CN102709984A - Electromobile charging path planning method based on intelligent transportation system - Google Patents

Electromobile charging path planning method based on intelligent transportation system Download PDF

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CN102709984A
CN102709984A CN2012101948731A CN201210194873A CN102709984A CN 102709984 A CN102709984 A CN 102709984A CN 2012101948731 A CN2012101948731 A CN 2012101948731A CN 201210194873 A CN201210194873 A CN 201210194873A CN 102709984 A CN102709984 A CN 102709984A
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charging
charging station
electric automobile
control center
traffic control
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CN102709984B (en
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郭庆来
孙宏斌
张伯明
吴文传
王尧
李正烁
辛蜀骏
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Tsinghua University
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Abstract

The invention relates to an electromobile charging path planning method based on an intelligent transportation system, belonging to the technical field of operation and control of electric systems. The electromobile charging path planning method comprises the following steps of: firstly, a traffic control center judges whether an electromobile needs to be charged according to information of the electromobile; if necessary, an owner is prompted to charge the electromobile; the traffic control center searches all charging stations in the maximum range of the electromobile as candidate charging stations and calculates the charging probability of the electromobile in each charging station; a charging load is predicated according to the charging probability and is transmitted to an electric system scheduling center, and the electric system scheduling center calculates the allowable maximum charging power of each charging station and transmits the allowable maximum charging power to the traffic control center; and the traffic control center transmits traveling and charging total time sequence obtained according to the maximum charging power to the owner. The electromobile charging path planning method provides an optimal charging path for the owner of the electromobile and improves the traveling efficiency of the owner. In addition, the operation requirements of the electric system are sufficiently considered in the selection of the charging stations, and therefore the safe operation of the electric system is guaranteed.

Description

A kind of charging electric vehicle paths planning method based on intelligent transportation system
Technical field
The present invention relates to a kind of charging electric vehicle paths planning method, belong to power system operation and control technology field based on intelligent transportation system.
Background technology
International SAE J1772-2010 standard code three kinds of charging modes: " exchange grade 1 " that is used for charging at a slow speed and " the exchanging grade 2 " and " DC charging " that are used for quick charge.Charging is mainly carried out at charging pile at a slow speed, and the charging duration is 6～8 hours, is suitable for the long-time electric automobile that stops.Quick charge is mainly carried out at charging station; The charging duration is 15 minutes～2 hours; The electric automobile that is used in the way of going carries out emergent charging, and the car owner hopes that the charge power that charging station provides is the receptible maximum power of electric automobile, so that accomplish charging as early as possible.Yet charging electric vehicle possibly cause adverse effect to electric power system aspect a lot: like traffic overload, voltage levvl is defective with the electric energy loss increase etc.After electric automobile was introduced market on a large scale, the space randomness of electric vehicle rapid charging possibly cause between different charging stations Load distribution extremely inhomogeneous, brought difficulty for the safety and economic operation of electrical network.The present invention is intended to utilize spatial information to address this problem.
The spatial information relevant with the charging station electricity needs has two types: the one, and the position of charging station in geographical winding diagram; The 2nd, the position of electric automobile and speed.The former can obtain through GIS-Geographic Information System (hereinafter to be referred as GIS), and the latter can gather through global satellite status system (hereinafter to be referred as GPS) in real time.GIS can form accurate electronic chart.Through the cooperation of GIS and database, the driver can obtain the demonstration directly perceived of street and peripheral facility thereof; GPS be based on 24 satellites be used to locate and regularly navigation system, the vehicle GPS receiver also can be measured the speed of a motor vehicle.GIS and GPS are the organic components of intelligent transportation system (hereinafter to be referred as ITS).
Summary of the invention
The objective of the invention is to propose a kind of charging electric vehicle paths planning method based on intelligent transportation system; Adopt intelligent transport technology; To reduce quick charge to the power system operation adverse effect, help practicing thrift car owner's time, help to promote electric network security again.
The charging electric vehicle paths planning method based on intelligent transportation system that the present invention proposes may further comprise the steps:
(1) electric automobile is sent to traffic control center with electric automobile information, and electric automobile information comprises: starting point A, destination B, initial power state E Soc0, battery capacity E B, departure time t 0, electric automobile during traveling max mileage d RanAnd the per unit electric energy kilometer KPGe that goes; Traffic control center is according to the electric automobile information that receives, the shortest path between 2 of the selected A, B, and the distance of shortest path is designated as d ABmin, selected from the destination the nearest charging station T of B, the distance of the shortest path between destination B, the charging station T is designated as d BTminTraffic control center is judged the energy state of electric automobile: if d Ran>d ABmin+ d BTmin, judge that then electric automobile need not charging, if d Ran≤d ABmin+ d BTmin, then point out the driver to charging electric vehicle, then carry out step (2);
(2) traffic control center is according to max mileage d Ran, search electric automobile all charging stations in max mileage, as candidate's charging station of electric automobile, the set of note candidate charging station is C; Traffic control center searches for total time of B to the destination again by A point each charging station j in the candidate's charging station set respectively, remembers t j, wherein: j ∈ C;
(3) establish electric automobile at t 0Constantly set out, obtain the total time t that electric automobile arrives all candidate's charging stations according to step (2) j, obtain the Probability p of electric automobile in any candidate's charging station j charging j, J ∈ C, wherein, s is the charging station sum;
(4) according to above-mentioned charging Probability p j, traffic control center calculates electric automobile and at the prediction charge power of charging station j charging is:
P j=p j·P EV·
Wherein: P EVMaximum charge power for electric automobile car owner expectation;
(5) traffic control center calculates electric automobile and arrives candidate's charging station j required time
Figure BDA00001760886000022
The required charging interval
Figure BDA00001760886000023
And electric automobile is at the load prediction L of candidate's charging station j charging j(t);
t j arr = t 0 + d j min v j ;
t j dur = E B - ( E SOC 0 - d j min / KPEe ) P EV .
Figure BDA00001760886000026
V wherein jThe wagon flow speed of the electric automobile starting point of measuring for traffic control center to candidate's charging station path,
Figure BDA00001760886000027
Arrive the distance of the shortest path of charging station j for electric automobile;
(6) repeating step (1)-(5); Traffic control center in the load prediction of candidate's charging station j stack, obtains total charging load prediction
Figure BDA00001760886000028
of charging station j and the load prediction of should always charging is sent to the power system dispatching center with all electric automobiles;
(7) power system dispatch center load forecasting based on the received charging and power system control center of the power system database charging station j belongs grid load point load forecasting
Figure BDA000017608860000210
j is calculated for each charging station maximum charging power
Figure BDA000017608860000211
P j max ( t ) = max { P EV , L j max ( t ) - L j O ( t ) - L j T ( t ) }
Wherein
Figure BDA00001760886000032
is the permission maximum charge power of the network load point under the charging station j that from the electric power system data storehouse, reads;
(8) repeating step (1)-(7), power system dispatching center are calculated the permission maximum charge power
Figure BDA00001760886000033
of each charging station of all electric automobiles in max mileage respectively and maximum charge power are sent to traffic control center;
(9) traffic control center is revised the charging interval of each electric automobile respectively according to maximum charge power
Figure BDA00001760886000035
, obtains the revised charging interval
Figure BDA00001760886000036
Figure BDA00001760886000037
Traffic control center calculated for each electric car from starting point to destination time required and the time required to charge the total time and the total time
Figure BDA00001760886000039
Send to power system control center:
Figure BDA000017608860000310
(10) the power system dispatching center according to each electric automobile that calculates respectively total time
Figure BDA000017608860000311
that receives to the probability that j charging station charges is:
Figure BDA000017608860000312
And and then calculate the charge power P of each electric automobile respectively in charging station j charging j' and load prediction L j' (t) be:
P j ′ = p j ′ · P j max ( t 0 ) , j ∈ C
Figure BDA000017608860000314
(11) the power system dispatching center is with the load prediction stack of all electric automobiles at candidate's charging station j; Obtain total charging load prediction
Figure BDA000017608860000315
of charging station j and will be somebody's turn to do total charging load prediction being added in the load prediction
Figure BDA000017608860000316
of electric power system, be used for the load prediction
Figure BDA000017608860000317
of the affiliated network load point of calculation procedure (7) charging station j
L j O ( t ) = L j O , old ( t ) + L j T ′ ( t )
Wherein
Figure BDA000017608860000319
Be departure time t 0The load prediction of the network load point under the charging station j constantly in the electric power system data storehouse;
What (12) traffic control center obtained above-mentioned steps (9) sorts total time
Figure BDA00001760886000041
from small to large, and ranking results is showed the car owner of electric automobile through graphical interfaces.
The charging electric vehicle paths planning method that the present invention proposes based on intelligent transportation system, its characteristics and advantage are:
After electric automobile became the main vehicles, car owner and electric power system faced new problem.On the one hand, the car owner need consider how to select an optimal charge station and charge path, makes the total time cost of trip and charging the shortest; On the other hand, electric power system need be avoided electric automobile to concentrate on the charging of a certain seat charging station in a large number causing its overload and voltage levvl low excessively.The inventive method can address the above problem, and for the electric automobile car owner provides an optimal charge path, the time that helps practicing thrift the car owner, improves car owner's the line efficiency that goes out.And charging station choose the service requirement that has taken into full account electric power system, avoid the congested phenomenon of electric power, ensured the safe operation of electric power system.
Description of drawings
Fig. 1 is to use the system block diagram of the inventive method.
Fig. 2 is the orientation sketch map of starting point, destination, charging station, shortest path of electric automobile in the inventive method etc.
Embodiment
The charging electric vehicle paths planning method that the present invention proposes based on intelligent transportation system, its system block diagram is as shown in Figure 1, and its method may further comprise the steps:
(1) electric automobile is sent to traffic control center with electric automobile information, and electric automobile information comprises: starting point A, destination B, initial power state E Soc0, battery capacity E B, departure time t 0, electric automobile during traveling max mileage d RanAnd the per unit electric energy kilometer KPGe that goes; Traffic control center is according to the electric automobile information that receives, the shortest path between 2 of the selected A, B, and the distance of shortest path is designated as d ABmin, selected from the destination the nearest charging station T of B, the distance of the shortest path between destination B, the charging station T is designated as d BTminTraffic control center is judged the energy state of electric automobile: if d Ran>d ABmin+ d BTmin, judge that then electric automobile need not charging, if d Ran≤d ABmin+ d BTmin, then point out the driver to charging electric vehicle, then carry out step (2).The orientation sketch map of the starting point of above-mentioned electric automobile, destination, charging station, shortest path etc. is as shown in Figure 2;
(2) traffic control center is according to max mileage d Ran, search electric automobile all charging stations in max mileage, as candidate's charging station of electric automobile, the set of note candidate charging station is C; Traffic control center searches for total time of B to the destination again by A point each charging station j in the candidate's charging station set respectively, remembers t j, wherein: j ∈ C;
(3) establish electric automobile at t 0Constantly set out, obtain the total time t that electric automobile arrives all candidate's charging stations according to step (2) j, obtain the Probability p of electric automobile in any candidate's charging station j charging j,
Figure BDA00001760886000051
J ∈ C, wherein, s is the charging station sum;
(4) according to above-mentioned charging Probability p j, traffic control center calculates electric automobile and at the prediction charge power of charging station j charging is:
P j=p j·P EV·
Wherein: P EVMaximum charge power for electric automobile car owner expectation;
(5) traffic control center calculates electric automobile and arrives candidate's charging station j required time The required charging interval
Figure BDA00001760886000053
And electric automobile is at the load prediction L of candidate's charging station j charging j(t);
t j arr = t 0 + d j min v j ;
t j dur = E B - ( E SOC 0 - d j min / KPEe ) P EV .
V wherein jThe wagon flow speed of the electric automobile starting point of measuring for traffic control center to candidate's charging station path,
Figure BDA00001760886000057
Arrive the distance of the shortest path of charging station j for electric automobile;
(6) repeating step (1)-(5); Traffic control center in the load prediction of candidate's charging station j stack, obtains total charging load prediction
Figure BDA00001760886000058
of charging station j and the load prediction of should always charging is sent to the power system dispatching center with all electric automobiles;
(7) power system dispatch center load forecasting based on the received charging
Figure BDA00001760886000059
and power system control center of the power system database charging station j belongs grid load point load forecasting
Figure BDA000017608860000510
j is calculated for each charging station maximum charging power
Figure BDA000017608860000511
P j max ( t ) = max { P EV , L j max ( t ) - L j O ( t ) - L j T ( t ) }
Wherein
Figure BDA000017608860000513
is the permission maximum charge power of the network load point under the charging station j that from the electric power system data storehouse, reads;
(8) repeating step (1)-(7), power system dispatching center are calculated the permission maximum charge power
Figure BDA00001760886000061
of each charging station of all electric automobiles in max mileage respectively and maximum charge power
Figure BDA00001760886000062
are sent to traffic control center;
(9) traffic control center is revised the charging interval of each electric automobile respectively according to maximum charge power
Figure BDA00001760886000063
, obtains the revised charging interval
Figure BDA00001760886000064
Traffic control center calculated for each electric car from starting point to destination time required and the time required to charge the total time
Figure BDA00001760886000066
and the total time
Figure BDA00001760886000067
Send to power system control center:
Figure BDA00001760886000068
(10) the power system dispatching center according to each electric automobile that calculates respectively total time
Figure BDA00001760886000069
that receives to the probability that j charging station charges is:
Figure BDA000017608860000610
And and then calculate the charge power P of each electric automobile respectively in charging station j charging j' and load prediction L j' (t) be:
P j ′ = p j ′ · P j max ( t 0 ) , j ∈ C
Figure BDA000017608860000612
(11) the power system dispatching center is with the load prediction stack of all electric automobiles at candidate's charging station j; Obtain total charging load prediction
Figure BDA000017608860000613
of charging station j and will be somebody's turn to do total charging load prediction being added in the load prediction
Figure BDA000017608860000614
of electric power system, be used for the load prediction
Figure BDA000017608860000615
of the affiliated network load point of calculation procedure (7) charging station j
L j O ( t ) = L j O , old ( t ) + L j T ′ ( t )
Wherein
Figure BDA000017608860000617
Be departure time t 0The load prediction of the network load point under the charging station j constantly in the electric power system data storehouse;
What (12) traffic control center obtained above-mentioned steps (9) sorts total time from small to large, and ranking results is showed the car owner of electric automobile through graphical interfaces.

Claims (1)

1. charging electric vehicle paths planning method based on intelligent transportation system is characterized in that this method may further comprise the steps:
(1) electric automobile is sent to traffic control center with electric automobile information, and electric automobile information comprises: starting point A, destination B, initial power state E Soc0, battery capacity E B, departure time t 0, electric automobile during traveling max mileage d RanAnd the per unit electric energy kilometer KPGe that goes; Traffic control center is according to the electric automobile information that receives, the shortest path between 2 of the selected A, B, and the distance of shortest path is designated as d ABmin, selected from the destination the nearest charging station T of B, the distance of the shortest path between destination B, the charging station T is designated as d BTminTraffic control center is judged the energy state of electric automobile: if d Ran>d ABmin+ d BTmin, judge that then electric automobile need not charging, if d Ran≤d ABmin+ d BTmin, then point out the driver to charging electric vehicle, then carry out step (2);
(2) traffic control center is according to max mileage d Ran, search electric automobile all charging stations in max mileage, as candidate's charging station of electric automobile, the set of note candidate charging station is C; Traffic control center searches for total time of B to the destination again by A point each charging station j in the candidate's charging station set respectively, remembers t j, wherein: j ∈ C;
(3) establish electric automobile at t 0Constantly set out, obtain the total time t that electric automobile arrives all candidate's charging stations according to step (2) j, obtain the Probability p of electric automobile in any candidate's charging station j charging j, J ∈ C, wherein, s is the charging station sum;
(4) according to above-mentioned charging Probability p j, traffic control center calculates electric automobile and at the prediction charge power of charging station j charging is:
P j=p j·P EV·
Wherein: P EVMaximum charge power for electric automobile car owner expectation;
(5) traffic control center calculates electric automobile and arrives candidate's charging station j required time
Figure FDA00001760885900012
The required charging interval
Figure FDA00001760885900013
And electric automobile is at the load prediction L of candidate's charging station j charging j(t);
t j arr = t 0 + d j min v j ;
t j dur = E B - ( E SOC 0 - d j min / KPEe ) P EV .
Figure FDA00001760885900021
V wherein jThe wagon flow speed of the electric automobile starting point of measuring for traffic control center to candidate's charging station path,
Figure FDA00001760885900022
Arrive the distance of the shortest path of charging station j for electric automobile;
(6) repeating step (1)-(5); Traffic control center in the load prediction of candidate's charging station j stack, obtains total charging load prediction
Figure FDA00001760885900023
of charging station j and the load prediction of should always charging is sent to the power system dispatching center with all electric automobiles;
(7) power system dispatch center load forecasting based on the received charging
Figure FDA00001760885900024
and power system control center of the power system database charging station j belongs grid load point load forecasting
Figure FDA00001760885900025
calculated for each charging station j The maximum charging power
Figure FDA00001760885900026
P j max ( t ) = max { P EV , L j max ( t ) - L j O ( t ) - L j T ( t ) }
Wherein
Figure FDA00001760885900028
is the permission maximum charge power of the network load point under the charging station j that from the electric power system data storehouse, reads;
(8) repeating step (1)-(7), power system dispatching center are calculated the permission maximum charge power of each charging station of all electric automobiles in max mileage respectively and maximum charge power
Figure FDA000017608859000210
are sent to traffic control center;
(9) traffic control center is revised the charging interval of each electric automobile respectively according to maximum charge power
Figure FDA000017608859000211
, obtains the revised charging interval
Figure FDA000017608859000212
Figure FDA000017608859000213
Traffic control center calculated for each electric car from starting point to destination time required and the time required to charge the total time
Figure FDA000017608859000214
and the total time
Figure FDA000017608859000215
Send to power system control center:
(10) the power system dispatching center according to each electric automobile that calculates respectively total time
Figure FDA000017608859000217
that receives to the probability that j charging station charges is:
Figure FDA000017608859000218
And and then calculate the charge power P of each electric automobile respectively in charging station j charging j' and load prediction L j' (t) be:
P j ′ = p j ′ · P j max ( t 0 ) , j ∈ C
Figure FDA00001760885900032
(11) the power system dispatching center is with the load prediction stack of all electric automobiles at candidate's charging station j; Obtain total charging load prediction of charging station j and will be somebody's turn to do total charging load prediction being added in the load prediction of electric power system, be used for the load prediction
Figure FDA00001760885900035
of the affiliated network load point of calculation procedure (7) charging station j
L j O ( t ) = L j O , old ( t ) + L j T ′ ( t )
Wherein
Figure FDA00001760885900037
Be departure time t 0The load prediction of the network load point under the charging station j constantly in the electric power system data storehouse;
What (12) traffic control center obtained above-mentioned steps (9) sorts total time
Figure FDA00001760885900038
from small to large, and ranking results is showed the car owner of electric automobile through graphical interfaces.
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