CN102709984B - 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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CN102709984B
CN102709984B CN201210194873.1A CN201210194873A CN102709984B CN 102709984 B CN102709984 B CN 102709984B CN 201210194873 A CN201210194873 A CN 201210194873A CN 102709984 B CN102709984 B CN 102709984B
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electric automobile
charging station
traffic control
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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 based on intelligent transportation system, belong to power system operation and control technology field.
Background technology
International SAE J1772-2010 standard has been stipulated three kinds of charging modes: for " exchange grade 1 " of charging at a slow speed and for " exchanging grade 2 " and " DC charging " of quick charge.Charging is mainly carried out at charging pile at a slow speed, and charging duration is 6～8 hours, is suitable for the long-time electric automobile stopping.Quick charge is mainly carried out at charging station, charging duration is 15 minutes～2 hours, be used to the electric automobile travelling in way to carry out emergent charging, car owner wishes that the charge power that charging station provides is the receptible maximum power of electric automobile, to complete as early as possible charging.Yet charging electric vehicle may cause adverse effect to electric power system aspect a lot: as traffic overload, the defective and electric energy loss increase of voltage levvl etc.When electric automobile is introduced behind market on a large scale, the space randomness of the electric vehicle rapid charging distributed pole that may cause loading between different charging stations is inhomogeneous, brings difficulty to 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 to charging station electricity needs has two classes: the one, and the position of charging station in geographical wiring diagram; The 2nd, the position of electric automobile and speed.The former can obtain by GIS-Geographic Information System (hereinafter to be referred as GIS), and the latter can pass through Global Satellite status system (hereinafter to be referred as GPS) Real-time Collection.GIS can form accurate electronic chart.By the cooperation of GIS and database, driver can obtain the demonstration directly perceived of street and peripheral facility thereof; GPS be based on 24 satellites for locating and navigation system regularly, 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 object 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 power system operation adverse effect, be conducive to save car owner's time, contribute to again to promote electric network security.
The charging electric vehicle paths planning method based on intelligent transportation system that the present invention proposes, comprises the following steps:
(1) electric automobile is sent to traffic control center by 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 travels; Traffic control center is according to the electric automobile information receiving, the shortest path between 2 of selected A, B, and the distance of shortest path is designated as d aBmin, select the nearest charging station T from destination B, the distance of the shortest path between destination B, charging station T is designated as d bTmin; Traffic control center judges the energy state of electric automobile: if d ran>d aBmin+ d bTmin, judge that electric automobile is without charging, if d ran≤ d aBmin+ d bTmin, point out driver to charging electric vehicle, carry out step (2);
(2) traffic control center is according to max mileage d ran, all charging stations of search electric automobile in max mileage, as candidate's charging station of electric automobile, the set of note candidate charging station is C; Traffic control center search for respectively by A point to each the charging station j in the set of candidate's charging station again to total time of destination B, note t j, wherein: j ∈ C;
(3) establish electric automobile at t 0constantly set out, according to step (2), obtain the total time t that electric automobile arrives all candidate's charging stations j, obtain electric automobile in the Probability p of any one candidate's charging station j charging j,
Figure BDA00001760886000021
j ∈ C, wherein, s is 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 electric automobile starting point of measuring for traffic control center is to the wagon flow speed on candidate's charging station path,
Figure BDA00001760886000027
for electric automobile arrives the distance of the shortest path of charging station j;
(6) repeating step (1)-(5), traffic control center is the load prediction stack at candidate's charging station j by all electric automobiles, obtains total charging load prediction of charging station j
Figure BDA00001760886000028
and this total charging load prediction is sent to power system dispatching center;
(7) power system dispatching center is according to the charging load prediction receiving
Figure BDA00001760886000029
and the load prediction of the network load point under charging station j in the Database Management System in Electrical Power System at power system dispatching center
Figure BDA000017608860000210
calculate the maximum charge power of each charging station j
Figure BDA000017608860000211
P j max ( t ) = max { P EV , L j max ( t ) - L j O ( t ) - L j T ( t ) }
Wherein
Figure BDA00001760886000032
permission maximum charge power for the network load point under the charging station j reading from Database Management System in Electrical Power System;
(8) repeating step (1)-(7), the permission maximum charge power of each charging station of all electric automobiles in max mileage is calculated respectively at power system dispatching center
Figure BDA00001760886000033
and by maximum charge power
Figure BDA00001760886000034
be sent to traffic control center;
(9) traffic control center is according to maximum charge power
Figure BDA00001760886000035
respectively the charging interval of each electric automobile is revised, obtained the revised charging interval
Figure BDA00001760886000036
Figure BDA00001760886000037
Each electric automobile calculates from starting point to destination required time and the total time of charging required time in traffic control center
Figure BDA00001760886000038
and by total time
Figure BDA00001760886000039
be sent to power system dispatching center:
Figure BDA000017608860000310
(10) power system dispatching center is according to the total time receiving
Figure BDA000017608860000311
calculating respectively each electric automobile to the probability of j charging station charging is:
Figure BDA000017608860000312
And and then calculate respectively each electric automobile at the charge power P of 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) power system dispatching center load prediction stack at candidate's charging station j by all electric automobiles, obtains total charging load prediction of charging station j
Figure BDA000017608860000315
and by the be added to load prediction of electric power system of this total charging load prediction
Figure BDA000017608860000316
in, for the load prediction of the network load point under calculation procedure (7) charging station j
Figure BDA000017608860000317
L j O ( t ) = L j O , old ( t ) + L j T ′ ( t )
Wherein
Figure BDA000017608860000319
for departure time t 0the load prediction of the network load point under the charging station j constantly in Database Management System in Electrical Power System;
(12) total time that traffic control center obtains above-mentioned steps (9)
Figure BDA00001760886000041
sort from small to large, and ranking results is showed to the car owner of electric automobile by graphical interfaces.
The charging electric vehicle paths planning method based on intelligent transportation system that the present invention proposes, its feature and advantage are:
When electric automobile becomes after main traffic instrument, car owner and electric power system face new problem.On the one hand, car owner need to consider optimal charge station of How to choose and charge path, makes the total time cost of trip and charging the shortest; On the other hand, electric power system need to avoid electric automobile concentrate in a large number the charging of a certain seat charging station cause its overload and voltage levvl too low.The inventive method can address the above problem, and for electric automobile car owner provides an optimal charge path, the time that is conducive to save 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.
Accompanying drawing explanation
Fig. 1 is the system block diagram that uses the inventive method.
Fig. 2 is the orientation schematic diagram of starting point, destination, charging station, shortest path of electric automobile in the inventive method etc.
Embodiment
The charging electric vehicle paths planning method based on intelligent transportation system that the present invention proposes, as shown in Figure 1, its method comprises the following steps its system block diagram:
(1) electric automobile is sent to traffic control center by 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 travels; Traffic control center is according to the electric automobile information receiving, the shortest path between 2 of selected A, B, and the distance of shortest path is designated as d aBmin, select the nearest charging station T from destination B, the distance of the shortest path between destination B, charging station T is designated as d bTmin; Traffic control center judges the energy state of electric automobile: if d ran>d aBmin+ d bTmin, judge that electric automobile is without charging, if d ran≤ d aBmin+ d bTmin, point out driver to charging electric vehicle, carry out step (2).The orientation schematic diagram of the starting point of above-mentioned electric automobile, destination, charging station, shortest path etc. as shown in Figure 2;
(2) traffic control center is according to max mileage d ran, all charging stations of search electric automobile in max mileage, as candidate's charging station of electric automobile, the set of note candidate charging station is C; Traffic control center search for respectively by A point to each the charging station j in the set of candidate's charging station again to total time of destination B, note t j, wherein: j ∈ C;
(3) establish electric automobile at t 0constantly set out, according to step (2), obtain the total time t that electric automobile arrives all candidate's charging stations j, obtain electric automobile in the Probability p of any one candidate's charging station j charging j,
Figure BDA00001760886000051
j ∈ C, wherein, s is 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 BDA00001760886000052
the required charging interval 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 BDA00001760886000056
V wherein jthe electric automobile starting point of measuring for traffic control center is to the wagon flow speed on candidate's charging station path,
Figure BDA00001760886000057
for electric automobile arrives the distance of the shortest path of charging station j;
(6) repeating step (1)-(5), traffic control center is the load prediction stack at candidate's charging station j by all electric automobiles, obtains total charging load prediction of charging station j
Figure BDA00001760886000058
and this total charging load prediction is sent to power system dispatching center;
(7) power system dispatching center is according to the charging load prediction receiving
Figure BDA00001760886000059
and the load prediction of the network load point under charging station j in the Database Management System in Electrical Power System at power system dispatching center calculate the maximum charge power of each charging station j
Figure BDA000017608860000511
P j max ( t ) = max { P EV , L j max ( t ) - L j O ( t ) - L j T ( t ) }
Wherein
Figure BDA000017608860000513
permission maximum charge power for the network load point under the charging station j reading from Database Management System in Electrical Power System;
(8) repeating step (1)-(7), the permission maximum charge power of each charging station of all electric automobiles in max mileage is calculated respectively at power system dispatching center
Figure BDA00001760886000061
and by maximum charge power
Figure BDA00001760886000062
be sent to traffic control center;
(9) traffic control center is according to maximum charge power
Figure BDA00001760886000063
respectively the charging interval of each electric automobile is revised, obtained the revised charging interval
Figure BDA00001760886000064
Figure BDA00001760886000065
Each electric automobile calculates from starting point to destination required time and the total time of charging required time in traffic control center
Figure BDA00001760886000066
and by total time
Figure BDA00001760886000067
be sent to power system dispatching center:
Figure BDA00001760886000068
(10) power system dispatching center is according to the total time receiving
Figure BDA00001760886000069
calculating respectively each electric automobile to the probability of j charging station charging is:
Figure BDA000017608860000610
And and then calculate respectively each electric automobile at the charge power P of 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) power system dispatching center load prediction stack at candidate's charging station j by all electric automobiles, obtains total charging load prediction of charging station j
Figure BDA000017608860000613
and by the be added to load prediction of electric power system of this total charging load prediction in, for the load prediction of the network load point under calculation procedure (7) charging station j
Figure BDA000017608860000615
L j O ( t ) = L j O , old ( t ) + L j T ′ ( t )
Wherein
Figure BDA000017608860000617
for departure time t 0the load prediction of the network load point under the charging station j constantly in Database Management System in Electrical Power System;
(12) total time that traffic control center obtains above-mentioned steps (9)
Figure BDA000017608860000618
sort from small to large, and ranking results is showed to the car owner of electric automobile by graphical interfaces.

Claims (1)

1. the charging electric vehicle paths planning method based on intelligent transportation system, is characterized in that the method comprises the following steps:
(1) electric automobile is sent to traffic control center by 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 travels; Traffic control center is according to the electric automobile information receiving, the shortest path between 2 of selected A, B, and the distance of shortest path is designated as d aBmin, select the nearest charging station T from destination B, the distance of the shortest path between destination B, charging station T is designated as d bTmin; Traffic control center judges the energy state of electric automobile: if d ran> d aBmin+ d bTmin, judge that electric automobile is without charging, if d ran≤ d aBmin+ d bTmin, point out driver to charging electric vehicle, carry out step (2);
(2) traffic control center is according to max mileage d ran, all charging stations of search electric automobile in max mileage, as candidate's charging station of electric automobile, the set of note candidate charging station is C; Traffic control center search for respectively by A point to each the charging station j in the set of candidate's charging station again to total time of destination B, note t j, wherein: j ∈ C;
(3) establish electric automobile at t 0constantly set out, according to step (2), obtain the total time t that electric automobile arrives all candidate's charging stations j, obtain electric automobile in the Probability p of any one candidate's charging station j charging j, j ∈ C, wherein, s is 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 FDA0000440493370000012
the required charging interval
Figure FDA0000440493370000013
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 / KPGe ) P EV .
Figure FDA0000440493370000021
V wherein jthe electric automobile starting point of measuring for traffic control center is to the wagon flow speed on candidate's charging station path,
Figure FDA0000440493370000022
for electric automobile arrives the distance of the shortest path of charging station j;
(6) repeating step (1)-(5), traffic control center is the load prediction stack at candidate's charging station j by all electric automobiles, obtains total charging load prediction of charging station j and this total charging load prediction is sent to power system dispatching center;
(7) power system dispatching center is according to the charging load prediction receiving
Figure FDA0000440493370000024
and the load prediction of the network load point under charging station j in the Database Management System in Electrical Power System at power system dispatching center calculate the maximum charge power of each charging station j
Figure FDA0000440493370000026
P j max ( t ) = max { P EV , L j max ( t ) - L j O ( t ) - L j T ( t ) }
Wherein
Figure FDA0000440493370000028
permission maximum charge power for the network load point under the charging station j reading from Database Management System in Electrical Power System;
(8) repeating step (1)-(7), the permission maximum charge power of each charging station of all electric automobiles in max mileage is calculated respectively at power system dispatching center
Figure FDA0000440493370000029
and by maximum charge power
Figure FDA00004404933700000210
be sent to traffic control center;
(9) traffic control center is according to maximum charge power
Figure FDA00004404933700000211
respectively the charging interval of each electric automobile is revised, obtained the revised charging interval
Figure FDA00004404933700000212
t j dur ′ = E B - ( E SOC 0 - d j min / KPGe ) P j max ( t 0 ) ,
Each electric automobile calculates from starting point to destination required time and the total time of charging required time in traffic control center
Figure FDA00004404933700000214
and by total time
Figure FDA00004404933700000215
be sent to power system dispatching center:
Figure FDA00004404933700000216
(10) power system dispatching center is according to the total time receiving
Figure FDA00004404933700000217
calculating respectively each electric automobile to the probability of j charging station charging is:
Figure FDA00004404933700000218
And and then calculate respectively each electric automobile at the charge power P of charging station j charging j' and load prediction L j' (t) be:
P j ′ = p j ′ · P j max ( t 0 ) , j ∈ C
Figure FDA0000440493370000032
(11) power system dispatching center load prediction stack at candidate's charging station j by all electric automobiles, obtains total charging load prediction of charging station j
Figure FDA0000440493370000033
and by the be added to load prediction of electric power system of this total charging load prediction
Figure FDA0000440493370000034
in, for the load prediction of the network load point under calculation procedure (7) charging station j
Figure FDA0000440493370000035
L j O ( t ) = L j O , old ( t ) + L j T ′ ( t )
Wherein
Figure FDA0000440493370000037
for departure time t 0the load prediction of the network load point under the charging station j constantly in Database Management System in Electrical Power System;
(12) total time that traffic control center obtains above-mentioned steps (9)
Figure FDA0000440493370000038
sort from small to large, and ranking results is showed to the car owner of electric automobile by graphical interfaces.
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