CN109636067A - A kind of electric car charging schedule optimization method based on particle swarm algorithm - Google Patents

A kind of electric car charging schedule optimization method based on particle swarm algorithm Download PDF

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CN109636067A
CN109636067A CN201910017770.XA CN201910017770A CN109636067A CN 109636067 A CN109636067 A CN 109636067A CN 201910017770 A CN201910017770 A CN 201910017770A CN 109636067 A CN109636067 A CN 109636067A
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CN109636067B (en
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钱丽萍
黄玉蘋
周欣悦
吴远
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Zhejiang University of Technology ZJUT
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Abstract

A method of the electric car charging schedule optimization based on particle swarm algorithm, include the following steps: 1) when electric car be in charge value it is lower when, user first sends charge request to server, server can first collect dump energy, air-conditioning state, the current location of electric car and the periphery charging station distribution situation of batteries of electric automobile after receiving request, while refer to surrounding road jam situation;2) road network is formatted, electric car charging schedule problem can be described as optimization problem;3) it is directed to this problem model, we use particle swarm algorithm to select suitable target charging station for electric car and reach the optimal driving path of target charging station;As long as 4) server and electric car are in connection status, the information of target charging station and arrival target charging station optimal path can be sent to user by server.The present invention improves the charge efficiency of electric car, provides the user with more intelligent charge path programme.

Description

A kind of electric car charging schedule optimization method based on particle swarm algorithm
Technical field
The present invention relates to electric car charging schedules and driving path to optimize field, especially a kind of to be calculated based on population The electric car charging schedule optimization method of method.
Background technique
Traditional energy depletion rate is very fast and reproduction speed is very slow, while very big to the pollution of environment.Closely Nian Lai, people constantly explore in Green Travel field, and electric car is exactly one of representative therein.Electric car is to use battery Power is provided instead of traditional energy, since energy utilization rate height, no pollution, the noise of electric car are low, so on the market Electric car ownership is increasing always.But electric car also has its defect compared with traditional energy automobile, existing can not be remote The problems such as lasting traveling of distance, charging time length, relevant electrically-charging equipment unreasonable allocation.
If the electric vehicle in traveling can provide rationally according to battery dump energy and charging station use state for user Charging schedule and driving path prioritization scheme, just can be reduced user to the worry of electric car driving range.Although at present To the correlative study in electric car charging schedule also in the starting stage, but the charging of the electric car based on particle swarm algorithm The scheme of scheduling and driving path optimization provides a kind of reliable accurate method, and method for optimizing scheduling can allow user reasonably to pacify Electric car charging opportunity and charging driving path are arranged, the waiting time of user is reduced, is also able to solve charging station resource not Reasonable distribution is inconvenient to user's bring, promotes the universal of electric car at the same time.
Summary of the invention
In order to overcome the lower deficiency of charge efficiency of existing electric car, in order to improve the charge efficiency of electric car, More intelligent charge path programme is provided the user with, the present invention provides a kind of electric cars based on particle swarm algorithm Charging schedule optimization method.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of electric car charging schedule optimization method based on particle swarm algorithm, the method for optimizing scheduling include as follows Step:
1) when electric car be in charge value it is lower when, user first to server send charge request, server, which receives, asks Dump energy, air-conditioning state, the current location of electric car and the periphery charging station point of batteries of electric automobile can be first collected after asking Cloth situation, while surrounding road jam situation is referred to, steps are as follows:
Step 1.1: by calculating the coulomb amount flowed in and out and estimating electric car residue using coulomb counting method Energy, in measurement process, battery capacity is using ampere-hour as measurement unit, calculation formula are as follows:
Al=Amax-Au (1-1)
Wherein, each parameter definition is as follows:
Amax: battery capacity under full power state;
Au: current used battery capacity;
Al: battery capacity workable for remaining;
Emax: the energy of battery under full power state;
El: battery remaining power;
Step 1.2: during electric automobile during traveling, the time that electric car reaches charging station suffers from congestion in road The influence of situation, introducing congestion coefficient ε indicates the jam situation of road, calculates electronic vapour further according to the jam situation on every section of road Running time of the vehicle in the road, calculation formula are as follows:
Wherein, each parameter definition is as follows:
Num: into the vehicle number of road;
T: threshold capacity guarantees the maximum vehicle number of the smooth passage of road;
C: critical value causes the vehicle number of congestion in road;
T: running time of the electric car in road;
Average running time of the electric car in the smooth passage in the section;
Step 1.3: energy consumed by air-conditioning during the energy and electric automobile during traveling that are consumed according to electric automobile during traveling Amount calculates the gross energy that electric car travels consumption on road are as follows:
E1=d × E (v) (1-6)
E2=t × E (1-7)
E=E1+E2 (1-8)
Wherein, each parameter definition is as follows:
V: travel speed of the electric car on road;
D: link length;
E (V): the electric car energy consumption corresponding with speed v traveling;
E1: the total energy consumption travelled on electric car;
E2: the air-conditioning total energy consumption of electric car;
E: the total energy consumption that electric car needs;
Step 1.4: filter out allow electric car charge f charging station, introducing indexed set I=1,2 ..., I ..., f }, allow the charging station of charging to be denoted as { CSi}i∈I
2) road network is formatted, it is assumed that electric car can only horizontal road x between crossing f and crossing mfmAnd crossing n Road y vertical with crossing mnmUpper traveling, both horizontally and vertically the road collection of the adjacent two o'clock of road network is combined into { x11,x12,…xfm} { y11,y12,…ynm, congestion coefficient sets both horizontally and vertically areWith F charging station is sequentially arranged in the terminal of road network, and in conjunction with step 1), electric car charging schedule problem is described as following optimization Problem:
s.t.xfm,ynm={ 0,1 } (2-1)
x11+y11=1 (2-2)
xnm+ynm=xnm'+yn'm (2-3)
xfm=xfm'+ynm (2-4)
Here, each parameter definition is as follows:
tf: the waiting time of the charging station of selection;
The road number in horizontal section;
The road number in vertical section;
Electric car is in horizontal section xfmTraveling energy consumption;
Electric car is in vertical section ynmTraveling energy consumption;
El: the dump energy of electric car;
Wherein, m'=m-1, n'=n-1, if m'=0, n'=0, corresponding xnm'、ynm'、xfm'It is 0;
3) it is directed to this problem model, particle swarm algorithm is used to select suitable target charging station for electric car and arrive Up to the optimal driving path of target charging station, steps are as follows:
Step 3.1: electric car is arranged in initialization road net model and the congestion coefficient that horizontal and vertical section is randomly generated Current location be starting point, each charging station be terminal, setting one biggish electric car initially travel total time Tini= 10000, current optimum solution CBS=Tini, setting travel route is L, current optimum drive route CBV=L, and the number of iterations k is arranged =1, it enables
Step 3.2: initialization population number I, the location of I population of initialization are the row for reaching target charging station i Sail route Lini={ Li}1≤i≤I, wherein Li={ Li,j}1≤j≤Ni, NiRefer to starting point to charging station i travel route number, just Beginningization the number of iterations iter=1;
Step 3.3: if iter≤I, and step 3.4 is executed, it is no to then follow the steps 3.7;
Step 3.4: if k < Niter, then T is calculatedsumAnd step 3.5 is executed, iter=iter+1, k=1 are otherwise enabled, and Execute step 3.3;
Step 3.5: if Tsum< Tini, then T is updatedini=Tsum, update Lini=Liter,kAnd step 3.6 is executed, otherwise Execute step 3.4;
Step 3.6: updating k=k+1, execute step 3.4;
Step 3.7: output electric car most short traveling total time CBS=Tini, the optimal travel route CBV=of electric car Lini
4) after, as long as server and electric car are in connection status, server can be by target charging station and arrival mesh The information of mark charging station optimal path is sent to user.
Technical concept of the invention are as follows: firstly, user needs first to send out to server when the charge value of electric car is lower Can be according to the dump energy and air-conditioning state of batteries of electric automobile after sending charge request, server to receive request, estimating can The remaining mileage number of traveling;Then, according to the current location of electric car and periphery charging station distribution situation, while library track is wanted Road jam situation selects accessibility optimal charging station, and plans optimal driving path for the user of electric car.Algorithm obtains It during optimal path, effectively can avoid solving local best points using Metropolis acceptance criterion, find out complete Office's optimum point, to obtain the optimal solution in electric automobile during traveling path.
Beneficial effects of the present invention are mainly manifested in: 1, the strong robustness of particle swarm algorithm, can effectively be obtained by search To optimal path, the Problems of Optimal Dispatch of electric car is relatively reliable solved;2, particle swarm algorithm is from RANDOM SOLUTION, by repeatedly In generation, finds optimal solution;3, particle swarm algorithm do not need traversal all situations compare their superiority and inferiority again and obtain optimal solution, this drop Low computation complexity also greatly reduces and calculates the time, improves computational efficiency.
Detailed description of the invention
Fig. 1 is road net model figure.
Specific embodiment
Present invention is further described in detail with reference to the accompanying drawing.
Referring to Fig.1, a kind of electric car charging schedule optimization method based on particle swarm algorithm, in other words, use particle Group's algorithm optimizes the charging schedule of electric car.The present invention is passed through in simplified road net model (as shown in Figure 1) Particle swarm algorithm carries out Path selection, final to provide the optimal path of charging.Electric car of the invention towards urgent need charging, for The jam situation of road, proposes in the energy of electric car remaining power, the status information of electric car and road net model Particle swarm algorithm obtains optimal charging station and charge path.The method for optimizing scheduling the following steps are included:
1) when electric car be in charge value it is lower when, user first to server send charge request, server, which receives, asks Dump energy, air-conditioning state, the current location of electric car and the periphery charging station point of batteries of electric automobile can be first collected after asking Cloth situation, while surrounding road jam situation is referred to, steps are as follows:
Step 1.1: by calculating the coulomb amount flowed in and out and estimating electric car residue using coulomb counting method Energy, in measurement process, battery capacity is using ampere-hour as measurement unit, calculation formula are as follows:
Al=Amax-Au (1-1)
Wherein, each parameter definition is as follows:
Amax: battery capacity under full power state;
Au: current used battery capacity;
Al: battery capacity workable for remaining;
Emax: the energy of battery under full power state;
El: battery remaining power;
Step 1.2: during electric automobile during traveling, the time that electric car reaches charging station suffers from congestion in road The influence of situation, introducing congestion coefficient ε indicates the jam situation of road, calculates electronic vapour further according to the jam situation on every section of road Running time of the vehicle in the road, calculation formula are as follows:
Wherein, each parameter definition is as follows:
Num: into the vehicle number of road;
T: threshold capacity guarantees the maximum vehicle number of the smooth passage of road;
C: critical value causes the vehicle number of congestion in road;
T: running time of the electric car in road;
Average running time of the electric car in the smooth passage in the section;
Step 1.3: energy consumed by air-conditioning during the energy and electric automobile during traveling that are consumed according to electric automobile during traveling Amount calculates the gross energy that electric car travels consumption on road are as follows:
E1=d × E (v) (1-6)
E2=t × E (1-7)
E=E1+E2 (1-8)
Wherein, each parameter definition is as follows:
V: travel speed of the electric car on road;
D: link length;
E (V): the electric car energy consumption corresponding with speed v traveling;
E1: the total energy consumption travelled on electric car;
E2: the air-conditioning total energy consumption of electric car;
E: the total energy consumption that electric car needs;
Step 1.4: filter out allow electric car charge f charging station, introducing indexed set I=1,2 ..., I ..., f }, allow the charging station of charging to be denoted as { CSi}i∈I
2) road network is formatted, it is assumed that electric car can only horizontal road x between crossing f and crossing mfmAnd crossing n Road y vertical with crossing mnmUpper traveling, both horizontally and vertically the road collection of the adjacent two o'clock of road network is combined into { x11,x12,…xfm} { y11,y12,…ynm, congestion coefficient sets both horizontally and vertically areWithF are filled Power station is sequentially arranged in the terminal of road network, and in conjunction with step 1), electric car charging schedule problem is described as following optimization problem:
s.t.xfm,ynm={ 0,1 } (2-1)
x11+y11=1 (2-2)
xnm+ynm=xnm'+yn'm (2-3)
xfm=xfm'+ynm (2-4)
Here, each parameter definition is as follows:
tf: the waiting time of the charging station of selection;
The road number in horizontal section;
The road number in vertical section;
Electric car is in horizontal section xfmTraveling energy consumption;
Electric car is in vertical section ynmTraveling energy consumption;
El: the dump energy of electric car;
Wherein, m'=m-1, n'=n-1, if m'=0, n'=0, corresponding xnm'、ynm'、xfm'It is 0;
3) it is directed to this problem model, particle swarm algorithm is used to select suitable target charging station for electric car and arrive Up to the optimal driving path of target charging station, steps are as follows:
Step 3.1: electric car is arranged in initialization road net model and the congestion coefficient that horizontal and vertical section is randomly generated Current location be starting point, each charging station be terminal, setting one biggish electric car initially travel total time Tini= 10000, current optimum solution CBS=Tini, setting travel route is L, current optimum drive route CBV=L, and the number of iterations k is arranged =1, it enables
Step 3.2: initialization population number I, the location of I population of initialization are the row for reaching target charging station i Sail route Lini={ Li}1≤i≤I, whereinNiRefer to starting point to charging station i travel route number, initially Change the number of iterations iter=1;
Step 3.3: if iter≤I, and step 3.4 is executed, it is no to then follow the steps 3.7;
Step 3.4: if k < Niter, then T is calculatedsumAnd step 3.5 is executed, iter=iter+1, k=1 are otherwise enabled, and Execute step 3.3;
Step 3.5: if Tsum< Tini, then T is updatedini=Tsum, update Lini=Liter,kAnd step 3.6 is executed, otherwise Execute step 3.4;
Step 3.6: updating k=k+1, execute step 3.4;
Step 3.7: output electric car most short traveling total time CBS=Tini, the optimal travel route CBV=of electric car Lini
4) after, as long as server and electric car are in connection status, server can be by target charging station and arrival mesh The information of mark charging station optimal path is sent to user.

Claims (1)

1. a kind of electric car charging schedule optimization method based on particle swarm algorithm, which is characterized in that the optimizing scheduling side Method includes the following steps:
1) when electric car be in charge value it is lower when, user first to server send charge request, after server receives request Dump energy, air-conditioning state, the current location of electric car and the periphery charging station that batteries of electric automobile can first be collected are distributed feelings Condition, while surrounding road jam situation is referred to, steps are as follows:
Step 1.1: by calculating the coulomb amount flowed in and out and estimating electric car dump energy using coulomb counting method, In measurement process, battery capacity is using ampere-hour as measurement unit, calculation formula are as follows:
Al=Amax-Au (1-1)
Wherein, each parameter definition is as follows:
Amax: battery capacity under full power state;
Au: current used battery capacity;
Al: battery capacity workable for remaining;
Emax: the energy of battery under full power state;
El: battery remaining power;
Step 1.2: during electric automobile during traveling, the time that electric car reaches charging station suffers from congestion in road situation Influence, introducing congestion coefficient ε indicates the jam situation of road, calculates electric car further according to the jam situation on every section of road and exists The running time of the road, calculation formula are as follows:
Wherein, each parameter definition is as follows:
Num: into the vehicle number of road;
T: threshold capacity guarantees the maximum vehicle number of the smooth passage of road;
C: critical value causes the vehicle number of congestion in road;
T: running time of the electric car in road;
Average running time of the electric car in the smooth passage in the section;
Step 1.3: energy meter consumed by air-conditioning during the energy and electric automobile during traveling that are consumed according to electric automobile during traveling Calculate the gross energy that electric car travels consumption on road are as follows:
E1=d × E (v) (1-6)
E2=t × E (1-7)
E=E1+E2 (1-8)
Wherein, each parameter definition is as follows:
V: travel speed of the electric car on road;
D: link length;
E (V): the electric car energy consumption corresponding with speed v traveling;
E1: the total energy consumption travelled on electric car;
E2: the air-conditioning total energy consumption of electric car;
E: the total energy consumption that electric car needs;
Step 1.4: f charging station for allowing electric car to charge is filtered out, is introduced indexed set I={ 1,2 ..., i ..., f }, The charging station of charging is allowed to be denoted as { CSi}i∈I
2) road network is formatted, it is assumed that electric car can only horizontal road x between crossing f and crossing mfmAnd crossing n and road The vertical road y of mouth mnmUpper traveling, both horizontally and vertically the road collection of the adjacent two o'clock of road network is combined into { x11,x12,…xfmAnd {y11,y12,…ynm, congestion coefficient sets both horizontally and vertically areWithF Charging station is sequentially arranged in the terminal of road network, and in conjunction with step 1), electric car charging schedule problem is described as following optimization and asks Topic:
s.t.xfm,ynm={ 0,1 } (2-1)
x11+y11=1 (2-2)
xnm+ynm=xnm'+yn'm (2-3)
xfm=xfm'+ynm (2-4)
Here, each parameter definition is as follows:
tf: the waiting time of the charging station of selection;
The road number in horizontal section;
The road number in vertical section;
Electric car is in horizontal section xfmTraveling energy consumption;
Electric car is in vertical section ynmTraveling energy consumption;
El: the dump energy of electric car;
Wherein, m'=m-1, n'=n-1, if m'=0, n'=0, corresponding xnm'、ynm'、xfm'It is 0;
3) it is directed to this problem model, particle swarm algorithm is used to select suitable target charging station for electric car and reach mesh The optimal driving path of charging station is marked, steps are as follows:
Step 3.1: working as electric car is arranged in initialization road net model and the congestion coefficient that horizontal and vertical section is randomly generated Anteposition is set to starting point, and each charging station is terminal, and one biggish electric car of setting initially travels total time Tini=10000, when Preceding optimum solution CBS=Tini, setting travel route is L, current optimum drive route CBV=L, and the number of iterations k=1 is arranged, enables
Step 3.2: initialization population number I, the location of I population of initialization are the traveling road for reaching target charging station i Line Lini={ Li}1≤i≤I, whereinNiRefer to travel route number of the starting point to charging station i, initialization The number of iterations iter=1;
Step 3.3: if iter≤I, and step 3.4 is executed, it is no to then follow the steps 3.7;
Step 3.4: if k < Niter, then T is calculatedsumAnd step 3.5 is executed, iter=iter+1, k=1 are otherwise enabled, and execute Step 3.3;
Step 3.5: if Tsum< Tini, then T is updatedini=Tsum, update Lini=Liter,kAnd step 3.6 is executed, otherwise execute Step 3.4;
Step 3.6: updating k=k+1, execute step 3.4;
Step 3.7: output electric car most short traveling total time CBS=Tini, the optimal travel route CBV=L of electric carini
4) after, as long as server and electric car are in connection status, server can fill target charging station and arrival target The information of power station optimal path is sent to user.
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CN110288132A (en) * 2019-06-06 2019-09-27 浙江工业大学 A kind of layout method of the charging pile for electric vehicle based on improved TLBO algorithm
CN110288132B (en) * 2019-06-06 2021-03-23 浙江工业大学 Improved TLBO algorithm-based electric vehicle charging pile layout method
CN110543967A (en) * 2019-07-23 2019-12-06 浙江工业大学 electric vehicle waiting time distribution short-time prediction method in network connection charging station environment
CN110543967B (en) * 2019-07-23 2021-06-08 浙江工业大学 Electric vehicle waiting time distribution short-time prediction method in network connection charging station environment
CN110533901A (en) * 2019-08-29 2019-12-03 厦门金龙联合汽车工业有限公司 A kind of the charging schedule method and cloud management server of automatic driving vehicle group
WO2021036132A1 (en) * 2019-08-29 2021-03-04 厦门金龙联合汽车工业有限公司 Charging scheduling method for unmanned vehicle group and cloud management server
CN112036719A (en) * 2020-08-20 2020-12-04 南京邮电大学 Electric vehicle path scheduling method for wireless charging through bus network
CN113222226A (en) * 2021-04-26 2021-08-06 武汉科技大学 Method and device for shunting motor train, electronic equipment and storage medium
CN113222226B (en) * 2021-04-26 2022-06-24 武汉科技大学 Method and device for shunting motor train station, electronic equipment and storage medium

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