisition module, a matching degree calculation module, a weighted bipartite graph construction module and a matching module; wherein:
the charging information acquisition module is used for acquiring the electric quantity required by the charging vehicle, the electric quantity provided by the discharging vehicle and the estimated arrival time of the charging and discharging vehicle;
the matching degree calculation module is used for determining the matching degree between charging and discharging vehicles;
the weighted bipartite graph construction module is used for constructing a weighted bipartite graph taking the matching degree of the charging and discharging vehicles as weight;
the matching module is used for determining the best matching result of the weighted bipartite graph through a KM algorithm.
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