CN108773279A - A kind of electric vehicle charge path method and device for planning - Google Patents
A kind of electric vehicle charge path method and device for planning Download PDFInfo
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- CN108773279A CN108773279A CN201810391319.XA CN201810391319A CN108773279A CN 108773279 A CN108773279 A CN 108773279A CN 201810391319 A CN201810391319 A CN 201810391319A CN 108773279 A CN108773279 A CN 108773279A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/44—Control modes by parameter estimation
-
- 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
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
-
- 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
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/54—Energy consumption estimation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- 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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—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
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Abstract
The problem of the application during automobile user trip for needing promptly to charge, proposes a kind of electric vehicle charge path method and device for planning.This method considers real-time dynamic energy consumption in driving process, mainly includes the following steps that：First average energy consumption value of the electric vehicle under all kinds of driving cycles is calculated according to electric vehicle history driving cycle data；Secondly electric vehicle is calculated by the dynamic remaining capacity after each section according to the real time running operating mode dynamic for travelling each section on the way；The information for Real-time Traffic Information and charging station monitoring operation the system publication issued according to internet calculates user's trip distance, travel time and charging cost；Use ant group algorithm with battery dump energy for constraint, optimal the sum of trip distance, travel time and charging cost three are goal programming electric vehicle charge path.
Description
Technical field
The present invention relates to electric vehicle charge path planning field more particularly to a kind of electric vehicle charge path planning sides
Method and device.
Background technology
With China's rapid economic development, automobile demand amount sustainable growth, the energy shortage thus brought and environment are dirty
Dye problem is more urgent.To meet city energy-saving and emission-reduction demand, people's environmental consciousness is promoted, fast-developing new-energy automobile is needed
Industry.Wherein, perfect electrically-charging equipment is the important leverage for developing new-energy automobile industry.However, current new-energy automobile
Growth scale is substantially higher than the growth scale of charging pile quantity, and the notch between knee still constantly expands, and upper road quick charge is still
To restrict the principal element of Rechargeable vehicle development, to alleviate remaining insufficient driving range, charging during new-energy automobile trip
The problems such as time is long, it is necessary to which new-energy automobile charging intelligent Service Platform under active construction real time information promotes electric vehicle
The two-way interaction of energy and information between electrically-charging equipment provides the services such as path planning, charging guiding for automobile user.
Static road network environment often only considered, suddenly to electric vehicle charging boot policy correlative study both at home and abroad at present
Influence of the time variation of traffic environment under actual conditions to electric vehicle power consumption has been omited, therefore, how to have passed through a thinking letter
The clear and effective method of Ming and Qing provides the traveling real-time Expenditure Levels of electricity on the way, to which selection is most preferably filled for electric vehicle car owner
Power path is one of the major issue that current electric vehicle charging industry faces.
In view of this, proposing a kind of electric vehicle charge path method and device for planning.
Invention content
The present invention proposes a kind of electric vehicle charge path planing method of dynamic energy consumption in consideration driving process, using mutual
Join the dynamic information of Web Publishing, the dynamic energy consumption of electric vehicle on the way in traveling is calculated, in conjunction with from starting point to purpose
The dynamic information of ground running section calculates user's trip distance, travel time and charging cost, and considers route mistake
Remaining capacity constraint in journey, using ant colony optimization for solving electric vehicle charge path, reduces the trip anxiety of user.
In order to achieve the above objectives, the present invention uses following technical proposals：
A kind of electric vehicle charge path planing method, including：
S1. all kinds of driving cycles of electric vehicle are obtained, average energy consumption value of the electric vehicle under all kinds of driving cycles is calculated；
S2. travel route is obtained, calculates electric vehicle in the dynamic remaining capacity for travelling each section of passage in transit；
S3. user's trip distance, travel time and charging cost are calculated according to travel route；
S4. electric vehicle charge path is determined according to the result of calculation of above-mentioned steps S1-S3.
A kind of electric vehicle charge path device for planning, described device include：
First computing module obtains all kinds of driving cycles of electric vehicle, calculates electric vehicle under all kinds of driving cycles
Average energy consumption value；
Second computing module obtains travel route, and it is remaining to calculate dynamic of the electric vehicle in each section of traveling passage in transit
Electricity；
Third computing module calculates user's trip distance, travel time and charging cost according to travel route；
Path planning module determines that electric vehicle fills according to the result of calculation of above-mentioned first, second, and third computing module
Power path.
Description of the drawings
Specific embodiments of the present invention will be described in further detail below in conjunction with the accompanying drawings：
Fig. 1 is a kind of electric vehicle charge path planing method schematic diagram；
Fig. 2 is a kind of specific processing step schematic diagrames of step S1；
Fig. 3 is a kind of specific processing step schematic diagrames of step S2；
Fig. 4 is a kind of specific processing step schematic diagrames of step S3；
Fig. 5 is that ant group algorithm flow chart solves optimal solution step schematic diagram；
Fig. 6 is a kind of electric vehicle charge path device for planning schematic diagram.
Specific implementation mode
In order to illustrate more clearly of the application, the application is done further with reference to preferred embodiments and drawings
It is bright.Similar component is indicated with identical reference numeral in attached drawing.It will be appreciated by those skilled in the art that being had below
The content of body description is illustrative and be not restrictive, and the protection domain of the application should not be limited with this.
This application provides a kind of electric vehicle charge path method and device for planning, can be in conjunction with the following drawings and real
Example is applied, the application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain
The application does not limit the application.
According to the one side of the application, the present embodiment provides a kind of electric vehicle charge path planing methods, such as Fig. 1 institutes
Show, this method includes：
S1. average energy consumption value of the electric vehicle under all kinds of driving cycles is calculated；
S2. electric vehicle is calculated in the dynamic remaining capacity for travelling each section of passage in transit；
S3. user's trip distance, travel time and charging cost are calculated；
S4. electric vehicle charge path is determined according to the result of calculation of above-mentioned steps S1-S3.
Further, with reference to figure 2, the S1 includes the following steps：
S101. the driving cycle characteristic parameter of electric automobile air conditioner opening and closing and driver's driving performance can be reflected by choosing；
Preferably, it chooses and quotes more characteristic parameter in the prior art, including：Maximum speed, averagely adds average speed
Speed, peak acceleration accelerate ratio, deceleration ratio, at the uniform velocity eight ratio, idling ratio characteristic parameters to be used for driving cycle
Classification, in the present embodiment, each feature be retrieved as it is multigroup specifically calculated, parameter is after filtering shown in reference table 1- tables 3.
Wherein, table 1 is maximum speed, average speed, average acceleration and peak acceleration related data, and 2 acceleration ratios of table subtract
Speed ratio example, at the uniform velocity ratio and idling ratio related data, table 3 be environment temperature, operation of air conditioner power, acceleration standard deviation and
Rate of acceleration change standard deviation related data；Further, in this embodiment follow-up calculating be all made of above-mentioned each table and provided
Data.
Secondly, environment temperature can run vehicle and have an impact, two selection environment temperature, operation of air conditioner power feature ginsengs
Number is classified for driving cycle.
Finally, it is special as different driving in reflection driving cycle that acceleration standard deviation, rate of acceleration change standard deviation are chosen
The characteristic parameter of property.
Table 1
Table 2
Table 3
S102. it analyzes between correlation and each characteristic parameter between selected driving cycle characteristic parameter and power consumption
Correlation it is strong and weak；
Electric vehicle history driving cycle data are studied, it is preferable that before charging to next time after this charging
Driving process be known as a cycle, in each cycle, electric vehicle is often travelled to 1km point for a traveling segment, to drawing
The traveling segment divided is analyzed, and above-mentioned 12 characteristic parameters and the power consumption under each segment are calculated.Using Spearman order
Related coefficient calculates the correlation of 12 characteristic parameters and electric vehicle energy consumption, shown in calculation formula such as formula (1),
According to result of calculation, the characteristic parameter variable with correlation minimum between power consumption is deleted.
S103. principal component analytical method is used to carry out Dimension Reduction Analysis to driving cycle characteristic parameter；
The segment comprising 11 driving cycle characteristic parameters is analyzed using principal component analytical method, obtain it is each it is main at
Point characteristic value and contribution rate, according to the principle of principal component analysis it is found that choose accumulation contribution rate it is main more than 80% n at
Divide it is considered that the information that reflection primal variable is included enough, it is weaker to choose this n principal component mainly reflects and correlation
M driving cycle characteristic parameter be used for clustering.
S104. it uses and is classified to the history driving cycle data of electric vehicle based on Fuzzy C-Means Clustering Algorithm；
Calculate average energy consumption value of the electric vehicle under all kinds of driving cycles.
Clustering is carried out to the sample data comprising m driving cycle characteristic parameter, according to the cluster centre of each operating mode,
According to apart from minimum principle, determine that the generic of each history driving cycle segment of electric vehicle, distance calculation formula are
dq=| | x-cq| | q=1,2..., 12 (2)
Wherein, x=[x1, x2..., x8] be certain segment characteristic parameter, cq=[cq1, cq2..., cq12] it is cluster q
Cluster centre parameter.Take the average value of each segment energy consumption in judged same class driving cycle as electronic vapour under the operating mode
The energy consumption of vehicle.
Further, with reference to figure 3, the S2 includes the following steps：
S201. internet is utilized to obtain the real time running operating mode in each section；
S202. electric vehicle is calculated in the real time energy consumption for travelling each section of passage in transit；It calculates by after the section
Remaining capacity.
Preferably, vehicle running section RijDistance be rij, utilize the section real time running road of internet publication
Condition calculates the cluster centre in the section, judges the real time running operating mode classification in the section for gq, corresponding every kilometer of the section
Energy consumption is Eq, then it is by the consumed electricity in the section
Eij=Eq×rij (3)
Further, with reference to figure 4, the S3 includes the following steps：
S301. the stroke distances cost in each section from starting point to destination is calculated；
Preferably, user's total distance during going to destination includes that electric vehicle passes through charging station S from starting point o
Arrive at the stroke total distance of D.Then stroke distances cost is：
In formula, xijFor 0-1 path decision variables, if section r of the electric vehicle between road-net node i and jij, then xij
=1, otherwise xij=0.LaIndicate the road-net node set that electric vehicle passes through, SaIndicate the charging station section that electric vehicle passes through
Point set.
S302. journey time cost needed for calculating from starting point to destination；
Preferably, the S302 includes：
S3021. distance running time needed for calculating from starting point to destination；
The section r issued according to internetijSpeedEstimate user by section rijRoute time Tij
For：
All link travel times that then user passes through from starting point to destination are：
S3022. charging station monitoring operation system is utilized to obtain the information such as electric vehicle quantity；
Electric automobile charging station monitoring management system monitors charging station charger total quantity, charger currently in use in real time
Quantity, into essential informations such as the electric vehicle quantity of queue, obtain the EV number of units point that moment t and t-1 reach charging station
It is not expressed asMoment t and t-1 are receiving the EV number of units of charging serviceCalculate moment t charging station j
User's average arrival rate and charging equipment average service rate, i.e.,
The vehicle average latency is：
In formula:SjFor the charging pile number of charging station j configuration,
Automobile user travels the whole charging station queue waiting times accessed to destination from initial point：
It calculates from starting point to charging stand-by period, the charging time of each charging station of destination approach；
Preferably, when the charging time refers to electric vehicle and reaches that be charged to state-of-charge be 80% from remaining capacity after charging station j
The estimated value taken time.
In formula, E0For electric vehicle rated capacity (kWh),Electric vehicle remaining capacity when to reach charging station j,For the charge capacity at charging station j, PeFor the charge power (kW) of charging station fast charge charger.
Total charging time of the electric vehicle from starting point to destination be：
S303. charging cost needed for calculating from starting point to destination；
Preferably, the S303 calculates the charging Costco Wholesale of electric vehicle, including:
S3031. filling in real time from starting point to each charging station of destination approach is obtained using charging station monitoring operation system
Electric electricity price information；
S3032. the charging cost from starting point to each charging station of destination approach is calculated.
Charging Costco Wholesale includes that charging electricity price and charging service take.The real time charging issued according to charging station operation system
Price price (t).
Quick charge cost of the electric vehicle in charging station j be
In formula, price (t) is the function (member/degree) that electric vehicle charging price and charging service price change over time.
tjThe time (h) when charging station j is reached for electric vehicle.
Total quick charge cost of the electric vehicle from starting point to destination be：
The S4 includes the following steps：Use ant group algorithm with battery dump energy for constraint, trip distance, travel time
And it is goal programming electric vehicle charge path that the sum of charging cost three is optimal.
Pass through the remaining capacity after each section in the process of moving according to the electric vehicle calculated in S2.Electric vehicle exists
When starting point, remaining capacity Estart, then remaining capacity E when reaching node jjIt calculates as follows：
EjMeet：Ej≥0,j∈La∪Sa, indicate the road-net node and charging tiny node electric vehicle for random access
Remaining capacity cannot be less than zero.
Remaining capacity E when reaching node jjTo constrain, the totle drilling cost described in step S3 is target, and reference is shown in fig. 5
Ant group algorithm flow chart solves the problem, obtains the charge path that optimal solution is planned.
According to further aspect of the application, the present embodiment provides a kind of electric vehicle charge path device for planning, such as Fig. 6
Shown, which includes：
First computing module obtains all kinds of driving cycles of electric vehicle, calculates electric vehicle under all kinds of driving cycles
Average energy consumption value；
Second computing module obtains travel route, and it is remaining to calculate dynamic of the electric vehicle in each section of traveling passage in transit
Electricity；
Third computing module calculates user's trip distance, travel time and charging cost according to travel route；
Path planning module determines that electric vehicle fills according to the result of calculation of above-mentioned first, second, and third computing module
Power path.
Further, first computing module includes：
Parameter acquiring unit, choosing can reflect that the driving cycle of electric automobile air conditioner opening and closing and driver's driving performance is special
Levy parameter；
Preferably, it chooses and quotes more characteristic parameter in the prior art, including：Maximum speed, averagely adds average speed
Speed, peak acceleration accelerate ratio, deceleration ratio, at the uniform velocity eight ratio, idling ratio characteristic parameters to be used for driving cycle
Classification.In the present embodiment, each feature be retrieved as it is multigroup specifically calculated, parameter is after filtering shown in reference table 1- tables 3.
Wherein, table 1 is maximum speed, average speed, average acceleration and peak acceleration related data, and 2 acceleration ratios of table subtract
Speed ratio example, at the uniform velocity ratio and idling ratio related data, table 3 be environment temperature, operation of air conditioner power, acceleration standard deviation and
Rate of acceleration change standard deviation related data；Further, in this embodiment follow-up calculating be all made of above-mentioned each table and provided
Data.
Secondly, environment temperature can run vehicle and have an impact, two selection environment temperature, operation of air conditioner power feature ginsengs
Number is classified for driving cycle.
Finally, it is special as different driving in reflection driving cycle that acceleration standard deviation, rate of acceleration change standard deviation are chosen
The index of property.
Table 1
Table 2
Table 3
First analytic unit analyzes the phase between the driving cycle characteristic parameter and power consumption selected by parameter acquiring unit
Correlation between closing property and each characteristic parameter is strong and weak；
Electric vehicle history driving cycle data are studied, it is preferable that before charging to next time after this charging
Driving process be known as a cycle, in each cycle, electric vehicle is often travelled to 1km point for a traveling segment, to drawing
The traveling segment divided is analyzed, and above-mentioned 12 characteristic parameters and the power consumption under each segment are calculated.Using Spearman order
Related coefficient calculates the correlation of 12 characteristic parameters and electric vehicle energy consumption, shown in calculation formula such as formula (1),
According to result of calculation, the characteristic parameter variable with correlation minimum between power consumption is deleted.
Second analytic unit, according to the analysis result of the first analytic unit, using principal component analytical method to driving cycle
Characteristic parameter carries out Dimension Reduction Analysis；
The segment comprising 11 driving cycle characteristic parameters is analyzed using principal component analytical method, obtain it is each it is main at
Point characteristic value and contribution rate, according to the principle of principal component analysis it is found that choose accumulation contribution rate it is main more than 80% n at
Divide it is considered that the information that reflection primal variable is included enough, it is weaker to choose this n principal component mainly reflects and correlation
M driving cycle characteristic parameter be used for clustering.
Energy consumption calculation unit, using based on Fuzzy C-Means Clustering Algorithm to the history driving cycle data of electric vehicle into
Row classification；Calculate average energy consumption value of the electric vehicle under all kinds of driving cycles.
Clustering is carried out to the sample data comprising m driving cycle characteristic parameter, according to the cluster centre of each operating mode,
According to apart from minimum principle, determine that the generic of each history driving cycle segment of electric vehicle, distance calculation formula are
dq=| | x-cq| | q=1,2 ..., 12 (2)
Wherein, x=[x1, x2..., x8] be certain segment characteristic parameter, cq=[cq1, cq2..., cq12] it is cluster q
Cluster centre parameter.Take the average value of each segment energy consumption in judged same class driving cycle as electronic under the operating mode
The energy consumption of automobile.
Further, second computing module includes：
Real time running operating mode acquiring unit obtains the real time running operating mode in each section using internet；
Energy consumption calculation unit calculates electric vehicle in the real time energy consumption for travelling each section of passage in transit；
Remaining capacity computing unit is calculated according to the real time energy consumption that energy consumption calculation unit calculates by surplus after the section
Remaining electricity.
Preferably, vehicle running section RijDistance be rij, utilize the section real time running road of internet publication
Condition calculates the cluster centre in the section, judges the real time running operating mode classification in the section for gq, corresponding every kilometer of the section
Energy consumption is Eq, then it is by the consumed electricity in the section
Eij=Eq×rij (3)
Further, the third computing module includes：
First computing unit calculates the trip distance cost in each section from starting point to destination；
Preferably, user's total distance during going to destination includes that electric vehicle passes through charging station S from starting point O
Arrive at the stroke total distance of D.Then stroke distances cost is：
In formula, xijFor 0-1 path decision variables, if section r of the electric vehicle between road-net node i and jij, then xij
=1, otherwise xij=0.LaIndicate the road-net node set that electric vehicle passes through, SaIndicate the charging station section that electric vehicle passes through
Point set.
Second computing unit, calculate from starting point to destination needed for journey time cost；
Preferably, it is described from starting point to destination needed for journey time cost include：
S3021. distance running time needed for calculating from starting point to destination；
The section r issued according to internetijSpeedEstimate user by section rijRoute time Tij
For：
All link travel times that then user passes through from starting point to destination are：
S3022. it utilizes charging station monitoring operation system to obtain the information such as electric vehicle quantity, calculates from starting point to purpose
Charging stand-by period, the charging time of each charging station of ground approach；
Electric automobile charging station monitoring management system monitors charging station charger total quantity, charger currently in use in real time
Quantity, into essential informations such as the electric vehicle quantity of queue, obtain the EV number of units point that moment t and t-1 reach charging station
It is not expressed asMoment t and t-1 are receiving the EV number of units of charging serviceCalculate moment t charging station j
User's average arrival rate and charging equipment average service rate, i.e.,
The vehicle average latency is：
In formula:SjFor the charging pile number of charging station j configuration,
Automobile user travels the whole charging station queue waiting times accessed to destination from initial point：
Preferably, when the charging time refers to electric vehicle and reaches that be charged to state-of-charge be 80% from remaining capacity after charging station j
The estimated value taken time.
In formula, E0For electric vehicle rated capacity (kWh),Electric vehicle remaining capacity when to reach charging station j,For the charge capacity at charging station j, PeFor the charge power (kW) of charging station fast charge charger.
Total charging time of the electric vehicle from starting point to destination be：
Third computing unit, calculate from starting point to destination needed for charging cost；
Preferably, it is described calculating from starting point to destination needed for charging cost include：
S3031. filling in real time from starting point to each charging station of destination approach is obtained using charging station monitoring operation system
Electric electricity price information；
S3032. the charging cost from starting point to each charging station of destination approach is calculated.
Charging Costco Wholesale includes that charging electricity price and charging service take.The real time charging issued according to charging station operation system
Price price (t).
Quick charge cost of the electric vehicle in charging station j be
In formula, price (t) is the function (member/degree) that electric vehicle charging price and charging service price change over time.
tjThe time (h) when charging station j is reached for electric vehicle.
Total quick charge cost of the electric vehicle from starting point to destination be：
The path planning module obtains the result of calculation of above-mentioned first, second, and third computing module, is calculated using ant colony
Method is constraint with battery dump energy, and optimal the sum of trip distance, travel time and charging cost three are that goal programming is electronic
Automobile charge path.
Use ant group algorithm with battery dump energy for constraint, the sum of trip distance, travel time and charging cost three
Optimal is goal programming electric vehicle charge path.
Pass through the remaining capacity after each section in the process of moving according to the electric vehicle calculated in S2.Electric vehicle exists
When starting point, remaining capacity Estart, then remaining capacity E when reaching node jjIt calculates as follows：
EjMeet：Ej≥0,j∈La∪Sa, indicate the road-net node and charging tiny node electric vehicle for random access
Remaining capacity cannot be less than zero.
Remaining capacity E when reaching node jjTo constrain, the totle drilling cost described in step S3 is target, and reference is shown in fig. 6
Ant group algorithm flow chart solves the problem, obtains the charge path that optimal solution is planned.
Obviously, the above embodiment is merely an example for clearly illustrating the present invention by the application, and is not pair
The restriction of presently filed embodiment may be used also on the basis of the above description for those of ordinary skill in the art
To make other variations or changes in different ways, all embodiments can not be exhaustive here, it is every to belong to this Shen
Row of the obvious changes or variations that technical solution please is extended out still in protection scope of the present invention.
Claims (10)
1. a kind of electric vehicle charge path planing method, which is characterized in that including：
S1. all kinds of driving cycles of electric vehicle are obtained, average energy consumption value of the electric vehicle under all kinds of driving cycles is calculated；
S2. travel route is obtained, calculates electric vehicle in the dynamic remaining capacity for travelling each section of passage in transit；
S3. user's trip distance, travel time and charging cost are calculated according to travel route；
S4. electric vehicle charge path is determined according to the result of calculation of above-mentioned steps S1-S3.
2. a kind of electric vehicle charge path planing method according to claim 1, which is characterized in that the S1 include with
Lower step：
S101. the driving cycle characteristic parameter of electric automobile air conditioner opening and closing and driver's driving performance can be reflected by choosing；
S102. the phase between correlation and each characteristic parameter between selected driving cycle characteristic parameter and power consumption is analyzed
Closing property is strong and weak；
S103. principal component analytical method is used to carry out Dimension Reduction Analysis to driving cycle characteristic parameter；
S104. it using being classified to the history driving cycle data of electric vehicle based on Fuzzy C-Means Clustering Algorithm, calculates
Average energy consumption value of the electric vehicle under all kinds of driving cycles.
3. a kind of electric vehicle charge path planing method according to claim 2, which is characterized in that the S2 include with
Lower step：
S201. internet is utilized to obtain the real time running operating mode in each section；
S202. electric vehicle is calculated in the real time energy consumption in each section of traveling passage in transit, is calculated by the residue after the section
Electricity.
4. according to a kind of any electric vehicle charge path planing methods of claim 1-3, which is characterized in that the S3
Include the following steps：
S301. the trip distance cost in each section from starting point to destination is calculated；
S302. journey time cost needed for calculating from starting point to destination；
S303. charging cost needed for calculating from starting point to destination；
Preferably, the S302 includes：
S3021. distance running time needed for calculating from starting point to destination；
S3022. it utilizes charging station monitoring operation system to obtain electric vehicle quantity information, calculates from starting point to destination approach
Charging stand-by period, the charging time of each charging station；
Preferably, the S303 includes:
S3031. charging station monitoring operation system is utilized to obtain from starting point to the real time charging of each charging station of destination approach electricity
Valence information；
S3032. the charging cost from starting point to each charging station of destination approach is calculated.
5. according to a kind of any electric vehicle charge path planing methods of claim 1-4, which is characterized in that the S4
Include the following steps：Use ant group algorithm with battery dump energy for constraint, trip distance, travel time and charging cost three
The sum of it is optimal be goal programming electric vehicle charge path.
6. a kind of electric vehicle charge path device for planning, which is characterized in that described device includes：
First computing module obtains all kinds of driving cycles of electric vehicle, calculates electric vehicle being averaged under all kinds of driving cycles
Power consumption values；
Second computing module obtains travel route, calculates electric vehicle in the dynamic remaining capacity for travelling each section of passage in transit；
Third computing module calculates user's trip distance, travel time and charging cost according to travel route；
Path planning module determines electric vehicle charging circuit according to the result of calculation of above-mentioned first, second, and third computing module
Diameter.
7. a kind of electric vehicle charge path device for planning according to claim 6, which is characterized in that described first calculates
Module includes：
Parameter acquiring unit chooses the driving cycle feature ginseng that can reflect electric automobile air conditioner opening and closing and driver's driving performance
Number；
First analytic unit analyzes the correlation between the driving cycle characteristic parameter and power consumption selected by parameter acquiring unit
And the correlation between each characteristic parameter is strong and weak；
Second analytic unit, according to the analysis result of the first analytic unit, using principal component analytical method to driving cycle feature
Parameter carries out Dimension Reduction Analysis；
Energy consumption calculation unit divides the history driving cycle data of electric vehicle using based on Fuzzy C-Means Clustering Algorithm
Class；Calculate average energy consumption value of the electric vehicle under all kinds of driving cycles.
8. according to a kind of any electric vehicle charge path device for planning of claim 6-7, which is characterized in that described the
Two computing modules include：
Real time running operating mode acquiring unit obtains the real time running operating mode in each section using internet；
Energy consumption calculation unit calculates electric vehicle in the real time energy consumption for travelling each section of passage in transit；
Remaining capacity computing unit is calculated according to the real time energy consumption that energy consumption calculation unit calculates by the residue electricity after the section
Amount.
9. according to a kind of any electric vehicle charge path device for planning of claim 6-8, which is characterized in that described the
Three computing modules include：
First computing unit calculates the trip distance cost in each section from starting point to destination；
Second computing unit, calculate from starting point to destination needed for journey time cost；
Third computing unit, calculate from starting point to destination needed for charging cost；
Preferably, it is described from starting point to destination needed for journey time cost include：
S3021. distance running time needed for calculating from starting point to destination；
S3022. it utilizes charging station monitoring operation system to obtain electric vehicle quantity information, calculates from starting point to destination approach
Charging stand-by period, the charging time of each charging station；
Preferably, it is described calculating from starting point to destination needed for charging cost include：
S3031. charging station monitoring operation system is utilized to obtain from starting point to the real time charging of each charging station of destination approach electricity
Valence information；
S3032. the charging cost from starting point to each charging station of destination approach is calculated.
10. according to a kind of any electric vehicle charge path device for planning of claim 6-9, which is characterized in that described
Path planning module obtains the result of calculation of above-mentioned first, second, and third computing module, uses ant group algorithm with remaining battery
Electricity is constraint, and optimal the sum of trip distance, travel time and charging cost three are goal programming electric vehicle charge path.
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