Nothing Special   »   [go: up one dir, main page]

CN110949149A - Electric vehicle charging positioning method and system - Google Patents

Electric vehicle charging positioning method and system Download PDF

Info

Publication number
CN110949149A
CN110949149A CN201911270634.8A CN201911270634A CN110949149A CN 110949149 A CN110949149 A CN 110949149A CN 201911270634 A CN201911270634 A CN 201911270634A CN 110949149 A CN110949149 A CN 110949149A
Authority
CN
China
Prior art keywords
charging
electric automobile
model
nodes
expected
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201911270634.8A
Other languages
Chinese (zh)
Other versions
CN110949149B (en
Inventor
郑声俊
艾闯
李默涵
薛斐
赵明宇
孙广明
汪映辉
石进永
王刚
李�浩
李充
柯慧敏
王冬
李奕杰
张宇峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
NARI Group Corp
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
Hainan Power Grid Co Ltd
Original Assignee
NARI Group Corp
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
Hainan Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by NARI Group Corp, Nari Technology Co Ltd, NARI Nanjing Control System Co Ltd, Hainan Power Grid Co Ltd filed Critical NARI Group Corp
Priority to CN201911270634.8A priority Critical patent/CN110949149B/en
Publication of CN110949149A publication Critical patent/CN110949149A/en
Application granted granted Critical
Publication of CN110949149B publication Critical patent/CN110949149B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0204—Market segmentation
    • G06Q30/0205—Location or geographical consideration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • 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
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • 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
    • Y02T90/12—Electric charging stations
    • 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
    • Y02T90/14—Plug-in electric vehicles
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Accounting & Taxation (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Marketing (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Tourism & Hospitality (AREA)
  • Primary Health Care (AREA)
  • Game Theory and Decision Science (AREA)
  • Human Resources & Organizations (AREA)
  • Water Supply & Treatment (AREA)
  • Public Health (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Navigation (AREA)

Abstract

The invention discloses an electric vehicle charging positioning method and system, aiming at the problem of charging station site selection caused by randomness of the driving range of an electric vehicle and different charging requirements, establishing an expected flowing charging positioning model of the electric vehicle by analyzing the problem of deterministic flowing charging positioning, and carrying out heuristic solving analysis by using a tabu search algorithm aiming at the problem of electric vehicle random driving in the model so as to obtain an optimal positioning solution. Experimental data show that the tabu search algorithm can provide a high-quality solution in a short calculation time all the time, and theoretical support is provided for planning and positioning of the electric vehicle charging station.

Description

Electric vehicle charging positioning method and system
Technical Field
The invention particularly relates to an electric automobile charging positioning method and system, and belongs to the technical field of electric automobile driving safety.
Background
According to the report of the international energy agency, transportation accounts for nearly one fourth of the global energy-related carbon dioxide emission, so that the electric automobile becomes the best choice for replacing the traditional fuel oil automobile. At present, the holding capacity of electric vehicles is increased explosively, and as the last half year of 2019, the holding capacity of pure electric vehicles in China reaches 281 thousands of vehicles, which account for 81.74 percent of the total amount of new energy vehicles. The rapid development of electric vehicles urgently needs the construction of matched charging facilities, the number and the positions of charging stations need to be planned to reduce the investment cost of the charging facilities, and the charging requirements of regional electric vehicles are met in an optimized manner, and the optimization problem is called a mobile charging positioning problem. This problem is generally assumed to be deterministic and known when analyzing the driving range of an electric vehicle, but this does not correspond to the actual case of travel of an electric vehicle.
Therefore, the change of the driving range is more meaningful to be taken into the mobile charging positioning problem for practical application. In addition, the charging demand also has a non-negligible influence on the location problem of the charging station, and the coverage flow of the charging demand in the road network has a very high reference value for the location of the charging station.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a charging positioning method and a charging positioning system for an electric automobile, and solves the problems of difficult charging and random charging positions of the electric automobile.
In order to solve the technical problem, the invention provides a charging positioning method for an electric vehicle, which is characterized by comprising the following steps of:
by analyzing the problem of deterministic mobile charging positioning and considering the influence caused by the randomness of the driving mileage, an expected mobile charging positioning model of the electric automobile is established to ensure that the electric automobile realizes charging in all possible ranges;
and solving the expected mobile charging positioning model of the electric automobile to obtain the charging position of the electric automobile.
Further, the analyzing the deterministic flow charging location problem comprises:
the deterministic flow charging localization problem is described using the FRLM formula, which is expressed as follows:
Max∑q∈Qfqyq(1)
Figure BDA0002314057890000021
Figure BDA0002314057890000022
Figure BDA0002314057890000023
∑j∈N\N'xj＝p (5)
Figure BDA0002314057890000024
Figure BDA0002314057890000025
Figure BDA0002314057890000026
wherein Q is the journey, Q is the set of journeys of the electric automobile driver from the departure place to the destination in the road network, R is the journey of the electric automobile, fqRepresenting the flow of the electric vehicle in the journey q; y isqIndicates whether the trip q is covered; x is the number ofkIndicating whether a charging station is established at node k; x is the number ofjIndicating whether the charging station is opened;
Figure BDA0002314057890000027
represents from O'qTo D'qWhen passing after node k belongs to NqA set of nodes of (a); n is a set of road network nodes, N 'represents a set of repeating nodes, p is the number of charging stations to be built, O'qStarting point O for journey qqRepetition of, D'qFor journey q destination DqRepetition of (A), (B), (C) and (C), NqIs O'qAnd D'qThe set of nodes of the shortest path between them,
Figure BDA0002314057890000028
is from O'qTo D'qWhen passing before node l belongs to NqThe set of nodes of (a) is,
Figure BDA0002314057890000029
is a circulation section [ k, l]The length of the stroke q over which the stroke q runs,
Figure BDA00023140578900000210
are assigned variables.
Further, the influence of the randomness of the driving range is considered to include:
defining a continuous variable z taking into account the coverage probability of a tripq∈[0,1]For all nodes l ∈ Nq\{O'qIs assigned to a position located at the length of the loop segment
Figure BDA0002314057890000031
Charging station with a distance less than the distance traveled
Figure BDA0002314057890000032
Is represented as:
Figure BDA0002314057890000033
wherein, R (omega) represents the driving distance of the electric automobile, wherein omega represents a given random condition; g represents a cumulative density function and defines
Figure BDA0002314057890000034
Further, the electric vehicle expected flow charging positioning model comprises:
obtaining an expected flowing charging positioning model of the electric automobile according to a formula FRLM for determinacy of a flowing charging position problem and the coverage probability of a stroke;
the expected flow charge localization model formula is as follows:
Max∑q∈Qfqzq(10)
Figure BDA0002314057890000035
Figure BDA0002314057890000036
Figure BDA0002314057890000037
further, the solving of the expected flowing charging positioning model of the electric vehicle comprises:
and solving the expected mobile charging positioning model of the electric automobile by adopting a tabu search algorithm.
Correspondingly, the invention also provides an electric automobile charging positioning system which is characterized by comprising a charging positioning model establishing module and a charging positioning model solving module;
the charging positioning model establishing module is used for establishing an expected mobile charging positioning model of the electric automobile by analyzing the problem of deterministic mobile charging positioning and considering the influence caused by the randomness of the driving mileage so as to ensure that the electric automobile realizes charging in all possible ranges;
and the charging positioning model solving module is used for solving the expected mobile charging positioning model of the electric automobile to obtain the charging position of the electric automobile.
Further, in the charging location model building module, the analyzing the deterministic flowing charging location problem includes:
the deterministic flow charging localization problem is described using the FRLM formula, which is expressed as follows:
Max∑q∈Qfqyq(1)
Figure BDA0002314057890000041
Figure BDA0002314057890000042
Figure BDA0002314057890000043
∑j∈N\N'xj＝p (5)
Figure BDA0002314057890000044
Figure BDA0002314057890000045
Figure BDA0002314057890000046
wherein Q is the journey, Q is the set of journeys of the electric automobile driver from the departure place to the destination in the road network, R is the journey of the electric automobile, fqRepresenting the flow of the electric vehicle in the journey q; y isqIndicates whether the trip q is covered; x is the number ofkIndicating whether a charging station is established at node k; x is the number ofjIndicating whether the charging station is opened;
Figure BDA0002314057890000047
represents from O'qTo D'qWhen passing after node k belongs to NqA set of nodes of (a); n is a set of road network nodes, N 'represents a set of repeating nodes, p is the number of charging stations to be built, O'qStarting point O for journey qqRepetition of, D'qFor journey q destination DqRepetition of (A), (B), (C) and (C), NqIs O'qAnd D'qThe set of nodes of the shortest path between them,
Figure BDA0002314057890000048
is from O'qTo D'qWhen passing before node l belongs to NqThe set of nodes of (a) is,
Figure BDA0002314057890000049
is a circulation section [ k, l]The length of the stroke q over which the stroke q runs,
Figure BDA0002314057890000051
are assigned variables.
Further, in the charging location model building module, the considering of the influence caused by the randomness of the traveled mileage includes:
defining a continuous variable z taking into account the coverage probability of a tripq∈[0,1]For all nodes l ∈ Nq\{O'qIs assigned to a position located at the length of the loop segment
Figure BDA0002314057890000052
Charging station with a distance less than the distance traveled
Figure BDA0002314057890000053
Is represented as:
Figure BDA0002314057890000054
wherein, R (omega) represents the driving distance of the electric automobile, wherein omega represents a given random condition; g represents a cumulative density function and defines
Figure BDA0002314057890000055
Further, in the charging location model building module, the electric vehicle expected mobile charging location model includes:
obtaining an expected flowing charging positioning model of the electric automobile according to a formula FRLM for determinacy of a flowing charging position problem and the coverage probability of a stroke;
the expected flow charge localization model formula is as follows:
Max∑q∈Qfqzq(10)
Figure BDA0002314057890000056
Figure BDA0002314057890000057
Figure BDA0002314057890000058
further, in the charging location model solving module, the solving of the expected flowing charging location model of the electric vehicle includes:
and solving the expected mobile charging positioning model of the electric automobile by adopting a tabu search algorithm.
Compared with the prior art, the invention has the following beneficial effects: the invention realizes the charging positioning of the electric automobile, fully exerts the development potential of the electric automobile, and solves the problems of difficult charging and random charging positions of the electric automobile, thereby ensuring that the electric automobile is not interrupted due to insufficient electric quantity in the normal driving process.
Drawings
Fig. 1 is a flow chart of a tabu search algorithm.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Aiming at the randomness of the vehicle running range and the problem of location of a charging station caused by different charging requirements, the invention establishes the expected mobile charging positioning model of the electric vehicle, and utilizes a tabu search algorithm to carry out heuristic solving analysis aiming at the random problems of the running state of the electric vehicle, the residual electric quantity of a battery and the like in the model, thereby shortening the time for obtaining optimal positioning.
The invention discloses a charging positioning method of an electric automobile, which comprises the following steps:
firstly, establishing an expected mobile charging positioning model of the electric automobile by analyzing the problem of deterministic mobile charging positioning and considering the influence caused by the randomness of the driving mileage, and ensuring that the electric automobile realizes charging in all possible ranges;
the method specifically comprises the following steps:
(1) put forward the formula of Flow Recharging Location Model (FRLM)
1) Description of the problem
Consider a road network G (N, a), where N represents a set of nodes and a represents a set of arcs linking the nodes. The demand is modeled as a set Q of predetermined trips to be taken by the driver of the electric vehicle, each trip Q of the set Q of trips corresponding to a given origin Oqe.N travels to a given destination DqE N and return, assuming all drivers performing the trip q follow the pass OqAnd DqThe shortest path therebetween, and there is no desire to deviate from this path for charging. Flow f of stroke qqCorresponding to the unit time of OqAnd DqThe number of vehicles traveling in between. Where the distance between each pair of adjacent stations on the path is defined to not exceed the R range, then the journey q is said to be covered. But as long as the distance between a pair of adjacent charging stations is greater than R, the journey q is considered uncovered. The optimization problem is therefore to determine the best positions to build a predetermined number p of charging stations in the road network in order to maximize the total flow of covered electric vehicles.
The concept of loop segments is introduced to facilitate modeling of the problem. Circulation section [ k, l ]]Defined as a sequence of consecutive arcs intersecting on the shortest path (outgoing and/or return direction) between node k and node l, it is noted that a loop segment [ O ]q,l]Two different ways can be understood: driver from OqGo to l from start or go to and fro l → Oq→ l for each trip q, node O is replicatedqAnd DqIs node O'qAnd D'qPrepared from (O'q，Oq) And (D)q，D′q) Added to A, circulating section [ O ]q,l](corresponding [ k, D)q]) Represents from OqTo l (from k to D)q) Goes outwards and circulates the section [ O'q,l](corresponding [ k, D'q]) Representing a round trip to the origin (destination). WhereinCirculation section [ k, l ]]Length over stroke q
Figure BDA0002314057890000071
The definition is as follows:
(1) for k ═ O'qAnd l ∈ Nq\{D'q}，
Figure BDA0002314057890000072
(2) For k ∈ Nq\{O'qD'q，
Figure BDA0002314057890000073
(3) For k ∈ Nq\{Oq,DqAre equal to l ∈ Nq\{Oq,Dq}，
Figure BDA0002314057890000074
(4) For k ═ O'qAnd l ═ D'q，
Figure BDA0002314057890000075
Formula (1) represents if node k is O'qAnd node l is O'qAnd D'qNode set of shortest path between or at D'qO 'is incorporated'qAnd D'qDefine an interpretation for a loop segment, then the loop segment [ k, l ]]Is equal to twice the sum of the arc lengths between k and l, wherein the arc length (m, n) belongs to a subset of the set of consecutive arcs on the shortest path between k and l; in the same way, the conclusion of the formula (2) is the same as that of the formula (1); compared with the formula (1) and the formula (2), the formula (3) has no reciprocating process in the circulating section, and only advances in one direction, so that the circulating section [ k, l ]]Is equal to the sum of the arc lengths between k and l; formula (4) represents, if k ═ O'qAnd l ═ D'qThen cycle section [ k, l]Which corresponds to always cycling between a start point and an end point, its length is shown as infinite.
The definitions of the symbols in the specific steps are shown in table 1.
TABLE 1 definition of symbols in the specific procedures
Figure BDA0002314057890000081
Figure BDA0002314057890000091
2) Put forward FRLM formula
In order to solve the problem of mobile charging location, the invention provides a formula FRLM, if a journey q is covered, after an open charging station in front of an electric vehicle is charged, each node l belongs to NqShould be reachable. Thus, in the case of coverage of the run q, it is necessary to cover for each node l ∈ NqAllocating a charging station within the range of the vehicle distance R
Figure BDA0002314057890000092
It is served so that the Electric Vehicle (EV) driver can charge the vehicle at node k and reach node l. If a charging station is established at node k, x k1, otherwise 0; if the run q is covered, then yqOtherwise, it is 0.
Introducing a set of binary variables into formula FRLM
Figure BDA0002314057890000093
It is defined as follows, for each trip q, each node l ∈ Nq\{O′qAnd each node
Figure BDA0002314057890000094
Comprises the following steps:
Figure BDA0002314057890000095
for k ≠ O'q: if the battery of the electric vehicle is charged at the charging station located at the node k, so that the driver can at least drive out to the node l again,
Figure BDA0002314057890000096
otherwise, the value is 0;
Figure BDA0002314057890000097
for k ═ O'q: if sub stroke l → O'qCharging of → l is ensured by the charging station of node l or a station thereafter, and the electric vehicle is in D'qTo O'qIs traveling the shortest path traversed by the return direction of (a),
Figure BDA0002314057890000098
the formula for FRLM is as follows:
Max∑q∈Qfqyq(1)
Figure BDA0002314057890000099
Figure BDA00023140578900000910
Figure BDA00023140578900000911
∑j∈N\N'xj＝p (5)
Figure BDA0002314057890000101
Figure BDA0002314057890000102
Figure BDA0002314057890000103
in the formula (f)qRepresenting the flow of the electric vehicle in the journey q; y isqRepresenting whether the journey q is covered, if yes, 1; x is the number ofkIndicating whether a charging station is built at the node k, and if yes, the node k is 1; x is the number ofjIndicating whether the charging station is opened or not, wherein the opening is 1;
Figure BDA0002314057890000104
represents from O'qTo D'qWhen passing after node k belongs to NqA set of nodes of (a);
Figure BDA0002314057890000105
represents from O'qTo D'qWhen, the one that passed before node l belongs to NqA set of nodes of (a); n' represents a set of repeating nodes.
The objective function (1) seeks to maximize the total coverage flux, calculated as a weighted sum of the coverage trips. Constraint equation (2) can be viewed as an assignment constraint, stating that if the trip q is covered, every node on the path, l ∈ Nq\{O′qAll should be assigned to a single node
Figure BDA0002314057890000106
Constraint (3) defines the relationship between variable w and variable x, if the charging station at node k is not open, then EV cannot be charged at node k, and all variables corresponding to node k
Figure BDA0002314057890000107
Should be set to 0; constraint (4) is a range constraint that ensures that each node l on the path is assigned to a station with a distance less than R if journey q is covered, and all nodes corresponding to journey q if journey q is uncovered
Figure BDA0002314057890000108
All variables are set to 0, and the constraint (4) is not used; the constraint (5) sets the number of charging stations that have to be opened to a predetermined number p; constraints (6) - (8) define the problem variables.
(2) Considering the influence caused by the randomness of the driving mileage and considering the coverage probability of the journey in problem modeling
In practice, the driving range R of an electric vehicle is influenced by a number of uncertainty factors, such as traffic conditions, weather orDriving habits and the like, and meanwhile, the randomness of the driving mileage needs to be considered to have important influence on problem modeling. In fact in the deterministic problem, whether a journey is covered depends on the position of the charging station, in contrast to when the driving range is random, the coverage becomes an accidental event. In this case, even if many charging stations are opened on the corresponding path, it may no longer be possible to ensure that charging is achieved for all possible ranges. Therefore, the coverage probability of the trip, i.e., the probability that the EV driver can make a round trip from the departure point to the destination without depleting the electric power, needs to be considered in the problem modeling. Defining a continuous variable zq∈[0,1]Is the coverage probability of the run q. Here, z isqDefined as all nodes l ∈ Nq\{O'qIs assigned to a position located at the length of the loop segment
Figure BDA0002314057890000111
Charging station with a distance less than the distance traveled
Figure BDA0002314057890000112
The joint probability of (a) can be obtained:
Figure BDA0002314057890000113
where R (ω) represents a travel distance of the electric vehicle, where ω represents a given random condition for which the value of the EV travel distance R (ω) is assumed to be the same in all road networks; g represents a cumulative density function. The second step of the equation calculates z using the assumption that the distance of travel R (ω) is the same in all road networksqIs the probability that the distance traveled is greater than the longest cycle length traveled on trip q. In the third step of the equation, the probability is expressed using the cumulative density function G, where the fourth step of the equation holds because G is a non-decreasing function. Due to the fact that
Figure BDA0002314057890000114
Is a binary variable and G (0) ═ 0, can be obtained
Figure BDA0002314057890000115
Thus, the fifth step of the equation is derived. Finally, by definition
Figure BDA0002314057890000116
The final result of the equation is obtained.
(3) Expected flow charging positioning model establishment
The expected flow charging location model (EFRLM) formula is as follows:
Max∑q∈Qfqzq(10)
Figure BDA0002314057890000117
Figure BDA0002314057890000118
Figure BDA0002314057890000121
the constraint (11) states that if the run q has a definite positive probability of coverage (z)q≧ 0), then for each node l ∈ Nq\{O'qDuring the journey q there must be a node k before node l is passed, so that the vehicle can be charged to reach node l. The constraint (12) will cover the probability variable zqAnd binary variables
Figure BDA0002314057890000122
Are linked together. The formula indicates the variable zqAll zones [ k, l ] calculated as node 1 supplemented by charging stations at node k]Minimum coverage probability of. The constraints (3), (5), (6) and (8) are guaranteed to be satisfied by deterministic equations.
And secondly, for the defect of long calculation time required for solving a large expected flowing charging positioning model, a taboo search heuristic algorithm is provided for solving and analyzing the expected flowing charging positioning model so as to obtain a high-quality solution in shorter calculation time:
for small and medium scale examples, a commercial Mixed Integer Linear Programming (MILP) solver may be used to solve for the stochastic flow charging localization model. However, when the size of the road network scale increases, the calculation time required to obtain an optimal solution may become very long. Tabu search can provide a high quality solution to general device location problems and is easy to develop and implement. The procedure for tabu search to solve the desired streaming charge location problem is as follows:
(1) constructing an initial solution
The process of constructing the initial solution aims at obtaining a set of charging station numbers p that can provide good network coverage, for which the journey is based on its flow value fqWhen the station to be opened is selected, the travel position where the traffic flow is maximum is considered first. When the number p of charging stations that need to be opened is reached, the process ends. However, the coverage probability is not minimized in the desired flow charging location model because its goal is to maximize the expected flow. Thus, when establishing an initial solution, this minimum probability is set as an input parameter and a set of initial number p of sites is established. At the end of the process, the objective function of the resulting solution is evaluated by examining the coverage (or coverage probability) of all trips.
(2) Evaluating an objective function for a given solution
To evaluate the objective function for a given solution, iterations of the runs are required to check which runs are covered and calculate the total (or expected) flow covered. In order to know whether the journey q is covered, the following steps are required: for each cycle segment of q in EFRLM, the probability z of coverage of the run qqCalculated as the minimum of its all-cycle segment coverage probabilities, and then by adding fqzqThe target value is updated.
The tabu search algorithm is shown in fig. 1, and an initial solution is first established, and then the optimal feasible solution is SolbestSet as initial solution SolinitThe optimum target value ObjbestSet to an initial target value ObjinitThe number of iterations nblterwithroutimpr of the tabu search is initialized to 0. The process is carried outOver exploration SolinitNeighborhood to iteratively improve SolbestTwo steps are performed when nblterWithoutImpr < maxWithoutImpr.
Step 1 comprises selecting a site s to open among | N | -p closed sites1，
Figure BDA0002314057890000131
And should achieve the highest expected coverage among all possible open sites. This step may improve the expected coverage, but the solution obtained is not feasible since p +1 charging stations are involved. Therefore, at the end of this step, the tabu list addition s needs to be updated1Go to tabuoverest without updating the optimal solution Solbest。
Step 2 comprises selecting a site s to close among the p +1 open sites2，
Figure BDA0002314057890000132
And should achieve the highest expected coverage among all possible shut down sites. A scofflaw criterion is considered here that accepts the closing of a contraindicated site, but improves the optimal target value.
At the end of step 2, the tabu list needs to be updated, if it is improved after the last shutdown, the optimal solution needs to be updated, otherwise the number of iterations is increased without improvement. When there is no improved ObjbestWhen the number of iterations reaches the maximum limit, the tabu search process stops. It is noted that in this process, two tabu lists are required, one for the most recently opened site and the other for the most recently closed site. Wherein the symbol definitions are shown in table 2.
TABLE 2 symbol definitions in tabu search Algorithm
Figure BDA0002314057890000133
Figure BDA0002314057890000141
And thirdly, verifying the feasibility of the method by calculation.
The performance of the proposed accurate and heuristic solving method is evaluated through the results of experimental data. All solution algorithms are written in the C + + language. The millp model was implemented using the Concert technique and solved using ILOG CPLEX12.6.2 version. All tests were performed on a personal computer running under the Windows 10 operating system, equipped with Intel i5-3210M core 2Duo (2.50GHz) and 8GB memory.
In numerical experiments, the road network used in the present invention was randomly generated. First, the | N | nodes are generated, and the coordinates of the nodes are uniformly distributed in [1,1000 ]]2And (4) internal random selection. The moving distance between each pair of nodes is calculated as the euclidean distance. The Kruskal algorithm is then applied to determine the minimum spanning tree of | N | -1 nodes, and experiments have also selected | N | additional arcs to be added to a, these arcs being the shortest potential arcs that have not been added to a, so that the order of each node remains below 4. Then M starting point to end point nodes are randomly selected from the nodes belonging to N. The deterministic range R is set to 250 km. Assuming that the random range R (ω) follows a Gamma distribution, the shape parameter k is 50 and the scale parameter θ is 5.
The present invention considers the problem from two dimensions: 100, M50 and N200, M100, randomly generating 5 network instances and a run set to be covered, for each of these 10 network instances, taking into account 9 possible station number p values: p ∈ {1,2,3,4,5,10,15,20,25}, resulting in a set of 90 instances in total, where in the representation of each test instance, first | N |, then M, and finally p are represented. For example, the example N100M50p10 represents testing a road network including 100 nodes, 50 start/end nodes and 10 charging stations open.
(1) Results of deterministic problems
This section analyzes the numerical performance of the formula FRLM for the deterministic flow charging localization problem. Table 3 shows the results obtained with CPLEX on a random generation network. The experimental results show the calculated integrity gap, i.e. the relative difference between the optimal integer feasible value and the linear relaxation value, the CPU time in seconds (setting the calculation time limit to 10 hours), the number of Branch & Bound nodes the algorithm gets within the time limit, while the remaining gap is defined as the relative gap between the optimal integer feasible solution and the optimal upper limit found within the time limit.
Table 3 numerical performance obtained using formula FRLM in random example (average 5 replicates)
Figure BDA0002314057890000151
Figure BDA0002314057890000161
First, from experimental results it can be observed that the average CPU time increases with the road network scale, because: as | N | and M increase, the formula calculation increases. It was also observed through experimental results that the average CPU time trend increased as the value of p increased, because: as long as p ≦ N/2, the number of possible combinations of site deployments may increase. It should be noted that there are exceptions to the small p-value using the formula FRLM. The calculations show that the average calculation time for the random instances is 422 seconds using the FRLM formula. In the random case, the average integrity gap for FRLM is 20.9%. Before finding the optimal solution, the average number of Branch & Bound nodes obtained by the algorithm is 10. Thus, although the order of magnitude of the constraint of the formula FRLM is large, its better compactness enables the mathematical solver CPLEX to solve deterministic variables of the problem more efficiently.
(2) Results of randomness problems
This section analyzes the formula EFRLM that solves the random flow charging location problem. The results in table 4 show that, when considering the randomness of the vehicle driving range and the problem of location of the charging station caused by different charging demands, the average calculation time of the random example is larger and larger as the road network is increased (5753 seconds is the maximum road network).
Table 4 numerical performance obtained using the formula EFRLM in the random example (average 5 replicates)
Figure BDA0002314057890000171
(3) Performance using tabu search algorithms
As shown in the calculation results of table 4, for large examples, the CPU time required to solve the optimal solution of the MILP formula corresponding to the stochastic problem is still long. However, the heuristic solving method can obtain a high-quality solution within a short running time, so that the tabu search algorithm is used for solving the random problem in the part, and the setting is as follows: the tabu list size Ntabu is 5, the maximum number of iterations maxWithoutImpr allowed without improving the target value is 10, and the minimum coverage probability is set to 60% when establishing an initial solution to the desired streaming charge location problem. Table 5 summarizes the results of the heuristic on the random instance of the random problem, where the average tabu gap is obtained, which is defined as the relative gap between the optimal solution found by the heuristic and the optimal solution, and the tabu search heuristic CPU time.
TABLE 5 tabu heuristic Performance of stochastic problem on random instances (5 replicates on average)
Figure BDA0002314057890000181
Figure BDA0002314057890000191
The results of table 5 show that the tabu heuristic provides a near-optimal solution in a short computation time. I.e., the tabu heuristic of EFRLP had an average gap of 0.9% over random instances with an average computation time of 35 seconds. Experimental results also show that for EFRLP, the tabu search heuristic can yield a near-optimal solution with significantly reduced computation time compared to the mathematical solver, i.e., the average computation time of the heuristic is reduced from 4792 to 35 seconds of the solver in all instances. For larger instances (random set of instances N200M100p15, N200M100p20, and N200M100p25), this reduction is even more pronounced, with the average computation time reduced from the solver 6h to 2min using the heuristic.
Correspondingly, the invention also provides an electric automobile charging positioning system which is characterized by comprising a charging positioning model establishing module and a charging positioning model solving module;
the charging positioning model establishing module is used for establishing an expected mobile charging positioning model of the electric automobile by analyzing the problem of deterministic mobile charging positioning and considering the influence caused by the randomness of the driving mileage so as to ensure that the electric automobile realizes charging in all possible ranges;
and the charging positioning model solving module is used for solving the expected mobile charging positioning model of the electric automobile to obtain the charging position of the electric automobile.
Further, in the charging location model building module, the analyzing the deterministic flowing charging location problem includes:
the deterministic flow charging localization problem is described using the FRLM formula, which is expressed as follows:
Max∑q∈Qfqyq(1)
Figure BDA0002314057890000201
Figure BDA0002314057890000202
Figure BDA0002314057890000203
∑j∈N\N'xj＝p (5)
Figure BDA0002314057890000204
Figure BDA0002314057890000205
Figure BDA0002314057890000206
wherein Q is the journey, Q is the set of journeys of the electric automobile driver from the departure place to the destination in the road network, R is the journey of the electric automobile, fqRepresenting the flow of the electric vehicle in the journey q; y isqIndicates whether the trip q is covered; x is the number ofkIndicating whether a charging station is established at node k; x is the number ofjIndicating whether the charging station is opened;
Figure BDA0002314057890000207
represents from O'qTo D'qWhen passing after node k belongs to NqA set of nodes of (a); n is a set of road network nodes, N 'represents a set of repeating nodes, p is the number of charging stations to be built, O'qStarting point O for journey qqRepetition of, D'qFor journey q destination DqRepetition of (A), (B), (C) and (C), NqIs O'qAnd D'qThe set of nodes of the shortest path between them,
Figure BDA0002314057890000208
is from O'qTo D'qWhen passing before node l belongs to NqThe set of nodes of (a) is,
Figure BDA0002314057890000209
is a circulation section [ k, l]The length of the stroke q over which the stroke q runs,
Figure BDA00023140578900002010
are assigned variables.
Further, in the charging location model building module, the considering of the influence caused by the randomness of the traveled mileage includes:
defining a continuous variable z taking into account the coverage probability of a tripq∈[0,1]For all nodes l ∈ Nq\{O'qIs assigned to a position located at the length of the loop segment
Figure BDA00023140578900002011
Charging station with a distance less than the distance traveled
Figure BDA00023140578900002012
Is represented as:
Figure BDA0002314057890000211
wherein, R (omega) represents the driving distance of the electric automobile, wherein omega represents a given random condition; g represents a cumulative density function and defines
Figure BDA0002314057890000212
Further, in the charging location model building module, the electric vehicle expected mobile charging location model includes:
obtaining an expected flowing charging positioning model of the electric automobile according to a formula FRLM for determinacy of a flowing charging position problem and the coverage probability of a stroke;
the expected flow charge localization model formula is as follows:
Max∑q∈Qfqzq(10)
Figure BDA0002314057890000213
Figure BDA0002314057890000214
Figure BDA0002314057890000215
further, in the charging location model solving module, the solving of the expected flowing charging location model of the electric vehicle includes:
and solving the expected mobile charging positioning model of the electric automobile by adopting a tabu search algorithm.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A charging positioning method for an electric automobile is characterized by comprising the following steps:
by analyzing the problem of deterministic mobile charging positioning and considering the influence caused by the randomness of the driving mileage, an expected mobile charging positioning model of the electric automobile is established to ensure that the electric automobile realizes charging in all possible ranges;
and solving the expected mobile charging positioning model of the electric automobile to obtain the charging position of the electric automobile.
2. The method of claim 1, wherein analyzing the deterministic streaming charging location problem comprises:
the deterministic flow charging localization problem is described using the FRLM formula, which is expressed as follows:
Max∑q∈Qfqyq(1)
Figure FDA0002314057880000011
Figure FDA0002314057880000012
Figure FDA0002314057880000013
∑j∈N\N'xj＝p (5)
Figure FDA0002314057880000014
Figure FDA0002314057880000015
Figure FDA0002314057880000016
wherein Q is the journey, Q is the set of journeys of the electric automobile driver from the departure place to the destination in the road network, R is the journey of the electric automobile, fqRepresenting the flow of the electric vehicle in the journey q; y isqIndicates whether the trip q is covered; x is the number ofkIndicating whether a charging station is established at node k; x is the number ofjIndicating whether the charging station is opened;
Figure FDA0002314057880000017
represents from O'qTo D'qWhen passing after node k belongs to NqA set of nodes of (a); n is a set of road network nodes, N 'represents a set of repeating nodes, p is the number of charging stations to be built, O'qStarting point O for journey qqRepetition of, D'qFor journey q destination DqRepetition of (A), (B), (C) and (C), NqIs O'qAnd D'qThe set of nodes of the shortest path between them,
Figure FDA0002314057880000018
is from O'qTo D'qWhen passing before node l belongs to NqThe set of nodes of (a) is,
Figure FDA0002314057880000021
is a circulation section [ k, l]The length of the stroke q over which the stroke q runs,
Figure FDA0002314057880000022
are assigned variables.
3. The method as claimed in claim 2, wherein the step of considering the influence of the randomness of the driving mileage comprises:
defining a continuous variable z taking into account the coverage probability of a tripq∈[0,1]For all nodes l ∈ Nq\{O'qIs assigned to a position located at the length of the loop segment
Figure FDA0002314057880000023
Charging station with a distance less than the distance traveled
Figure FDA0002314057880000024
Is represented as:
Figure FDA0002314057880000025
wherein, R (omega) represents the driving distance of the electric automobile, wherein omega represents a given random condition; g represents a cumulative density function and defines
Figure FDA0002314057880000026
4. The electric vehicle charging positioning method as claimed in claim 3, wherein the electric vehicle expected flow charging positioning model comprises:
obtaining an expected flowing charging positioning model of the electric automobile according to a formula FRLM for determinacy of a flowing charging position problem and the coverage probability of a stroke;
the expected flow charge localization model formula is as follows:
Max∑q∈Qfqzq(10)
Figure FDA0002314057880000027
Figure FDA0002314057880000028
Figure FDA0002314057880000029
5. the method as claimed in claim 1, wherein the step of solving the expected mobile charging location model of the electric vehicle comprises:
and solving the expected mobile charging positioning model of the electric automobile by adopting a tabu search algorithm.
6. The electric automobile charging positioning system is characterized by comprising a charging positioning model establishing module and a charging positioning model solving module;
the charging positioning model establishing module is used for establishing an expected mobile charging positioning model of the electric automobile by analyzing the problem of deterministic mobile charging positioning and considering the influence caused by the randomness of the driving mileage so as to ensure that the electric automobile realizes charging in all possible ranges;
and the charging positioning model solving module is used for solving the expected mobile charging positioning model of the electric automobile to obtain the charging position of the electric automobile.
7. The system of claim 6, wherein the module for establishing the charging location model is configured to analyze the deterministic fluid charging location problem, and the system comprises:
the deterministic flow charging localization problem is described using the FRLM formula, which is expressed as follows:
Max∑q∈Qfqyq(1)
Figure FDA0002314057880000031
Figure FDA0002314057880000032
Figure FDA0002314057880000033
∑j∈N\N'xj＝p (5)
Figure FDA0002314057880000034
Figure FDA0002314057880000035
Figure FDA0002314057880000036
wherein Q is the journey, Q is the set of journeys of the electric automobile driver from the departure place to the destination in the road network, R is the journey of the electric automobile, fqRepresenting the flow of the electric vehicle in the journey q; y isqIndicates whether the trip q is covered; x is the number ofkIndicating whether a charging station is established at node k; x is the number ofjIndicating whether the charging station is opened;
Figure FDA0002314057880000041
represents from O'qTo D'qWhen passing after node k belongs to NqA set of nodes of (a); n is a set of road network nodes, N 'represents a set of repeating nodes, p is the number of charging stations to be built, O'qStarting point O for journey qqRepetition of, D'qFor journey q destination DqRepetition of (A), (B), (C) and (C), NqIs O'qAnd D'qThe set of nodes of the shortest path between them,
Figure FDA0002314057880000042
is from O'qTo D'qWhen passing before node l belongs to NqThe set of nodes of (a) is,
Figure FDA0002314057880000043
is a circulation section [ k, l]The length of the stroke q over which the stroke q runs,
Figure FDA0002314057880000044
are assigned variables.
8. The system according to claim 7, wherein in the charging location model building module, the influence of the randomness of the driving range is considered to include:
defining a continuous variable z taking into account the coverage probability of a tripq∈[0,1]For all nodes l ∈ Nq\{O'qIs assigned to a position located at the length of the loop segment
Figure FDA0002314057880000045
Charging station with a distance less than the distance traveled
Figure FDA0002314057880000046
Is represented as:
Figure FDA0002314057880000047
wherein, R (omega) represents the driving distance of the electric automobile, wherein omega represents a given random condition; g represents a cumulative density function and defines
Figure FDA0002314057880000048
9. The system of claim 8, wherein in the charging location model building module, the expected-to-flow charging location model of the electric vehicle comprises:
obtaining an expected flowing charging positioning model of the electric automobile according to a formula FRLM for determinacy of a flowing charging position problem and the coverage probability of a stroke;
the expected flow charge localization model formula is as follows:
Max∑q∈Qfqzq(10)
Figure FDA0002314057880000051
Figure FDA0002314057880000052
Figure FDA0002314057880000053
10. the system according to claim 6, wherein in the charging location model solving module, the solving of the expected flowing charging location model of the electric vehicle includes:
and solving the expected mobile charging positioning model of the electric automobile by adopting a tabu search algorithm.
CN201911270634.8A 2019-12-12 2019-12-12 Electric vehicle charging positioning method and system Active CN110949149B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911270634.8A CN110949149B (en) 2019-12-12 2019-12-12 Electric vehicle charging positioning method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911270634.8A CN110949149B (en) 2019-12-12 2019-12-12 Electric vehicle charging positioning method and system

Publications (2)

Publication Number Publication Date
CN110949149A true CN110949149A (en) 2020-04-03
CN110949149B CN110949149B (en) 2022-12-06

Family

ID=69981150

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911270634.8A Active CN110949149B (en) 2019-12-12 2019-12-12 Electric vehicle charging positioning method and system

Country Status (1)

Country Link
CN (1) CN110949149B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113343437A (en) * 2021-05-20 2021-09-03 国网上海市电力公司 Electric vehicle rapid charging guiding method, system, terminal and medium

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105160428A (en) * 2015-08-19 2015-12-16 天津大学 Planning method of electric vehicle fast-charging station on expressway
CN108182508A (en) * 2017-12-07 2018-06-19 北京国网普瑞特高压输电技术有限公司 A kind of method and system of electric automobile charging station planning
CN207866927U (en) * 2017-12-31 2018-09-14 陕西中兴祥林电子科技有限公司 A kind of urban public transport vehicle charging system
CN109193727A (en) * 2018-10-30 2019-01-11 国网江苏省电力有限公司南通供电分公司 A kind of intelligent distribution network probability coordinated planning method containing electric car
CN109460853A (en) * 2018-09-29 2019-03-12 中国电力科学研究院有限公司 A kind of electric car charging workload demand determines method and system
CN109849718A (en) * 2018-12-06 2019-06-07 海南电网有限责任公司 A kind of method of electric car electrically-charging equipment and the active collaborative planning of power distribution network
CN109910781A (en) * 2017-12-07 2019-06-21 福特全球技术公司 System and method for monitoring vehicle electrical system
CN110210683A (en) * 2019-06-12 2019-09-06 海南电网有限责任公司 A kind of electric car electrically-charging equipment site selecting method
EP3556601A1 (en) * 2018-04-16 2019-10-23 Morgan Brown Consultancy Ltd. Vehicle routing

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105160428A (en) * 2015-08-19 2015-12-16 天津大学 Planning method of electric vehicle fast-charging station on expressway
CN108182508A (en) * 2017-12-07 2018-06-19 北京国网普瑞特高压输电技术有限公司 A kind of method and system of electric automobile charging station planning
CN109910781A (en) * 2017-12-07 2019-06-21 福特全球技术公司 System and method for monitoring vehicle electrical system
CN207866927U (en) * 2017-12-31 2018-09-14 陕西中兴祥林电子科技有限公司 A kind of urban public transport vehicle charging system
EP3556601A1 (en) * 2018-04-16 2019-10-23 Morgan Brown Consultancy Ltd. Vehicle routing
CN109460853A (en) * 2018-09-29 2019-03-12 中国电力科学研究院有限公司 A kind of electric car charging workload demand determines method and system
CN109193727A (en) * 2018-10-30 2019-01-11 国网江苏省电力有限公司南通供电分公司 A kind of intelligent distribution network probability coordinated planning method containing electric car
CN109849718A (en) * 2018-12-06 2019-06-07 海南电网有限责任公司 A kind of method of electric car electrically-charging equipment and the active collaborative planning of power distribution network
CN110210683A (en) * 2019-06-12 2019-09-06 海南电网有限责任公司 A kind of electric car electrically-charging equipment site selecting method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113343437A (en) * 2021-05-20 2021-09-03 国网上海市电力公司 Electric vehicle rapid charging guiding method, system, terminal and medium
CN113343437B (en) * 2021-05-20 2023-11-24 国网上海市电力公司 Electric automobile quick charge guiding method, system, terminal and medium

Also Published As

Publication number Publication date
CN110949149B (en) 2022-12-06

Similar Documents

Publication Publication Date Title
Perumal et al. Solution approaches for integrated vehicle and crew scheduling with electric buses
Küçükoğlu et al. Hybrid simulated annealing and tabu search method for the electric travelling salesman problem with time windows and mixed charging rates
Montoya et al. The electric vehicle routing problem with nonlinear charging function
Duman et al. Branch-and-price-and-cut methods for the electric vehicle routing problem with time windows
Qi et al. Development and evaluation of an evolutionary algorithm-based online energy management system for plug-in hybrid electric vehicles
James Two-stage request scheduling for autonomous vehicle logistic system
Sheppard et al. Cost-effective siting of electric vehicle charging infrastructure with agent-based modeling
de Vries et al. Incorporating driving range variability in network design for refueling facilities
Boujelben et al. Efficient solution approaches for locating electric vehicle fast charging stations under driving range uncertainty
Xiong et al. Mixed optimization on vehicle scheduling and recharge scheduling of plug-in electric buses with consideration of partial recharge
CN110949149B (en) Electric vehicle charging positioning method and system
Sivagnanam et al. Minimizing energy use of mixed-fleet public transit for fixed-route service
Quintana et al. Robust ebuses charging location problem
Avishan et al. Electric bus fleet scheduling under travel time and energy consumption uncertainty
NaitMalek et al. On the use of machine learning for state-of-charge forecasting in electric vehicles
Zhou et al. Electric bus charging scheduling on a bus network
Rajan et al. Robustness generalizations of the shortest feasible path problem for electric vehicles
Gambuti et al. Electric vehicle trip planning integrating range constraints and charging facilities
Erdoğan et al. Finding an energy efficient path for plug-in electric vehicles with speed optimization and travel time restrictions
Huber et al. Long-trip optimization of charging strategies for battery electric vehicles
Karimpour et al. Estimating energy consumption and charging duration of electric vehicle in multigraph
Luo et al. A Two-Stage Approach for Electric Vehicle Routing Problem with Time Windows and Heterogeneous Recharging Stations
Šaltenis et al. Charge-arrival-time profiles for long ev routes
Izdebski et al. An Efficient Hybrid Algorithm for Energy Expenditure Estimation for Electric Vehicles in Urban Service Enterprises. Energies 2021, 14, 2004
Kouider et al. Large Neighborhood Search for Periodic Electric Vehicle Routing Problem.

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant