CN106599390B - It is a kind of meter and electric taxi space-time stochastic behaviour charging load calculation method - Google Patents
It is a kind of meter and electric taxi space-time stochastic behaviour charging load calculation method Download PDFInfo
- Publication number
- CN106599390B CN106599390B CN201611046903.9A CN201611046903A CN106599390B CN 106599390 B CN106599390 B CN 106599390B CN 201611046903 A CN201611046903 A CN 201611046903A CN 106599390 B CN106599390 B CN 106599390B
- Authority
- CN
- China
- Prior art keywords
- charging
- indicate
- soc
- station
- moment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 25
- 238000000342 Monte Carlo simulation Methods 0.000 claims abstract description 4
- 230000005611 electricity Effects 0.000 claims description 38
- 238000000034 method Methods 0.000 claims description 21
- 238000009826 distribution Methods 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000004088 simulation Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 238000012512 characterization method Methods 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims description 3
- 230000000977 initiatory effect Effects 0.000 claims description 3
- 235000012054 meals Nutrition 0.000 claims description 3
- 230000002123 temporal effect Effects 0.000 claims description 3
- 238000012614 Monte-Carlo sampling Methods 0.000 claims description 2
- 230000000903 blocking effect Effects 0.000 claims 1
- 238000005070 sampling Methods 0.000 abstract 1
- 230000006399 behavior Effects 0.000 description 25
- 238000010586 diagram Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 3
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses the calculation methods of a kind of meter and the charging load of electric taxi space-time stochastic behaviour, specifically comprise the following steps: that (1) by carrying out grid dividing to urban road network, determines that the seat that State Grid Corporation of China is runed in the city fills the/position of electrical changing station within a grid；(2) traffic network of the gridding divided according to step (1), constructed respectively based on Monte Carlo simulation the running model of electric taxi, the traveling destination of electric taxi and path Choice Model, electric taxi fill/change electric behavior model and fill/electrical changing station charges carry calculation model；(3) the EV time-space behavior that the model that step (2) are established carries out sampling based on Monte Carlo in the urban road network that step (1) divides is simulated, obtain in one day respectively fill/EV of electrical changing station charges load.The present invention considers the space-time stochastic behaviour factor of electric taxi, so that calculated result is closer to actual conditions.
Description
Technical field
The present invention relates to electric automobile load specificity analysis field, more particularly to a kind of meter and electric taxi space-time with
The calculation method of the charging load of machine characteristic.
Background technique
With the continuous development of electric car (electric vehicle, EV), EV will become Future Power System most
One of load of characteristic.The charging part throttle characteristics and user behavior of EV is closely related, have biggish spatio-temporal distribution with
Machine, prediction difficulty are larger.For EV at present still in the stage is popularized, corresponding charging service network is also unsound, therefore point
The factors such as operation mode in the geographical location of electrically-charging equipment, charging service network must be taken into consideration in the charging behavior of analysis EV.Public
Field of traffic, EV generally take quick charge or change power mode.
Summary of the invention
In view of the deficiencies of the prior art, it is an object of the present invention to provide a kind of meter and electric taxi space-time stochastic behaviours
Charge load calculation method, this method consider the space-time stochastic behaviour of electric taxi, electrically-charging equipment geographical location with
And the factors such as operation mode of charging service network, so that calculated result is closer to actual conditions.
In order to achieve the above object, the technical solution adopted in the present invention is as follows: it is a kind of meter and electric taxi space-time with
The calculation method of the charging load of machine characteristic, specifically comprises the following steps:
(1) by carrying out grid dividing to urban road network, the seat that State Grid Corporation of China is runed in the city is determined
Fill/the position of electrical changing station within a grid；
(2) traffic network of the gridding divided according to step (1), electronic taxi is constructed based on Monte Carlo simulation respectively
The running model of vehicle, the traveling destination of electric taxi and path Choice Model, electric taxi fill/change electric behavior model
And fill/electrical changing station charging carry calculation model；
(3) model that step (2) are established is carried out taking out based on Monte Carlo in the urban road network that step (1) divides
The EV time-space behavior of sample is simulated, and is obtained in one day and is respectively filled the/EV charging load of electrical changing station.
Further, the step (1) specifically:
The position of the urban area traffic zone Nei Ge is determined using Meshing Method, it for ease of calculation, can be by city
City region is divided into several equal-sized grids, and traffic network model is with R=(WN,WR, A, C) and characterization, wherein, WNIt indicates to hand over
Logical center of housing estate node set；WRIt indicates equivalent road section set, is the directed line segment table of endpoint to two central nodes
Show；A indicates road attribute, including category of roads, two-way street or one-way road attribute；C indicates road traffic congestion index；
After grid dividing is good, so that it is determined that the seat that State Grid Corporation of China is runed in the city fill/electrical changing station within a grid
Position.
Further, the step (2) specifically:
(2.1) traffic network of the gridding divided according to step (1), establishes the running model of electric taxi, specifically
It is as follows:
The travel speed of (2.1.1) electric car
In view of urban road is mainly that north-south and East and West direction are distributed in length and breadth, thus, it is supposed that occurring in EV driving process
When change in location, only travelled by present node to its adjacent node.The travel speed v of EVu(t) it is calculated by formula (2)-(3)
It obtains:
In formula:WithRespectively indicate travel speed when the u EV carrying of t moment and zero load；It indicates to carry
The maximum travelling speed of EV when objective, generally urban road provide speed limit；The maximum travelling speed of EV when indicating unloaded, with
Driver, which cruises, looks for the psychological related of visitor；Indicate t moment central node giThe traffic congestion index at place, numerical value can pass through city
Traffic congestion index real-time monitoring platform statistics in city's obtains；Indicate the vehicle driving speed under corresponding traffic congestion index
Degree can be obtained by tabling look-up.
The spatial and temporal distributions of (2.1.2) electric car
Traveling of the EV in traffic zone must can just cause the variation of geographical coordinate across grid, that is, meet formula (5)
Condition, when EV within the Δ t period the four corners of the world either direction traveling distance between r/2 and 3r/2 when, abscissa or
Ordinate changes a unit.
LEV,u(t)=(xu(t),yu(t)) (4)
In formula: the distance between the side length and adjacent center node of each traffic zone grid is r；LEV,u(t) when indicating t
Carve the geographical coordinate of the u EV；xu(t) and yu(t) horizontal, axial coordinate value is respectively indicated；Δ t indicates time interval.
The vehicle flowrate of each traffic zone, i.e., the EV quantity at each moment central node can be counted according to EV geographical coordinate.
The traffic zone spatial distribution matrix N (t) of EV quantity is indicated with mathematical formulae are as follows:
In formula: N (t) is nx,y(t) matrix form；nx,y(t) quantity of t moment EV at central node (x, y) is indicated；
X and Y respectively indicates maximum cross, the ordinate value of traffic zone；NEVIndicate the total quantity of EV.Formula (7) indicates to work as EV current geographic
When coordinate is consistent with traffic zone central node geographical coordinate,Value is 1, and otherwise value is 0.
(2.1.3) single stroke operating range
Distribution function f (d) Rayleigh distributed of EV single stroke operating range indicates trip probability with operating range elder generation
Reduce after increase, meet the characteristic of the short-distance trip of taxi general satisfaction, indicates are as follows:
In formula: d indicates EV single stroke operating range；σdIndicate the rayleigh distributed being fitted by practical investigational data
Parameter.
(2.2) traffic network of the gridding divided according to step (1), establishes the traveling destination and road of electric taxi
Diameter preference pattern, specific as follows:
The destination (2.2.1) selection
There are three kinds of states by each EV, that is, travel paths, charge path or path of cruising are in, before travel paths indicate EV
Toward passenger point (pick-up points, PUPs) and the destination of the passenger (drop-off points, DOPs)；Charge path indicates
EV, which is gone to, fills/electrical changing station；The path representation EV that cruises is in zero load and looks for objective state.By the survey data of taxi behavior,
Passenger's trip characteristics meet:
In formula: giIndicate the geographical coordinate of traffic zone central node；WithT moment passenger is respectively indicated in gi
The probability that point gets on the bus and gets off；T indicates emulation cycle.Formula (1) indicates that passengers quantity of getting on or off the bus in urban area is kept in one day
Perseverance does not consider the case where carrying out Intercity Transportation of calling a taxi.
The initial geographical coordinate of EV is in WNIt is randomly choosed in a central node；On travel paths, charge path or road of cruising
It is constantly converted in three kinds of states of diameter.When EV carrying, destination, that is, the destination of the passenger；When EV zero load, needed if having and filling/change electricity
It asks, then select to fill/electrical changing station is destination；If without filling/changing electricity demanding, driver is according to being currently located node and adjacent 4 nodes
Passenger go on a journey generate probabilityThe maximum node of select probability is destination；If using present node as destination, then next
Moment is in suspended state.After destination determines, EV is according to traffic information at that time, in the shortest all paths of space length
Select the shortest paths of time gap；Space shortest path is found out by dijkstra's algorithm, according to shortest time path diameter
The time-consuming the smallest path of stroke is calculated in traffic congestion index.
(2.2.2) optimal route selection
Fill/change electricity demanding when EV is in light condition and has, just using fill/electrical changing station is has passenger in the driving process of destination
Transmission is called a taxi order, is not considered to enter the station under passenger carrying status and is filled/change the situation of electricity, EV need to judge that remaining battery power whether can at this time
After having picked passenger ,/electrical changing station progress electric energy supply is filled for EV arrival.If there is low battery stagnation of movement risk, then refuse order, and
Still using fill/electrical changing station as destination advance；If then carrying passenger without stagnation of movement risk, and changing destination is the destination of the passenger, In
Complete passenger's stroke after, then using fill/electrical changing station be destination advance.
The longest mileage travelled d of EVmax,uIt may be expressed as:
dmax,u=(Socfull-Soce)Qbattery,u/wu (9)
In formula: Qbattery,uIndicate the battery capacity of the u EV；wuIndicate average hundred kilometers of power consumption of the u EV；
SocfullExpression is completely filledSocValue, generally 100%；SoceIndicate the Soc value of generation stagnation of movement risk.
(2.3) traffic network of the gridding divided according to step (1), that establishes electric taxi fills/changes electric behavior mould
Type, specific as follows:
(2.3.1) fills/changes power mode selection
In order to compare influence of the different operation modes to EV behavior and charging load, in an emulation cycle T, all EV
It can only select to charge or change electric one of electric energy supply mode.
(2.3.2) fills/changes electricity demanding judgement
Practical finding is run according to taxi, changing shifts and period meal time distinguish Normal Distribution:
tu,start~N (mt,st 2) (10)
Under charge mode, defining EV and generating the Soc threshold value of charge requirement in time window is Socw.It is another to define Soca(meet
Soca<Socw), no matter in the case where charging or changing power mode, when the Soc of EV is less than Soc at any timea, can all generate charging or change
Electricity demanding.
(2.3.3) remaining battery power calculates
In charging mode, EV battery dump energy and day operating range and the relationship of time are as follows:
In formula: Q0,uAnd Qr,u(t) EV initial cells electricity and t moment remaining battery power are respectively indicated；tu,startAnd tu endIt respectively indicates initiation of charge moment of the EV in charging station and terminates charging moment；Pf,uIndicate the quick charge function of the u EV
Rate, du(t) it indicates the day to the u EV of t moment to accumulate operating range.Formula (11) describe EV in the process of moving with charged
Battery dump energy in journey under two kinds of scenes.
(2.4)/electrical changing station charging carry calculation model is filled in the traffic network of the gridding divided according to step (1), foundation,
It is specific as follows:
(2.4.1) charging station charging carry calculation
Smaller value in duration needed for charging duration of the EV in charging station takes time window duration and battery trickle charge, can indicate
Are as follows:
tu,end=tu,start+tu,ct (13)
In formula: tu,ctIndicate charging duration；tu,wIndicate time window duration；hcIndicate charge efficiency.
Soc need to meet bound constraint in EV charging process:
In formula: Socu(t) Soc of the u EV of t moment is indicated；SocexpIndicate expectation Soc when EV leaves charging station.
The charging load of charging station is the sum of the electric car charging load to charge in the station, can be by formula (15)-
(16) it calculates and acquires:
In formula: PFCS(t) N is indicatedFCSThe charging load of a quick charge station；Indicate k-th of charging station of t moment
Charge load；NEV,kIndicate that t moment is in the EV quantity of charged state in k-th of charging station；nx,y(t) it indicates in quick charge
EV quantity where standing in traffic zone；suIt (t) is to fill/change electricity condition target variable, su(t) value is that 0 expression EV is not needed
Fill/change electricity, be worth for 1 expression EV needs fill/change electricity；Indicate that t moment is filled having in traffic zone where quick charge station
The EV quantity of electricity demanding.
(2.4.2) electrical changing station charging carry calculation
The charging load of electrical changing station is the sum of the charging load of battery to charge in the station, can be by formula (17)-(18)
It is calculated:
In formula: PBSS(t) N is indicatedBSSThe charging load of a electrical changing station；Indicate the charging of k-th of electrical changing station of t moment
Load；PsIndicate the specified charge power of charger in electrical changing station；WithRespectively indicate t the and t-1 moment
The number of batteries for needing to charge；Indicate the fully charged number of batteries of t moment；nBIt indicates one and changes electric-type EV vehicle mounted electric
Pond quantity.
Beneficial effects of the present invention are as follows: by carrying out grid dividing to urban road network, it is determined that in urban area
Each traffic zone, the position of electrically-charging equipment and specific road information, to establish a more true electronic taxi
Vehicle charging scenarios；According to the urban transportation network after grid dividing, the electronic taxi based on Monte Carlo sampled analog of proposition
Vehicle time-space behavior simulation model can the stochastic behaviour of true simulation electric taxi charging behavior over time and space, make
Finally be calculated fill/the electric taxi of electrical changing station charging load closing to reality situation.
Accompanying drawing content
Fig. 1 is traffic network grid dividing schematic diagram；
Fig. 2 is the electric car Behavior modeling flow chart sampled based on Monte Carlo；
Fig. 3 is the typical day road traffic congestion index map in Hangzhou；
Fig. 4 is Hangzhou main city zone grid dividing and fills/electrical changing station site figure；
Fig. 5 is to fill/electrical changing station service range division figure based on Voronoi diagram；
Fig. 6 is the charging load diagram of electric taxi under charge mode；
Fig. 7 is the charging load diagram for changing electric taxi under power mode；
Fig. 8 is the day of each charging station under charge mode to service EV quantity figure；
Fig. 9 is the day service EV quantity figure for changing each electrical changing station under power mode.
Specific embodiment
Below with reference to embodiment and attached drawing, the present invention is further illustrated.
As shown in Fig. 2, the present invention provide it is a kind of meter and electric taxi space-time stochastic behaviour charging load calculating side
Method specifically comprises the following steps:
(1) by carrying out grid dividing to urban road network, the seat that State Grid Corporation of China is runed in the city is determined
/ the position of electrical changing station within a grid is filled, as shown in Figure 1；
(2) traffic network of the gridding divided according to step (1), electronic taxi is constructed based on Monte Carlo simulation respectively
The running model of vehicle, the traveling destination of electric taxi and path Choice Model, electric taxi fill/change electric behavior model
And fill/electrical changing station charging carry calculation model；
(3) model that step (2) are established is carried out taking out based on Monte Carlo in the urban road network that step (1) divides
The EV time-space behavior of sample is simulated, and is obtained in one day and is respectively filled the/EV charging load of electrical changing station.
Further, the step (1) specifically:
As shown in Figure 1, the square that each fine line is surrounded is a traffic zone, with corresponding central node table
Show.The position of the urban area traffic zone Nei Ge is determined using Meshing Method, it for ease of calculation, can be by urban area
Several equal-sized grids are divided into, traffic network model is with R=(WN,WR, A, C) and characterization, wherein, WNIndicate traffic zone
Central node set；WRIt indicates equivalent road section set, is indicated to the directed line segment that two central nodes are endpoint；A table
Show road attribute, including category of roads, two-way street or one-way road attribute；C indicates road traffic congestion index；
After grid dividing is good, so that it is determined that the seat that State Grid Corporation of China is runed in the city fill/electrical changing station within a grid
Position.
Further, the step (2) specifically:
(2.1) traffic network of the gridding divided according to step (1), establishes the running model of electric taxi, specifically
It is as follows:
The travel speed of (2.1.1) electric car
In view of urban road is mainly that north-south and East and West direction are distributed in length and breadth, thus, it is supposed that occurring in EV driving process
When change in location, only travelled by present node to its adjacent node.The travel speed v of EVu(t) it is calculated by formula (2)-(3)
It obtains:
In formula:WithRespectively indicate travel speed when the u EV carrying of t moment and zero load；Indicate carrying
When EV maximum travelling speed, generally urban road provide speed limit；The maximum travelling speed of EV when indicating unloaded, with department
Machine, which is cruised, looks for the psychological related of visitor；Indicate t moment central node giThe traffic congestion index at place, numerical value can pass through city
Traffic congestion index real-time monitoring platform statistics obtains；Indicate the Vehicle Speed under corresponding traffic congestion index,
It can be obtained by tabling look-up.
The spatial and temporal distributions of (2.1.2) electric car
Traveling of the EV in traffic zone must can just cause the variation of geographical coordinate across grid, that is, meet formula (5)
Condition, when EV within the Δ t period the four corners of the world either direction traveling distance between r/2 and 3r/2 when, abscissa or
Ordinate changes a unit.
LEV,u(t)=(xu(t),yu(t)) (4)
In formula: the distance between the side length and adjacent center node of each traffic zone grid is r；LEV,u(t) when indicating t
Carve the geographical coordinate of the u EV；xu(t) and yu(t) horizontal, axial coordinate value is respectively indicated；Δ t indicates time interval.
The vehicle flowrate of each traffic zone, i.e., the EV quantity at each moment central node can be counted according to EV geographical coordinate.
The traffic zone spatial distribution matrix N (t) of EV quantity is indicated with mathematical formulae are as follows:
In formula: N (t) is nx,y(t) matrix form；nx,y(t) quantity of t moment EV at central node (x, y) is indicated；
X and Y respectively indicates maximum cross, the ordinate value of traffic zone；NEVIndicate the total quantity of EV.Formula (7) indicates to work as EV current geographic
When coordinate is consistent with traffic zone central node geographical coordinate, 1LEV, u (t)=(x, y)Value is 1, and otherwise value is 0.
(2.1.3) single stroke operating range
Distribution function f (d) Rayleigh distributed of EV single stroke operating range indicates trip probability with operating range elder generation
Reduce after increase, meet the characteristic of the short-distance trip of taxi general satisfaction, indicates are as follows:
In formula: d indicates EV single stroke operating range；σdIndicate the rayleigh distributed being fitted by practical investigational data
Parameter.
(2.2) traffic network of the gridding divided according to step (1), establishes the traveling destination and road of electric taxi
Diameter preference pattern, specific as follows:
The destination (2.2.1) selection
There are three kinds of states by each EV, that is, travel paths, charge path or path of cruising are in, before travel paths indicate EV
Toward passenger point (pick-up points, PUPs) and the destination of the passenger (drop-off points, DOPs)；Charge path indicates
EV, which is gone to, fills/electrical changing station；The path representation EV that cruises is in zero load and looks for objective state.By the survey data of taxi behavior,
Passenger's trip characteristics meet:
In formula: giIndicate the geographical coordinate of traffic zone central node；WithT moment passenger is respectively indicated in gi
The probability that point gets on the bus and gets off；T indicates emulation cycle.Formula (1) indicates that passengers quantity of getting on or off the bus in urban area is kept in one day
Perseverance does not consider the case where carrying out Intercity Transportation of calling a taxi.
The initial geographical coordinate of EV is in WNIt is randomly choosed in a central node；On travel paths, charge path or road of cruising
It is constantly converted in three kinds of states of diameter.When EV carrying, destination, that is, the destination of the passenger；When EV zero load, needed if having and filling/change electricity
It asks, then select to fill/electrical changing station is destination；If without filling/changing electricity demanding, driver is according to being currently located node and adjacent 4 nodes
Passenger go on a journey generate probabilityThe maximum node of select probability is destination；If using present node as destination, then next
Moment is in suspended state.After destination determines, EV is according to traffic information at that time, in the shortest all paths of space length
Select the shortest paths of time gap；Space shortest path is found out by dijkstra's algorithm, according to shortest time path diameter
The time-consuming the smallest path of stroke is calculated in traffic congestion index.
(2.2.2) optimal route selection
Fill/change electricity demanding when EV is in light condition and has, just using fill/electrical changing station is has passenger in the driving process of destination
Transmission is called a taxi order, is not considered to enter the station under passenger carrying status and is filled/change the situation of electricity, EV need to judge that remaining battery power whether can at this time
After having picked passenger ,/electrical changing station progress electric energy supply is filled for EV arrival.If there is low battery stagnation of movement risk, then refuse order, and
Still using fill/electrical changing station as destination advance；If then carrying passenger without stagnation of movement risk, and changing destination is the destination of the passenger, In
Complete passenger's stroke after, then using fill/electrical changing station be destination advance.
The longest mileage travelled d of EVmax,uIt may be expressed as:
dmax,u=(Socfull-Soce)Qbattery,u/wu (9)
In formula: Qbattery,uIndicate the battery capacity of the u EV；wuIndicate average hundred kilometers of power consumption of the u EV；
SocfullExpression is completely filledSocValue, generally 100%；SoceIndicate the Soc value of generation stagnation of movement risk.
(2.3) traffic network of the gridding divided according to step (1), that establishes electric taxi fills/changes electric behavior mould
Type, specific as follows:
(2.3.1) fills/changes power mode selection
In order to compare influence of the different operation modes to EV behavior and charging load, in an emulation cycle T, all EV
It can only select to charge or change electric one of electric energy supply mode.
(2.3.2) fills/changes electricity demanding judgement
Practical finding is run according to taxi, changing shifts and period meal time distinguish Normal Distribution:
tu,start~N (mt,st 2) (10)
Under charge mode, defining EV and generating the Soc threshold value of charge requirement in time window is Socw.It is another to define Soca(meet
Soca<Socw), no matter in the case where charging or changing power mode, when the Soc of EV is less than Soc at any timea, can all generate charging or change
Electricity demanding.
(2.3.3) remaining battery power calculates
In charging mode, EV battery dump energy and day operating range and the relationship of time are as follows:
In formula: Q0,uAnd Qr,u(t) EV initial cells electricity and t moment remaining battery power are respectively indicated；tu,startAnd tu endIt respectively indicates initiation of charge moment of the EV in charging station and terminates charging moment；Pf,uIndicate the quick charge function of the u EV
Rate, du(t) it indicates the day to the u EV of t moment to accumulate operating range.Formula (11) describe EV in the process of moving with charged
Battery dump energy in journey under two kinds of scenes.
(2.4)/electrical changing station charging carry calculation model is filled in the traffic network of the gridding divided according to step (1), foundation,
It is specific as follows:
(2.4.1) charging station charging carry calculation
Smaller value in duration needed for charging duration of the EV in charging station takes time window duration and battery trickle charge, can indicate
Are as follows:
tu,end=tu,start+tu,ct (13)
In formula: tu,ctIndicate charging duration；tu,wIndicate time window duration；hcIndicate charge efficiency.
Soc need to meet bound constraint in EV charging process:
Socexp,u￡ Socu(t) ￡ Socfull (14)
In formula: Socu(t) Soc of the u EV of t moment is indicated；SocexpIndicate expectation Soc when EV leaves charging station.
The charging load of charging station is the sum of the electric car charging load to charge in the station, can be by formula (15)-
(16) it calculates and acquires:
In formula: PFCS(t) N is indicatedFCSThe charging load of a quick charge station；Indicate k-th of charging station of t moment
Charge load；NEV,kIndicate that t moment is in the EV quantity of charged state in k-th of charging station；nx,y(t) it indicates in quick charge
EV quantity where standing in traffic zone；suIt (t) is to fill/change electricity condition target variable, su(t) value is that 0 expression EV is not needed
Fill/change electricity, be worth for 1 expression EV needs fill/change electricity；Indicate that t moment is filled having in traffic zone where quick charge station
The EV quantity of electricity demanding.
(2.4.2) electrical changing station charging carry calculation
The charging load of electrical changing station is the sum of the charging load of battery to charge in the station, can be by formula (17)-(18)
It is calculated:
In formula: PBSS(t) N is indicatedBSSThe charging load of a electrical changing station；Indicate the charging of k-th of electrical changing station of t moment
Load；PsIndicate the specified charge power of charger in electrical changing station；WithRespectively indicate t the and t-1 moment
The number of batteries for needing to charge；Indicate the fully charged number of batteries of t moment；nBIt indicates one and changes electric-type EV vehicle mounted electric
Pond quantity.
In conjunction with the running model of the electric taxi of foundation, the traveling destination of electric taxi and path Choice Model,
/ electrical changing station charging carry calculation model and is filled electric behavior model in filling/changing for electric taxi, is equally divided into 1440 for one day
Period, was minimum period unit with 1 minute, and the EV time-space behavior simulation process based on Monte Carlo sampling is as shown in Figure 2.In conjunction with
Simulation to relevant passenger's travel behaviour can calculate to acquire in one day and respectively fill the/EV of electrical changing station charging load.
Embodiment:
Parameter setting: electric-type taxi and the rechargeable taxi of BYD e6 are changed with many Tai Langyue of Hangzhou trial operation
For parameter, see Table 1 for details for design parameter.Hangzhou cabbie population about 8000 at present consider EV permeability for 25% (i.e.
2000) under charging load.Per 100 km of the average daily mileage travelled of taxi generally in 350~500km, EV driving process
Power consumption is 20kWh.3 Soc threshold values are set separately are as follows: Soce=10%, Soca=40%, Socw=70%.σdValue is
4.4659.Morning changing shifts period (T1), lunch dining period (T2) and period (T3) three periods of changing shifts at dusk obey respectively
Normal distribution N (4.5,12),N(12.5,12) and N (16.5,12).The traffic congestion index and correspondence of traffic zone central node
Vehicle Speed can by Hangzhou traffic congestion index real-time monitoring platform inquire obtain, Hangzhou typical case day traffic gather around
Stifled index is as shown in Figure 3.WithIt is respectively set to 50km/h and 25km/h.Initialize initial position and the starting of each EV
Soc, the position of 0 moment each EV and starting Soc are as emulation initial value after selecting model continuous simulation 3 days.
1 electric car parameter of table
Table 2 fills/electrical changing station traffic zone coordinate information
It is public with national grid in the region for Hangzhou main city zone (by around city high speed and Qiantang River area encompassed)
Take charge of runed 17 fill/site of electrical changing station is used as and fills/the position of electrical changing station in model, labeled as ☆.As shown in figure 4, fixed
One 38 × 42 grid of justice, establishes coordinate system, each rounded coordinate o'clock is as traffic zone (i.e. unit grids)
Central node, the area of unit grids are 0.5km*0.5km.Fig. 5 shows the geographical coordinate figure after gridding, each dark circles
Point represents an effective traffic zone central node, according to Voronoi diagram widely applied in GIS-Geographic Information System to filling/change
The coverage in power station is divided, and obtains each filling the/geographical service area of electrical changing station.Table 2 give respectively fill/electrical changing station exists
Coordinate information in gridding map.
Relevant rudimentary information and parameter are inputted, charge mode can be obtained and changes total charging load curve difference of EV under power mode
As shown in Figure 6 and Figure 7.Comparing Fig. 6 and Fig. 7 is can be found that, the fluctuation of charging load is relatively changed big under power mode under charge mode,
This is mainly due to charge power under charge mode is larger, the single EV charging time is shorter, to be further exacerbated by taxi
The randomness bring of charging behavior influences.Under charge mode, each EV days bulk charge 2.17 times, charging load is changed shifts at two
There is apparent peak in time (i.e. morning 4-5 point, 16-17 point in afternoon) and lunchtime (11-12 point).In addition, go on a journey sooner or later
After peak, also some EV has electric energy to feed demand.One day charging load peak appears in the lunchtime, this explanation is big
EV is measured after the traveling in a morning, Soc value will be less than Socw, driver will utilize the lunchtime carry out quick charge.It changes
Under power mode, since battery capacity is smaller, charge requirement can be frequently generated in peak traffic phase EV, model emulation is every as the result is shown
EV daily changes electricity 4.22 times.After morning peak, electricity is changed in a large amount of EV selections, so as to cause in morning 8-9 point appearance one day
Charge load peak.In addition, also resulted in after trip requirements biggish time point EV replacement number of batteries increase to
Influence charging load curve.
Fig. 8 and Fig. 9, which respectively illustrates in one day charge mode and changes, each under power mode fills/electrical changing station day service EV number
Amount.Table 3 and table 4 then respectively illustrate in one day filled under two kinds of charging operation modes/maximum quantity of the EV of electrical changing station service and
At the time of corresponding.Compare Fig. 8 and Fig. 9 as can be seen, respectively fill/electrical changing station day quantity of service opposite tendency only with its locating for
Manage position relevant information it is related, such as with nearby fill/change at a distance from electric facility, the geographic range of service, and with specifically fill
The operation mode that electricity still changes electricity is substantially unrelated.But since the continual mileage for changing electric-type EV is about the half of rechargeable EV, often
EV is daily changed twice that electric number is about charging times, so as to cause the difference of the Sino-Japan service EV quantity of figure.
It should be noted that, it is recognized herein that the service ability of charging station and electrical changing station is to be able to satisfy the filling of EV/change electricity demanding,
I.e. there is no wait in line to fill/electrical changing station phenomenon.Purpose is in order to which under no site capacity restrict, analysis EV charging is negative
The spatial distribution of lotus, so that the following charging and conversion electric facility be instructed to layout planning.In table 4, working as if changing the vehicle-mounted battery pack of electric-type EV
Preceding moment underfill electricity will be included in the maximum access quantity amount of subsequent time.Comparison sheet 3 and table 4 are as can be seen that day maximum access quantity
Amount is influenced at the time of appearance by operation mode, because operation mode will affect the traveling behavior of EV.The number of more each website
According to, it can be found that, the Hangzhou main city zone west of a city, the south of a city layout it is very few, cause No. 1, No. 2, No. 17 website days service EV quantity and
Maximum access quantity amount is more, and future fills/changes electric facility it is contemplated that increasing as needed or increase newly in region in existing website
Fill/electrical changing station point.
Each charging station of table 3 accesses the maximum quantity of EV and corresponds to the time
Each electrical changing station of table 4 services the maximum quantity of EV and corresponds to the time
Claims (2)
1. a kind of calculation method of the charging load of meter and electric taxi space-time stochastic behaviour, which is characterized in that specifically include
Following steps:
(1) by carrying out grid dividing to urban road network, determine that the seat that State Grid Corporation of China is runed in the city is filled/changed
The position of power station within a grid；
(2) traffic network of the gridding divided according to step (1), constructs electric taxi based on Monte Carlo simulation respectively
Running model, electric taxi traveling destination and path Choice Model, electric taxi fill/change electric behavior model and
Fill/electrical changing station charging carry calculation model；The step specifically:
(2.1) traffic network of the gridding divided according to step (1), establishes the running model of electric taxi, specific as follows:
The travel speed of (2.1.1) electric car
In view of urban road is mainly that north-south and East and West direction are distributed in length and breadth, thus, it is supposed that position occurs in EV driving process
When variation, only travelled by present node to its adjacent node；The travel speed v of EVu(t) it is calculated by formula (2)-(3):
In formula:WithRespectively indicate travel speed when the u EV carrying of t moment and zero load；When indicating carrying
The maximum travelling speed of EV；The maximum travelling speed of EV when indicating unloaded, cruising with driver, it is related to look for the psychology of visitor；
Indicate t moment central node giThe traffic congestion index at place, numerical value can pass through urban traffic blocking index real-time monitoring platform
Statistics obtains；It indicates the Vehicle Speed under corresponding traffic congestion index, can be obtained by tabling look-up；
The spatial and temporal distributions of (2.1.2) electric car
Traveling of the EV in traffic zone must can just cause the variation of geographical coordinate across grid, that is, meet the condition of formula (5),
When EV within the Δ t period when the distance of four corners of the world either direction traveling is between r/2 and 3r/2, abscissa or ordinate
Change a unit；
LEV,u(t)=(xu(t),yu(t)) (4)
In formula: the distance between the side length and adjacent center node of each traffic zone grid is r；LEV,u(t) t moment u is indicated
The geographical coordinate of EV；xu(t) and yu(t) horizontal, axial coordinate value is respectively indicated；Δ t indicates time interval；
The vehicle flowrate of each traffic zone, i.e., the EV quantity at each moment central node can be counted according to EV geographical coordinate；EV number
The traffic zone spatial distribution matrix N (t) of amount is indicated with mathematical formulae are as follows:
In formula: N (t) is nx,y(t) matrix form；nx,y(t) quantity of t moment EV at central node (x, y) is indicated；X and Y
Respectively indicate maximum cross, the ordinate value of traffic zone；NEVIndicate the total quantity of EV；Formula (7) indicates to work as EV current geographic coordinate
When consistent with traffic zone central node geographical coordinate,Value is 1, and otherwise value is 0；
(2.1.3) single stroke operating range
Distribution function f (d) Rayleigh distributed of EV single stroke operating range indicates that trip probability first increases with operating range
After reduce, meet the characteristic of the short-distance trip of taxi general satisfaction, indicate are as follows:
In formula: d indicates EV single stroke operating range；σdIndicate the rayleigh distributed parameter being fitted by practical investigational data；
(2.2) traffic network of the gridding divided according to step (1), the traveling destination and path for establishing electric taxi are selected
Model is selected, specific as follows:
The destination (2.2.1) selection
There are three kinds of states by each EV, that is, are in travel paths, charge path or path of cruising, and travel paths indicate that EV goes to load
Objective point (pick-up points, PUPs) and the destination of the passenger (drop-off points, DOPs)；Before charge path indicates EV
It is past to fill/electrical changing station；The path representation EV that cruises is in zero load and looks for objective state；By the survey data of taxi behavior, passenger
Trip characteristics meet:
In formula: giIndicate the geographical coordinate of traffic zone central node；WithT moment passenger is respectively indicated in giPoint on
Vehicle and the probability got off；T indicates emulation cycle；Formula (1) indicates passengers quantity conservation of getting on or off the bus in urban area in one day, i.e.,
Do not consider the case where carrying out Intercity Transportation of calling a taxi；
The initial geographical coordinate of EV is in ΩNIt is randomly choosed in a central node；In travel paths, charge path or path three of cruising
It is constantly converted in kind state；When EV carrying, destination, that is, the destination of the passenger；When EV zero load, electricity demanding is filled/changes if having, then
Select to fill/electrical changing station is destination；If driver is according to the passenger for being currently located node and adjacent 4 nodes without filling/changing electricity demanding
Trip generates probabilityThe maximum node of select probability is destination；If using present node as destination, then at subsequent time
In suspended state；After destination determines, EV is according to traffic information at that time, when selecting in the shortest all paths of space length
Between the shortest paths of distance；Space shortest path is found out by dijkstra's algorithm, and shortest time path diameter is to be gathered around according to traffic
The time-consuming the smallest path of stroke is calculated in stifled index；
(2.2.2) optimal route selection
Fill/change electricity demanding when EV is in light condition and has, just using fill/electrical changing station is has passenger's transmission in the driving process of destination
It calls a taxi order, does not consider to enter the station under passenger carrying status and fill/change the situation of electricity, EV need to judge whether remaining battery power can connect at this time
After having sent passenger ,/electrical changing station progress electric energy supply is filled for EV arrival；If there is low battery stagnation of movement risk, then refuse order, and still with
Fill/electrical changing station be destination advance；If then carrying passenger without stagnation of movement risk, and changing destination is the destination of the passenger, is completed
After passenger's stroke, then using fill/electrical changing station as destination advance；
The longest mileage travelled d of EVmax,uIt may be expressed as:
dmax,u=(Socfull-Soce)Qbattery,u/wu (9)
In formula: Qbattery,uIndicate the battery capacity of the u EV；wuIndicate average hundred kilometers of power consumption of the u EV；SocfullTable
Show and completely fillsSocValue, generally 100%；SoceIndicate the Soc value of generation stagnation of movement risk；
(2.3) traffic network of the gridding divided according to step (1), that establishes electric taxi fills/changes electric behavior model, tool
Body is as follows:
(2.3.1) fills/changes power mode selection
In order to compare influence of the different operation modes to EV behavior and charging load, in an emulation cycle T, all EV can only
Electric one of electric energy supply mode is changed in selection charging；
(2.3.2) fills/changes electricity demanding judgement
Practical finding is run according to taxi, changing shifts and period meal time distinguish Normal Distribution:
tu,start~N (μt,σt 2) (10)
Under charge mode, defining EV and generating the Soc threshold value of charge requirement in time window is Socw；It is another to define Soca, meet Soca
<Socw, no matter in the case where charging or changing power mode, when the Soc of EV is less than Soc at any timea, can all generate charging or change electricity needs
It asks；
(2.3.3) remaining battery power calculates
In charging mode, EV battery dump energy and day operating range and the relationship of time are as follows:
In formula: Q0,uAnd Qr,u(t) EV initial cells electricity and t moment remaining battery power are respectively indicated；tu,startAnd tuendRespectively
Indicate initiation of charge moment and end charging moment of the EV in charging station；Pf,uIndicate the quick charge power of the u EV, du
(t) it indicates the day to the u EV of t moment to accumulate operating range；Formula (11) describes EV in the process of moving and in charging process
Battery dump energy under two kinds of scenes；
(2.4)/electrical changing station charging carry calculation model is filled in the traffic network of the gridding divided according to step (1), foundation, specifically
It is as follows:
(2.4.1) charging station charging carry calculation
Smaller value in duration needed for charging duration of the EV in charging station takes time window duration and battery trickle charge, may be expressed as:
tu,end=tu,start+tu,ct (13)
In formula: tu,ctIndicate charging duration；tu,wIndicate time window duration；ηcIndicate charge efficiency；
Soc need to meet bound constraint in EV charging process:
Socexp,u≤Socu(t)≤Socfull (14)
In formula: Socu(t) Soc of the u EV of t moment is indicated；SocexpIndicate expectation Soc when EV leaves charging station；
The charging load of charging station is the sum of the electric car charging load to charge in the station, can be counted by formula (15)-(16)
It acquires:
In formula: PFCS(t) N is indicatedFCSThe charging load of a quick charge station；Indicate the charging of k-th of charging station of t moment
Load；NEV,kIndicate that t moment is in the EV quantity of charged state in k-th of charging station；nx,y(t) it indicates in quick charge station institute
EV quantity in traffic zone；suIt (t) is to fill/change electricity condition target variable, su(t) value is that 0 expression EV does not need to fill/change
Electricity, be worth for 1 expression EV needs fill/change electricity；Indicate that t moment is having charge requirement in traffic zone where quick charge station
EV quantity；
(2.4.2) electrical changing station charging carry calculation
The charging load of electrical changing station is the sum of the charging load of battery to charge in the station, can be calculated by formula (17)-(18)
It obtains:
In formula: PBSS(t) N is indicatedBSSThe charging load of a electrical changing station；Indicate the charging load of k-th of electrical changing station of t moment；
PsIndicate the specified charge power of charger in electrical changing station；WithRespectively indicate t and t-1 moment needs
The number of batteries of charging；Indicate the fully charged number of batteries of t moment；nBIt indicates one and changes electric-type EV on-vehicle battery number
Amount；
(3) model that step (2) are established is carried out in the urban road network that step (1) divides based on Monte Carlo sampling
The simulation of EV time-space behavior obtains in one day and respectively fills the/EV of electrical changing station charging load.
2. the calculation method of the charging load of meter according to claim 1 and electric taxi space-time stochastic behaviour, special
Sign is, the step (1) specifically:
The position of the urban area traffic zone Nei Ge is determined using Meshing Method, it for ease of calculation, can be by metropolitan district
Domain is divided into several equal-sized grids, and traffic network model is with R=(ΩN,ΩR, A, C) and characterization, wherein, ΩNIndicate traffic
Center of housing estate node set；ΩRIt indicates equivalent road section set, is the directed line segment table of endpoint to two central nodes
Show；A indicates road attribute, including category of roads, two-way street or one-way road attribute；C indicates road traffic congestion index；
After grid dividing is good, so that it is determined that the seat that State Grid Corporation of China is runed in the city fills the/position of electrical changing station within a grid
It sets.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611046903.9A CN106599390B (en) | 2016-11-23 | 2016-11-23 | It is a kind of meter and electric taxi space-time stochastic behaviour charging load calculation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611046903.9A CN106599390B (en) | 2016-11-23 | 2016-11-23 | It is a kind of meter and electric taxi space-time stochastic behaviour charging load calculation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106599390A CN106599390A (en) | 2017-04-26 |
| CN106599390B true CN106599390B (en) | 2019-11-22 |
Family
ID=58592075
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611046903.9A Expired - Fee Related CN106599390B (en) | 2016-11-23 | 2016-11-23 | It is a kind of meter and electric taxi space-time stochastic behaviour charging load calculation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106599390B (en) |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107403273B (en) * | 2017-07-28 | 2020-11-27 | 中铁程科技有限责任公司 | Information processing method, device and non-transitory computer readable storage medium |
| CN107392400B (en) * | 2017-09-04 | 2020-10-16 | 重庆大学 | EV charging load space-time distribution prediction method considering real-time traffic and temperature |
| CN107704963A (en) * | 2017-10-13 | 2018-02-16 | 国电南瑞科技股份有限公司 | A kind of charging electric vehicle load forecasting method for considering spatial and temporal distributions |
| CN108510128B (en) * | 2018-04-11 | 2022-04-15 | 华南理工大学广州学院 | Method for predicting space-time distribution of charging load of regional electric automobile |
| CN109034648B (en) * | 2018-08-13 | 2021-03-19 | 华南理工大学广州学院 | Electric vehicle cluster demand response potential evaluation method |
| CN109190935B (en) * | 2018-08-15 | 2021-08-17 | 天津大学 | Highway charging station planning method considering service area and vehicle accident |
| CN109347130B (en) * | 2018-08-17 | 2020-12-01 | 杭州电子科技大学 | Electric automobile accommodation scale estimation method considering electricity price and static voltage stability |
| CN109521449B (en) * | 2019-01-23 | 2022-10-28 | 吉林大学 | Method for selecting cruising destination based on taxi GPS track |
| CN110466384A (en) * | 2019-07-05 | 2019-11-19 | 武汉新能源汽车工业技术研究院有限公司 | A kind of charging module group power distribution method and device |
| CN110598985A (en) * | 2019-08-09 | 2019-12-20 | 广州智湾科技有限公司 | Vehicle path planning method |
| CN111242403B (en) * | 2019-11-08 | 2023-05-05 | 武汉旌胜科技有限公司 | Charging load prediction method, device equipment and storage medium for charging station |
| CN113326594B (en) * | 2021-05-28 | 2023-08-01 | 南京工程学院 | Electric vehicle battery replacement station and power grid interaction method and system based on microscopic traffic simulation |
| CN113386770B (en) * | 2021-06-10 | 2024-03-26 | 武汉理工大学 | Charging station data sharing-based dynamic planning method for charging path of electric vehicle |
| CN113343358B (en) * | 2021-06-25 | 2023-04-18 | 国网山西省电力公司经济技术研究院 | Electric vehicle charging load space-time distribution modeling method considering road information |
| CN114626762B (en) * | 2022-04-28 | 2023-04-07 | 北京建筑大学 | Mobile battery replacement network address selection method, battery scheduling method, device and system |
| CN116822912B (en) * | 2023-08-30 | 2023-11-24 | 北京玖行智研交通科技有限公司 | Intelligent dispatching method and device for electric vehicle trunk line long-distance transportation charging and changing |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103241130A (en) * | 2013-04-10 | 2013-08-14 | 华中科技大学 | Energy management method and system for electric bus charging and swap station |
| CN104680256A (en) * | 2015-03-06 | 2015-06-03 | 北京交通大学 | Method and device for optimizing charging load of electric vehicle |
| CN105260807A (en) * | 2015-11-30 | 2016-01-20 | 江苏省电力公司苏州供电公司 | Electric automobile charging load estimation method considering random characteristics |
| CN105825434A (en) * | 2015-11-25 | 2016-08-03 | 国家电网公司 | Charging load calculation method for electric vehicles |
-
2016
- 2016-11-23 CN CN201611046903.9A patent/CN106599390B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103241130A (en) * | 2013-04-10 | 2013-08-14 | 华中科技大学 | Energy management method and system for electric bus charging and swap station |
| CN104680256A (en) * | 2015-03-06 | 2015-06-03 | 北京交通大学 | Method and device for optimizing charging load of electric vehicle |
| CN105825434A (en) * | 2015-11-25 | 2016-08-03 | 国家电网公司 | Charging load calculation method for electric vehicles |
| CN105260807A (en) * | 2015-11-30 | 2016-01-20 | 江苏省电力公司苏州供电公司 | Electric automobile charging load estimation method considering random characteristics |
Non-Patent Citations (2)
| Title |
|---|
| 电动汽车负荷特性及其与电力***交互的研究;杨俊;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20150715(第7期);正文第15-27页 * |
| 计及路上成本的换电站布点效率分析;于强强 等;《电力***自动化》;20140625;第38卷(第12期);第81-87页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106599390A (en) | 2017-04-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106599390B (en) | It is a kind of meter and electric taxi space-time stochastic behaviour charging load calculation method | |
| CN109034648B (en) | Electric vehicle cluster demand response potential evaluation method | |
| CN107067110B (en) | Electric vehicle charging load space-time prediction method under vehicle-road-network mode | |
| CN107323300B (en) | A kind of-electric car reservation charging method of vehicle conjunctive model of being stood based on road- | |
| CN110880054B (en) | Planning method for electric network car-booking charging and battery-swapping path | |
| CN107392400A (en) | Meter and real-time traffic and the EV of temperature charging load spatial and temporal distributions Forecasting Methodology | |
| CN109711630A (en) | A kind of electric car fast charge station addressing constant volume method based on trip probability matrix | |
| CN109447410A (en) | A kind of charging station site selecting method considering urban road network traffic operating status | |
| CN108932561B (en) | Electric vehicle charging path selection method considering nonlinear charging function | |
| CN107392336A (en) | Distributed electric automobile charging dispatching method based on reservation in intelligent transportation | |
| CN111160639B (en) | Electric vehicle charging load prediction method based on user travel time-space distribution characteristics | |
| CN108053058A (en) | A kind of electric taxi charging pile site selecting method based on big data | |
| CN106532686A (en) | Area power distribution network load prediction method based on electric automobile charging apparatus | |
| Franca | Electricity consumption and battery lifespan estimation for transit electric buses: drivetrain simulations and electrochemical modelling | |
| CN110232219A (en) | A kind of schedulable capacity ratification method of electric car based on data mining | |
| CN113085655A (en) | Vehicle-mounted electric automobile comprehensive service system | |
| CN111915150A (en) | Electric public transportation system planning method | |
| CN108133329B (en) | Electric automobile travel and charging demand analysis method considering charging feedback effect | |
| Liao et al. | Load modeling for electric taxi battery charging and swapping stations: Comparison studies | |
| Falco et al. | Agent-based modelling to evaluate the impact of plug-in electric vehicles on distribution systems | |
| Mahmoudi et al. | Spatial-temporal prediction of electric vehicle charging demand in realistic urban transportation system of a mid-sized city in brazil | |
| Shrestha et al. | Spatial-temporal stochasticity of electric vehicles in an integrated traffic and power system | |
| Zhang et al. | Charging time and location recommendation strategy considering taxi user satisfaction | |
| CN114840984A (en) | Electric vehicle charging load space-time prediction method considering travel path decision | |
| Shi et al. | The economic impact of electric vehicle routing and charging strategy on traffic-power integrated networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20191122 Termination date: 20201123 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |