CN108528233A - The multifactor charging method of electric vehicle of energy information is considered in a kind of intelligent transportation - Google Patents
The multifactor charging method of electric vehicle of energy information is considered in a kind of intelligent transportation Download PDFInfo
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- CN108528233A CN108528233A CN201810165809.8A CN201810165809A CN108528233A CN 108528233 A CN108528233 A CN 108528233A CN 201810165809 A CN201810165809 A CN 201810165809A CN 108528233 A CN108528233 A CN 108528233A
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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
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
-
- 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
- B60L2240/70—Interactions with external data bases, e.g. traffic centres
- B60L2240/72—Charging station selection relying on external data
-
- 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
-
- 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
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- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention discloses the multifactor charging methods of electric vehicle that energy information is considered in a kind of intelligent transportation, belong to electric vehicle charging schedule field；Its step are as follows, step 1：The RSU into its communication radius sends inquiry request acquisition charging station status information to vehicle in the process of moving.Step 2：RSU receives the charging station status information that its caching is issued after the inquiry request of electric vehicle.Step 3：After each correlative factor for obtaining charging station selection by the above method, optimal charging station is selected according to multifactor optimal charging station selection strategy.Step 4：Decide whether using the information newly obtained to update its reservation during vehicle node drives towards selected charging station.It proposes based on the optimal charging station selection method under the Multiple factors such as stroke duration, energy and distance., there is certain practical significance in the problems such as to alleviating mileage anxiety.
Description
Technical field
The present invention relates to electric vehicle charging schedule fields, and in particular to the electricity of energy information is considered in a kind of intelligent transportation
The multifactor charging method of electrical automobile.
Background technology
In recent years, due to the growing life requirement of people, more and more motor vehicles appear in the horse in each city
Road center, they bring the facility of life to the mankind, while also bringing great challenge to the living environment of the earth.Very much
Big and medium-sized cities are shrouded by suffocative haze, and unprecedented injury is brought to human body.Electric vehicle is as cleaning energy
Source automobile by more and more governments and it is personal praise highly, however since can not to meet electric vehicle long for the development of battery science and technology
The demand of time traveling, electric vehicle needs repeatedly charge in stroke.Charging is required for the consumption regular hour every time.In order to
Shorten the charging time, promotion drives to experience, and charging system needs to formulate charging method for each electric vehicle.
In view of the fast development of intelligent transportation, the motor vehicle in traveling can pass through drive test unit to access network, that is, electric vehicle
It is communicated by V2R, the information of vehicles of magnanimity can be obtained, improve the efficiency of information transmission.This patent is communicated with pull-mode
On the basis of, reduce the probability that charging station formulates erroneous decision because the potential stand-by period is ignored.
Invention content
The purpose of the present invention is to provide the multifactor charging sides of electric vehicle that energy information is considered in a kind of intelligent transportation
Method, mileage anxiety for solving electric vehicle and the utilization rate for improving existing charging station.By mobile electric automobile in V2R and
The opportunistic communication of roadside unit carries out distribution and the electric vehicle charging reservation of real time charging station information.Using charging station can
Realize that stroke is held with charging time prediction, the prediction of electric vehicle stand-by period, stroke Duration Prediction and electric energy incremental forecasting
Continuous time, energy and three aspect of distance carry out multifactor charging station selection method.
The multifactor charging method of electric vehicle of energy information is considered in a kind of intelligent transportation, which is characterized in that realize step
It is rapid as follows：
Step 1:Electric vehicle is travelled along pre-determined route, and is detected and be whether there is RSU in its communication radius, if logical
RSU is inquired in letter radius and thens follow the steps two, otherwise executes step 3；
Step 2：RSU into its communication radius sends inquiry request and obtains charging station status information；And predict electronic vapour
Electric vehicle obtains the probability of information when vehicle and RSU are communicated；When electric vehicle and first RSU are communicated, electric vehicle
The probability of acquisition is Pf, such as formula (1)；
When electric vehicle leaves first RSU, when driving towards second RSU, electric vehicle obtains when being communicated with second RSU
The probability of information is Pab, such as formula (2)；
RSU in R-PULL mechanism has caching function, as by more RSU, electric vehicle obtains information
Probability can be stepped up, therefore, RSU and electric vehicle communication when acquisition informational probability filled depending on whether RSU is cached with
The information in power station；With the increase of running time, electric vehicle has more high probability and obtains information, because RSU has more machines
Charging station status information can be cached；By concluding above-mentioned two formula, when electric vehicle and i-th of RSU communication, obtain
Winning the confidence the probability of breath can be with PabProbability increase, such as formula (3)；
Step 3：Judge whether vehicle state of charge is less than charge threshold, if it is, executing step 4, otherwise returns
Step 1；
Step 4：Judge whether to obtain charge station information from RSU, if it is, step 5 is executed, it is no to then follow the steps
Seven；
Step 5：It is chosen whether by available charging station selection mechanism in the presence of charging station can be used, is to then follow the steps six, it is no
Then follow the steps seven；
Step 6：Before being estimated respectively by stand-by period algorithm for estimating and stroke duration and electric energy increase algorithm for estimating
Stroke duration after charging toward each charging station and electric energy increment；After calculating each correlative factor for obtaining charging station selection, root
Optimal charging station is selected according to multifactor optimal charging station selection method, and reservation letter is sent to selected charging station by RSU
It ceases and assesses it and preengage stability；Then step 8 is executed；
Step 7：Electric vehicle recommends nearest charging station according to record；Then step 8 is executed；
Step 8：Selected charging station is driven towards, and judges whether that charge station information is met and obtained with RSU, if it is, holding
Row step 5, otherwise executes step 9；
Step 9：Judge whether to reach selected charging station, return to step eight if not reaching, if reaching institute
Charging station is selected, then completes and terminates.
Advantage of the invention is that considering the multifactor charging side of electric vehicle of energy information in proposing a kind of intelligent transportation
Method, the information exchange for realizing electric vehicle and charging station, the energy information based on charging station are communicated using V2R, and comprehensive charging is adjusted
The many factors charging schedule method of degree, wherein mainly include R-PULL patterns under information distribution and booking-mechanism and based on mostly because
The charging selection method of element.Information distribution and booking-mechanism realize information distribution and the electric vehicle of charging station under R-PULL patterns
Charging reservation.In based on multifactor charging selection method, propose multiple based on stroke duration, energy and distance etc.
Optimal charging station selection method under factor., there is certain practical significance in the problems such as to alleviating mileage anxiety.
Description of the drawings
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is the analogous diagram of Harbin City；
Fig. 3 is the flow chart that feature value vector method selects optimal charging station；
Fig. 4 is the scene simplification figure in two adjacent RSU wireless coverages.
Specific implementation mode
The present invention is described further below in conjunction with the accompanying drawings.
Consider that the multifactor charging method of electric vehicle of energy information, implementation step are as follows in a kind of intelligent transportation：
Step 1：Electric vehicle sends inquiry request in communication range to RSU, predicts electric vehicle and RSU communications
When electric vehicle obtain information probability.Simplification scene graph such as Fig. 4 of acquisition of information probability under the communication overlay of two RSU
It is shown.When electric vehicle and first RSU are communicated, the probability that electric vehicle obtains is Pf, such as formula (1).
When electric vehicle leaves first RSU, when driving towards second RSU, electric vehicle obtains when being communicated with second RSU
The probability of information is Pab, such as formula (2).
RSU in R-PULL mechanism has caching function, as by more RSU, electric vehicle obtains information
Probability can be stepped up, therefore, RSU and electric vehicle communication when acquisition informational probability filled depending on whether RSU is cached with
The information in power station.With the increase of running time, electric vehicle has more high probability and obtains information, because RSU has more machines
Charging station status information can be cached..By concluding above-mentioned two step, when electric vehicle and i-th of RSU communication,
The probability for obtaining information can be with PabProbability increase, such as formula (3).
The acquisition of information probability of R-PULL mechanism can be obtained on this basis.This conclusion is complicated suitable for City scenarios
Road topology and electric vehicle difference movement speed scene.
Step 2：After the inquiry request for receiving electric vehicle, RSU is distributed to the charging station state letter of electric vehicle caching
Breath.When the remaining capacity of electric vehicle reaches the threshold value of full power consumption, system will guide electric vehicle charging.In order to realize pair
The planning of optimal case, system will predict electric vehicle in the stand-by period of each charging station and after going to each charging station to charge
The stroke duration and electric energy increment.
When electric vehicle proceeds by charging planning, charging station is according to the charging of the electric vehicle for having rested on charging station
Situation estimates the available charging time of each charging pile.Systematic survey goes out the electric vehicle number of queues N to chargec,
The electric vehicle number of queues N to be charged such as charging stationw.Before planning, all charging piles are not used by current network.
If planning that the pot life of moment charging pile is network current time Tcur.When electric vehicle goes to charging station to charge, from current
The charging time of energy state to maximum electricity isThe substantially numerical value in charging time, i.e. formula (4) can be predicted out in system
Charging station needs to judge whether electric vehicle can be full of in the retention period.In NcBy each electric vehicle in queue
It is completely filled with the down time of the time and the electric vehicle of electricityIt is compared, such as formula (5).If meeting formula
(5), then battery can be full of by electric vehicle before leaving charging station.System can measure NcVehicle in queue reaches charging station
Afterwards in charging station residence time.System can calculate the charge completion time of electric vehicle in this caseSuch as formula
(6)
If being unsatisfactory for formula (5), i.e. electric vehicle can not be fully charged in the charging station retention period, electric in this case
The charge completion time of electrical automobile is the deadline to chargeThe time limit can be measured by system, such as formula (7)
The charging situation of the electric vehicle of all arrival is weaved into available charging time list by charging station, when there is idle charging
When stake, electric vehicle can charge, such as formula (8)
Similar with the queue situation in charging, system can calculate electric vehicle by formula (9) and formula (10) and wait for team
The charge completion time of row.In each electric vehicle cycle, ACTLIST lists in ascending order, are used simultaneouslyIt substitutes
ACTLIST.GET(0).In the available charging time for predicting each stake, value is input in ACTLIST lists, when the queuing of output
Between charging station each issue interval in as charging station local information publication.When receiving new information from RSU, in value
Old information will be replaced.Electric vehicle carries out charging station selection judgement using these information.According to the information received, electronic vapour
Vehicle can estimate the expection stand-by period of given charging station.
System can calculate the charging stand-by period of each electric vehicle, such as formula (11).
When there is electric vehicle to enter charging station, whether judgement has idle charging pile at this time, if it is not, obtaining has
At the time of idle charging pileThe available charging time of each charging pile is obtained, calculates electric vehicle on this basis in the charging
The stand-by period stood.If the arrival time of electric vehicleThe charging time most can be used soon earlier than what is discharged from charging pile, then
By the way that the charging pot life is expected the charging time with correspondingPhase Calais determines the charging complete moment of the vehicle.Such as
The arrival time of fruit electric vehicle is later than the most fast available charging time discharged from charging pile, passes throughWithIt sums to count
Calculate the charging complete moment.Because charging pile has been released when electric vehicle reaches.It is used for using charge completion time replacement
The most fast pot life of charging, dynamically updates the available charging time of each charging pile, until being directed to the circulate operation inspection
NrAll arrival times in queue.By being predicted using the stand-by period each charging station, electric vehicle obtains all available
The stand-by period of charging station.
Obtain electric vehicle prediction stand-by period after can obtain electric vehicle charging station the estimated stand-by period.Electricity
Electrical automobile calculates its stroke duration by the following method：It measures and drives to taking for selected charging station from current location
Dev,cs, electric vehicle drives to the time-consuming D of destination from charging stationcs,d.The taking of selected charging station (including etc. it is to be charged
The time of time and charging) there are two kinds of situations, a kind of situation is to meetElectric vehicle can when condition
With fully charged, it is in taking for charging stationElectric vehicle is in the charging station residence time in another case
Reach Dev(i)But charging station is left when not completing charging, is D in the charging station residence timeev(i), system will the charging of calculating midway
The total time-consuming that arrives at the destination of electric vehicleFull charge of electric vehicle drives towards destination in selected charging station
Shown in total time-consuming such as formula (12).
In another case, due to Dev(r)Limitation and it is not fully charged when by being calculated, such as formula (13)
By calculating each available charging stationObtain stroke duration charging station list ATDLIST for charging
It stands selection method.
Electric vehicle can calculate in stroke and have a net increase of electricity：Systematic survey goes out electric vehicle and drives to choosing from current location
Fixed charging station power consumption Mev,cs.Start running that the power consumption to destination is M from charging stationev,d, i.e., filled in charging station completion
After electricity, electric vehicle will continue towards the power consumption of destination traveling.Charging station goes to the shortest path of destination, according to electronic
Every meter of energy consumption of automobile can obtain M with path lengthev,dAnd Mev,cs。
Step 3：After system obtains each correlative factor of charging station selection by above-mentioned calculating, according to multifactor optimal
Charging station selection method selects optimal charging station.After obtaining optimal charge station information, electric vehicle is sent to selected charging station
Reservation application.During optimal charging station selects, automobile user faces the restriction in charging time.This feature value vector
Method is as shown in Figure 3.The multifactor tradeoff and selection such as charging station distance and desired charge volume, charging station selection method is according to stroke
The Multiple factors such as duration, electric energy increment and distance determine.Weighted coefficient distribution under multifactor decision making is for charging performance
Also large effect is will produce, system will seek multifactor weight coefficient using feature value vector method.Feature value vector method
The foundation of the middle judgment matrix importance between factor two-by-two is compared to each other, and it is as shown in table 1 to compare scale.Judgment matrix,
Wherein WiIndicate the weight coefficient of i-th of factor.pijIndicate importance of i-th of factor compared to j-th of factor.Optimal charging
It stands and select to be selected based on the decision of three factors, shown in the three rank judgment matrixs such as formula (14) of foundation.
The meaning of 1 ratio scale of table
System will use following algorithm.For third-order matrix P, acquired using P- λ E=0；Find out eigenvalue λmaxIt is corresponding
Feature vector is W=[W1,W2,W3]T, meet PW=λmax·W；The feature vector acquired is normalized
And then using providing in formula (14) and (15) and table 2
Aver-age Random Consistency Index carries out consistency check, thinks that judgment matrix keeps one if the CR values obtained meet CR ＜ 0.1
Cause property, the weight of distribution is more conform with, if being unsatisfactory for CR ＜ 0.1, needs to be adjusted judgment matrix P, repeats the above step
Suddenly until meeting consistency check；
2 Aver-age Random Consistency Index of table (RI)
After consistency check passes through, shown in finally obtained weight coefficient：
In carrying out charging selection, user can be selected the importance of three factors by the ratio scale that table 1 defines
It selects, obtains the weight coefficient of each factor.Since the dimension of each correlative factor is different, need that it is normalized, it can be with
The value of utility size for obtaining each charging station, selects optimal charging station.System measure vehicle node to selected charging station distance and
Distance to farthest available charging station is respectively Pev(r)And Pmax, by selected charging station charging the stroke duration and selection
The longest journey time of charging station can be usedAnd Dmax, the electric energy increment in stroke and the vehicle maximum battery capacityWithIt can finally obtain the effect value for going to each charging station, the charging station of selection effect value minimum, effect value judgement such as formula
(18)
Step 4：Decide whether to update it using the information newly obtained during vehicle node drives towards selected charging station
Reservation, continues its stroke after charging.It is then repeated the above process for need repeatedly to charge before reaching its destination.
While the above process executes, charging station obtains charging according to local charging vehicle information by available charging time correlation computations
The charging time can be used by standing, while polymerize the vehicle reservation information of this charging station.In each release cycle certainly to all RSU publications
The available charging time of body and vehicle reservation situation.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, any made by repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (1)
1. considering the multifactor charging method of electric vehicle of energy information in a kind of intelligent transportation, which is characterized in that realize step
It is as follows：
Step 1:Electric vehicle is travelled along pre-determined route, and is detected and be whether there is RSU in its communication radius, if in communication half
RSU is inquired in diameter and thens follow the steps two, otherwise executes step 3；
Step 2：RSU into its communication radius sends inquiry request and obtains charging station status information；And predict electric vehicle and
Electric vehicle obtains the probability of information when RSU is communicated；When electric vehicle and first RSU are communicated, electric vehicle obtains
Probability be Pf, such as formula (1)；
When electric vehicle leaves first RSU, when driving towards second RSU, electric vehicle obtains information when being communicated with second RSU
Probability be Pab, such as formula (2)；
RSU in R-PULL mechanism has caching function, as by more RSU, electric vehicle obtains the probability of information
Can be stepped up, therefore, RSU and electric vehicle communication when acquisition informational probability whether be cached with charging station depending on RSU
Information；With the increase of running time, electric vehicle has more high probability and obtains information, because RSU has more chances
Cache charging station status information；By concluding above-mentioned two formula, when electric vehicle and i-th of RSU communication, letter is obtained
The probability of breath can be with PabProbability increase, such as formula (3)；
Step 3：Judge whether vehicle state of charge is less than charge threshold, if it is, executing step 4, otherwise return to step
One；
Step 4：Judge whether to obtain charge station information from RSU, if it is, executing step 5, otherwise executes step 7；
Step 5：It chooses whether, in the presence of charging station can be used, to be to then follow the steps six, otherwise hold by available charging station selection mechanism
Row step 7；
Step 6：Increase algorithm for estimating by stand-by period algorithm for estimating and stroke duration and electric energy to estimate to go to respectively respectively
Stroke duration after charging station charging and electric energy increment；After calculating each correlative factor for obtaining charging station selection, according to more
The optimal charging station selection method of factor selects optimal charging station, and sends reservation information simultaneously to selected charging station by RSU
It assesses it and preengages stability；Then step 8 is executed；
Step 7：Electric vehicle recommends nearest charging station according to record；Then step 8 is executed；
Step 8：Selected charging station is driven towards, and judges whether that charge station information is met and obtained with RSU, if it is, executing step
Rapid five, otherwise execute step 9；
Step 9：Judge whether to reach selected charging station, return to step eight if not reaching, if filled selected by reaching
Power station is then completed and is terminated.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110207717A (en) * | 2019-06-21 | 2019-09-06 | 北京嘀嘀无限科技发展有限公司 | Automobile charging air navigation aid, electronic equipment and storage medium |
| CN110329091A (en) * | 2019-03-09 | 2019-10-15 | 国网电动汽车服务有限公司 | It is a kind of to realize the electric car intelligent charge custom system inserted and filled |
| CN110414750A (en) * | 2019-08-28 | 2019-11-05 | 哈尔滨工程大学 | A kind of electric car real time charging station selection method based on depth enhancing study |
| CN110549896A (en) * | 2019-08-28 | 2019-12-10 | 哈尔滨工程大学 | charging station selection method based on reinforcement learning |
| CN111267667A (en) * | 2020-02-14 | 2020-06-12 | 山东中科先进技术研究院有限公司 | Intelligent charging method and system for electric automobile highway |
| CN112183888A (en) * | 2020-10-26 | 2021-01-05 | 南京明德产业互联网研究院有限公司 | Charging station recommendation method, device and system based on link prediction |
| CN112330203A (en) * | 2020-11-24 | 2021-02-05 | 深圳北航新兴产业技术研究院 | Management method for electric energy supply of pure electric taxi |
| CN115534706A (en) * | 2022-10-17 | 2022-12-30 | 重庆金康赛力斯新能源汽车设计院有限公司 | New energy automobile charging method and device, computer equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105844432A (en) * | 2016-05-01 | 2016-08-10 | 上海大学 | VANET based electric automobile charge scheduling system and method |
| CN106515494A (en) * | 2016-12-02 | 2017-03-22 | 北京邮电大学 | Information obtaining method and system for state of charging station based on road side unit |
| CN107392336A (en) * | 2017-07-17 | 2017-11-24 | 哈尔滨工程大学 | Distributed electric automobile charging dispatching method based on reservation in intelligent transportation |
-
2018
- 2018-02-28 CN CN201810165809.8A patent/CN108528233A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105844432A (en) * | 2016-05-01 | 2016-08-10 | 上海大学 | VANET based electric automobile charge scheduling system and method |
| CN106515494A (en) * | 2016-12-02 | 2017-03-22 | 北京邮电大学 | Information obtaining method and system for state of charging station based on road side unit |
| CN107392336A (en) * | 2017-07-17 | 2017-11-24 | 哈尔滨工程大学 | Distributed electric automobile charging dispatching method based on reservation in intelligent transportation |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110329091B (en) * | 2019-03-09 | 2020-08-04 | 国网电动汽车服务有限公司 | Electric automobile intelligent charging user system capable of realizing plug-and-play charging |
| CN110329091A (en) * | 2019-03-09 | 2019-10-15 | 国网电动汽车服务有限公司 | It is a kind of to realize the electric car intelligent charge custom system inserted and filled |
| CN110207717B (en) * | 2019-06-21 | 2020-12-29 | 北京嘀嘀无限科技发展有限公司 | Automobile charging navigation method, electronic equipment and storage medium |
| CN110207717A (en) * | 2019-06-21 | 2019-09-06 | 北京嘀嘀无限科技发展有限公司 | Automobile charging air navigation aid, electronic equipment and storage medium |
| CN110549896A (en) * | 2019-08-28 | 2019-12-10 | 哈尔滨工程大学 | charging station selection method based on reinforcement learning |
| CN110414750A (en) * | 2019-08-28 | 2019-11-05 | 哈尔滨工程大学 | A kind of electric car real time charging station selection method based on depth enhancing study |
| CN110414750B (en) * | 2019-08-28 | 2022-04-05 | 哈尔滨工程大学 | Electric vehicle real-time charging station selection method based on deep reinforcement learning |
| CN110549896B (en) * | 2019-08-28 | 2022-08-02 | 哈尔滨工程大学 | Charging station selection method based on reinforcement learning |
| CN111267667A (en) * | 2020-02-14 | 2020-06-12 | 山东中科先进技术研究院有限公司 | Intelligent charging method and system for electric automobile highway |
| CN111267667B (en) * | 2020-02-14 | 2021-03-23 | 山东中科先进技术研究院有限公司 | Intelligent charging method and system for electric automobile highway |
| CN112183888A (en) * | 2020-10-26 | 2021-01-05 | 南京明德产业互联网研究院有限公司 | Charging station recommendation method, device and system based on link prediction |
| CN112183888B (en) * | 2020-10-26 | 2024-05-17 | 南京明德产业互联网研究院有限公司 | Method, device and system for recommending charging station based on link prediction |
| CN112330203A (en) * | 2020-11-24 | 2021-02-05 | 深圳北航新兴产业技术研究院 | Management method for electric energy supply of pure electric taxi |
| CN115534706A (en) * | 2022-10-17 | 2022-12-30 | 重庆金康赛力斯新能源汽车设计院有限公司 | New energy automobile charging method and device, computer equipment and storage medium |
| CN115534706B (en) * | 2022-10-17 | 2024-05-14 | 重庆赛力斯新能源汽车设计院有限公司 | New energy automobile charging method and device, computer equipment and storage medium |
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