CN107316097A - Charging electric vehicle needing forecasting method and system - Google Patents
Charging electric vehicle needing forecasting method and system Download PDFInfo
- Publication number
- CN107316097A CN107316097A CN201710351396.8A CN201710351396A CN107316097A CN 107316097 A CN107316097 A CN 107316097A CN 201710351396 A CN201710351396 A CN 201710351396A CN 107316097 A CN107316097 A CN 107316097A
- Authority
- CN
- China
- Prior art keywords
- probability
- electric automobile
- charging
- charged
- service
- 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.)
- Pending
Links
- 238000007600 charging Methods 0.000 title claims abstract description 75
- 238000013277 forecasting method Methods 0.000 title claims abstract description 15
- 238000000034 method Methods 0.000 claims abstract description 16
- 238000004590 computer program Methods 0.000 claims description 14
- 230000006854 communication Effects 0.000 claims description 11
- 238000004891 communication Methods 0.000 claims description 10
- 230000005611 electricity Effects 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 7
- 238000012544 monitoring process Methods 0.000 claims description 5
- 230000004044 response Effects 0.000 abstract description 14
- 241001269238 Data Species 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Educational Administration (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The present invention relates to a kind of charging electric vehicle needing forecasting method, including：For each electric automobile in monitored area, its battery electric quantity, the estimated down time in current location are determined respectively, and its probability charged in current time is determined according to historical data；The charge requirement probability of each electric automobile is calculated respectively, and the charge requirement probability of each electric automobile is ranked up to form the list of potential Rechargeable vehicle.This method can substantially shorten the response time of user's charge request, alleviate the surge pressure of system, so as to provide good Consumer's Experience.
Description
Technical field
The present invention relates to electric vehicle engineering field, more specifically to a kind of charging electric vehicle requirement forecasting side
Method and system.
Background technology
Electric automobile has been gradually gained popularity, it is adaptable to which the charging service of electric automobile is that those skilled in the art's emphasis is closed
Note.
In existing charging service, placed an order in user after (request charging), system passively dispatch service personnel
Or service vehicle goes to provide power-up service, this mode be present：
First, the response time of all services can not be ensured, when attendant is distant from user's vehicle, service can be caused
Response time it is elongated.
2nd, there is surge pressure in system call, in some periods, and possible order compares concentration, so as to influence system entirety
Efficiency of operation；
3rd, same user may place an order in different time sections but obtains various criterion (for example, significant difference of response time)
Service, so as to influence Consumer's Experience.
In addition, for this service mode of movable charging vehicle, if driver (attendant) does not know next service
Order is where, he just do not know charging vehicle leave for where, be parked in where, and can only passively waiting system dispatch so that
Charging resource can not make full use of.
Therefore, in view of drawbacks described above, those skilled in the art need a kind of scheme, predict user's on one's own initiative by system
Potential charge requirement, so that early response user's charge request.
The content of the invention
The technical purpose of the present invention is to provide a kind of charging electric vehicle needing forecasting method, to early response
The charge request of user.
To achieve the above object, a kind of technical scheme of present invention offer is as follows：
A kind of charging electric vehicle needing forecasting method, comprises the following steps：A), for each electronic vapour in monitored area
Car, determines its battery electric quantity, the estimated down time in current location respectively, and determines it when current according to historical data
Between the probability that is charged；B) the charge requirement probability of each electric automobile, is calculated respectively, and to the charge requirement of each electric automobile
Probability is ranked up to form the list of potential Rechargeable vehicle；Wherein, charge requirement probability is electric automobile at least first
The conditional probability charged when part is met.
Preferably, this method also includes：C), each potential Rechargeable vehicle carries out charging institute in respective service point in estimation list
The projected service time needed；D), with each potential Rechargeable vehicle with regard to whether implementing charging service and being communicated.
Preferably, in step b), charge requirement probability is that electric automobile is satisfied by first condition and second condition
The conditional probability of Shi Jinhang chargings, wherein, first condition is less than first threshold, Article 2 for the current battery charge of electric automobile
Part is estimated down time of the electric automobile in current location to exceed Second Threshold.
Preferably, charge requirement probability is calculated according to Bayes law using equation below：P (D)=P (C) * P (S | D) * P
(T | D), wherein, the probability that a certain electric automobile is charged in corresponding current time in P (C) expression historical datas, P (S | D)
Represent historical data in the electric automobile when being charged its corresponding current battery charge less than first threshold probability, P (T |
D the electric automobile probability of its corresponding down time more than Second Threshold when being charged in historical data) is represented.
Preferably, first threshold is the average value of corresponding battery electric quantity when each electric automobile is charged, and Second Threshold is
The average value of each electric automobile corresponding down time when being charged.
Preferably, in step d), if the projected service time meets service standard, active warning user charging；If pre-
Meter service time can not meet service standard, then are informed into user the projected service time, and wait user in very first time window
Instruction, and based on indicate come dispatch service personnel or service vehicle response send instruction potential Rechargeable vehicle charging please
Ask.
Preferably, in step d), according to the sequence of the charge requirement probability of each potential Rechargeable vehicle, it is preferential and actively with
The high potential Rechargeable vehicle of charge requirement probability is communicated.
Preferably, in addition to step e)：If potential Rechargeable vehicle is by communication without receiving charging service, or potential charging
Automobile has completed charging service, then some Rechargeable vehicles of charge requirement probability highest in monitored area is added into potential charging
The list of automobile, and return to step c) and continue executing with.
Invention additionally discloses a kind of charging electric vehicle demand forecast system, including：Area monitoring module, for monitoring section
Each electric automobile in domain, determines its battery electric quantity, the estimated down time in current location respectively, and true according to historical data
The fixed probability that it is charged in current time；Probability evaluation entity, the charge requirement for calculating each electric automobile respectively is general
Rate, and the charge requirement probability of each electric automobile is ranked up to form the list of potential Rechargeable vehicle.
It is each electronic in the charging electric vehicle needing forecasting method that various embodiments of the present invention are provided, active predicting monitored area
The charge requirement probability of automobile, and then communicated with each potential Rechargeable vehicle, asked through user, not even etc. user's request and
Forward scheduling is serviced, and this scheduling mode can substantially shorten the response time of user's charge request, alleviates the surge pressure of system,
So as to provide good Consumer's Experience.
In addition, charging electric vehicle demand forecast system can predict that the potential charging of electric automobile (user) is needed on one's own initiative
Ask, and then can be with early response user's charge request, the system operating efficiency is higher, it is possible to prevente effectively from surge pressure, is adapted to
Promoted in large- and-medium size cities.
Brief description of the drawings
Fig. 1 shows the schematic flow sheet for the charging electric vehicle needing forecasting method that first embodiment of the invention is provided.
Fig. 2 shows the modular structure signal for the charging electric vehicle demand forecast system that second embodiment of the invention is provided
Figure.
Embodiment
As shown in figure 1, first embodiment of the invention provides a kind of charging electric vehicle needing forecasting method, it includes as follows
Multiple steps.
Step S10, the battery electric quantity of each electric automobile in monitored area, estimated down time are determined respectively and current
The probability that time is charged.
Specifically, first, traveling or parked, all registered electric automobiles in monitored area are monitored, obtained
Take the battery electric quantity of each electric automobile, estimated down time；Then, determine each electric automobile in current time according to historical data
The probability charged.
Wherein, battery electric quantity can be characterized using SOC parameter, it is contemplated that down time can be counted according to historical data
Analyze and obtain, can also voluntarily be provided by user.For any electric automobile, historgraphic data recording, for example, history
Upper certain day electric automobile in monitored area the down time of each service point, whether the information such as charge., can according to historical data
To calculate the probability for determining that electric automobile is charged in current time.
Step S12, the charge requirement probability for calculating each electric automobile, and be ranked up to form the row of potential Rechargeable vehicle
Table.
Specifically, in step S12, the charge requirement probability of each electric automobile in monitor area is calculated respectively, and it is right
The charge requirement probability of each electric automobile carries out descending sort to form the list of potential Rechargeable vehicle.
Wherein, charge requirement probability is a kind of conditional probability, for example, entering for electric automobile when at least first condition is met
The conditional probability of row charging.The current battery charge that first condition can be expressed as electric automobile is less than first threshold.
Under preferable case, charge requirement probability is that electric automobile is filled when first condition and second condition are satisfied by
The conditional probability of electricity.Wherein, first condition can be expressed as the current battery charge of electric automobile less than first threshold, Article 2
Part can be expressed as estimated down time of the electric automobile in current location more than Second Threshold.
Charge requirement probability can be calculated as a kind of conditional probability according to Bayes' theorem, and its calculation formula is P
(D)=P (C) * P (S | D) * P (T | D), wherein, P (C) represents that a certain electric automobile is carried out in corresponding current time in historical data
The probability of charging, P (S | D) represents in historical data that the electric automobile its corresponding current battery charge when being charged is less than the
The probability of one threshold value (that is, meeting first condition), and P (T | D) represent the electric automobile its phase when being charged in historical data
The probability of Second Threshold (that is, meeting second condition) should be exceeded down time.
Below only as a kind of example：If the current SoC of electric automobile is St, it is contemplated that down time is Te, SaIt is from history
The respective battery electricity SoC of each electric automobile average value, T during the charging drawn in charging service recordaIt is from history charging
The average value of the corresponding down time of each electric automobile during the charging drawn in service log, then charge requirement probability can be by such as
Under type is calculated：
If 1) St<=SaAnd Te<=Ta, then P (D)=P (C) * P (S | D) * (1-P (T | D))；
If 2) St<=SaAnd Te>Ta, then P (D)=P (C) * P (S | D) * P (T | D)；
If 3) St>SaAnd Te<=Ta, then P (D)=P (C) * (1-P (S | D)) * (1-P (T | D))；
If 4) St>SaAnd Te>Ta, then P (D)=P (C) * (1-P (S | D)) * P (T | D).
After the charge requirement probability of each electric automobile is tried to achieve, or, descending sort is carried out to them, and need to charge
N electric automobile of probability highest is asked to form the list of potential Rechargeable vehicle, or, a certain threshold is more than with charge requirement probability
N electric automobile of value forms the list of potential Rechargeable vehicle.
Improved as one kind to above-described embodiment, charging electric vehicle needing forecasting method can further comprise following step
Suddenly：
Each potential Rechargeable vehicle is in the projected service needed for respective service point is charged in step S14, estimation list
Between.
Specifically, the list based on the step S12 potential Rechargeable vehicles generated, estimates each potential charging in step S14
Projected service time of the automobile needed for respective service point is charged.
Wherein, described respective service point can be the current location of each potential Rechargeable vehicle or the clothes away from its nearest neighbours
Business point position or system for its dispatching distribution service point position or be suitable for carrying out other suitable positions of charging service.It is expected that
Time that service time can be the potential Rechargeable vehicle needed for fully charged or needed for charging to user's Expected energy when
Between or charging interval for specifying of user.
As an example, the projected service time can represent that attendant or service vehicle respond the charging of potential Rechargeable vehicle
Request is until the potential Rechargeable vehicle completes to charge and be back to the duration of user.
Step S16, with potential Rechargeable vehicle with regard to whether implementing charging service and being communicated.
Any electric automobile in the list of potential Rechargeable vehicle is likely to selection and requires charging or without charging, in view of
This, based on the projected service time obtained in step S14, in step s 16, whether system is with potential Rechargeable vehicle with regard to implementing to fill
Electricity service is communicated, if user requires charging service, dispatch service personnel or service vehicle are gone to and are suitable for carrying out by system
The service point of charging service, can also indicate that the potential Rechargeable vehicle goes to the service point.
During communication, according to the current electric quantity of Rechargeable vehicle, and it is expected that down time, system can also be to user
Propose the recommendation of different stage.In the case of appropriate (such as the electricity of Rechargeable vehicle is in an emergency), system will be adjusted while communication
Degree attendant or service vehicle go to the service point for being suitable for carrying out charging service, please to following charging of early response user
Ask.
As another improvement embodiment, in communication process, if the projected service time from step S14 meets certain
One service standard (for example, the very first time needed for attendant responds the charge request of potential Rechargeable vehicle is less than 15 minutes),
Then inform that user etc. is (also can be simultaneously electric automobile distribution service point to be charged) to be charged；If the projected service time can not expire
Sufficient service standard (that is, the above-mentioned very first time exceeds 15 minutes), then inform use by the projected service time (or above-mentioned very first time)
Family, and wait the instruction of user in the very first time window (such as 5 minutes durations), and based on indicating come dispatch service personnel or
Service vehicle response sends the charge request of the potential Rechargeable vehicle of instruction.
As preferred embodiment, according to the sequence of the charge requirement probability of each potential Rechargeable vehicle, preferential and active
High potential Rechargeable vehicle is communicated with charge requirement probability, responds to anticipation and as early as possible the charge request of user, so that
Good Consumer's Experience is provided.
As the further improvement to above-mentioned first embodiment, charging electric vehicle needing forecasting method is additionally included in step
The following steps are performed after S16：If potential Rechargeable vehicle is by communication without receiving charging service, or potential Rechargeable vehicle is complete
Into charging service, then some Rechargeable vehicles of charge requirement probability highest in monitored area are added to the row of potential Rechargeable vehicle
Table, and return to step S14 and continue executing with.
The charging electric vehicle needing forecasting method that above-mentioned first embodiment is provided, not according to the charging order of user
Dispatch service, but in active predicting monitored area each electric automobile charge requirement probability, to form potential Rechargeable vehicle
List, and then communicated with each potential Rechargeable vehicle, is asked, user's request and the forward scheduling service such as not even through user,
This scheduling mode can substantially shorten the response time of user's charge request, alleviate the surge pressure of system, so as to provide good
Consumer's Experience.
It is contemplated that, according to the charge requirement probability obtained by the calculating of above-mentioned first embodiment, it is contemplated that the history choosing of user
Select, the factor such as the current electric quantity of long-term habits and electric automobile so that this conditional probability is more fitted, user is about whether please
Seek the actual selection of charging.The potential Rechargeable vehicle list generated based on the charge requirement probability of each electric automobile, is more suitable for
A series of current electric vehicles for expecting charging are characterized, therefore, this charging electric vehicle needing forecasting method is conducive into one
Step shortens the response time of user's charge request, improves the efficiency of charging service system.
Second embodiment of the invention provides a kind of charging electric vehicle demand forecast system, and it includes area monitoring module
201st, probability evaluation entity 203, estimated module 205 of optional time and optional user's communication module 207.
Wherein, area monitoring module 201 determines its battery electric quantity, worked as respectively for each electric automobile in monitored area
The estimated down time of front position, and its probability charged in current time further determined according to historical data.
Probability evaluation entity 203 is coupled with region detection module 201, and it calculates the charge requirement of each electric automobile respectively
Probability, and the charge requirement probability of each electric automobile is ranked up to form the list of potential Rechargeable vehicle.Wherein, charging is needed
It is the conditional probability that electric automobile is charged when at least first condition is met to seek probability, and first condition can be expressed as electronic
The current battery charge of automobile is less than first threshold.
As an improvement embodiment, probability evaluation entity 203 calculates electric automobile in first condition and Article 2
The conditional probability charged when part is satisfied by, the charge requirement probability using the conditional probability as electric automobile；Wherein, first
Condition is less than first threshold for the current battery charge of electric automobile, and second condition is that electric automobile stops in the estimated of current location
The car time exceedes Second Threshold.
Time estimated module 205, user communication module 207 can be coupled in above-described embodiment as add-on module.
Time, estimated module 205 was coupled with probability evaluation entity 203, for estimating that each potential Rechargeable vehicle exists in list
Respective service point charged needed for the projected service time.
User communication module 207 is coupled with time estimated module, and whether it is with each potential Rechargeable vehicle (user) with regard to real
Charging service is applied to be communicated.
The charging electric vehicle demand forecast system that above-mentioned second embodiment is provided, predicts electric automobile (user) on one's own initiative
Potential charge requirement, and then can there is provided good Consumer's Experience with early response user charge request.It is based on this pre-
Survey (anticipation), the system operating efficiency is higher, it is possible to prevente effectively from surge pressure, is adapted to promote in large- and-medium size cities.
It should be noted that above-mentioned charge requirement Forecasting Methodology can be realized with computer program, it is thus possible in advance
Material, the embodiment party of the present invention all should be covered by by being related to the computer system of this computer program, computer-readable recording medium
In case.
As an example, the present invention also provides a kind of computer system, it includes memory, processor and is stored in memory
Computer program that is upper and being run by processor, wherein, realize the following steps during computing device computer program：
S21, for each electric automobile in monitored area, its battery electric quantity, the estimated parking in current location are determined respectively
Time, and its probability charged in current time is determined according to historical data.
S23, the charge requirement probability for calculating each electric automobile respectively, and the charge requirement probability of each electric automobile is carried out
Sort to form the list of potential Rechargeable vehicle.Wherein, charge requirement probability is electric automobile when at least first condition is met
The conditional probability charged.
As a further improvement, in above computer system, can also further be realized during computing device computer program
The following steps：
Each potential Rechargeable vehicle is in the projected service needed for respective service point is charged in S25, estimation gained list
Between.
S27, with each potential Rechargeable vehicle with regard to whether implementing charging service and being communicated respectively.
In above-mentioned example, charge requirement probability is that electric automobile is filled when first condition and second condition are satisfied by
The conditional probability of electricity, wherein, first condition is less than first threshold for the current battery charge of electric automobile, and second condition is electronic
Estimated down time of the automobile in current location exceedes Second Threshold.
Under preferable case, charge requirement probability is calculated according to Bayes law using equation below：P (D)=P (C) * P (S |
D) * P (T | D), wherein, P (C) represents the probability that a certain electric automobile is charged in corresponding current time in historical data, P (S
| D) represent historical data in the electric automobile when being charged its corresponding current battery charge less than first threshold probability, P
(T | D) represent the electric automobile probability of its corresponding down time more than Second Threshold when being charged in historical data.
Further, the various improvement projects for the charging electric vehicle needing forecasting method that above-mentioned first embodiment is provided
Can by processor perform memory on computer program when implement.
As another example, the present invention provides a kind of computer-readable recording medium, is stored thereon with computer program, its
In, computer program realizes the following steps when being executed by processor：
S31, for each electric automobile in monitored area, its battery electric quantity, the estimated parking in current location are determined respectively
Time, and its probability charged in current time is determined according to historical data.
S33, the charge requirement probability for calculating each electric automobile respectively, and the charge requirement probability of each electric automobile is carried out
Sort to form the list of potential Rechargeable vehicle.Wherein, charge requirement probability is electric automobile when at least first condition is met
The conditional probability charged.
As a further improvement, the computer program stored on the readable storage medium storing program for executing when executed can also be further real
Existing the following steps：
Each potential Rechargeable vehicle is in the projected service needed for respective service point is charged in S35, estimation gained list
Between.
S37, with each potential Rechargeable vehicle with regard to whether implementing charging service and being communicated respectively.
In above-mentioned example, similarly, charge requirement probability is that electric automobile is satisfied by first condition and second condition
The conditional probability of Shi Jinhang chargings, wherein, first condition is less than first threshold, Article 2 for the current battery charge of electric automobile
Part is estimated down time of the electric automobile in current location to exceed Second Threshold.
Under preferable case, charge requirement probability is calculated according to Bayes law using equation below：P (D)=P (C) * P (S |
D) * P (T | D), wherein, P (C) represents the probability that a certain electric automobile is charged in corresponding current time in historical data, P (S
| D) represent historical data in the electric automobile when being charged its corresponding current battery charge less than first threshold probability, P
(T | D) represent the electric automobile probability of its corresponding down time more than Second Threshold when being charged in historical data.
Further, the various improvement projects for the charging electric vehicle needing forecasting method that above-mentioned first embodiment is provided
Can by processor perform storage medium on computer program when implement.
Described above is not lain in and limited the scope of the invention only in the preferred embodiments of the present invention.Ability
Field technique personnel can make various modifications design, without departing from the thought and subsidiary claim of the present invention.
Claims (14)
1. a kind of charging electric vehicle needing forecasting method, comprises the following steps：
A), for each electric automobile in monitored area, its battery electric quantity, the estimated down time in current location are determined respectively,
And its probability charged in current time is determined according to historical data；And
B) the charge requirement probability of each electric automobile, is calculated respectively, and to the charge requirement probability of each electric automobile
It is ranked up to form the list of potential Rechargeable vehicle；Wherein, the charge requirement probability is the electric automobile at least the
The conditional probability that one condition is charged when meeting.
2. according to the method described in claim 1, it is characterised in that methods described also includes：
C), estimate that each potential Rechargeable vehicle is in the projected service needed for respective service point is charged in the list
Between；And
D), with each potential Rechargeable vehicle with regard to whether implementing charging service and being communicated.
3. according to the method described in claim 1, it is characterised in that in the step b), the charge requirement probability is institute
The conditional probability that electric automobile is charged when the first condition and second condition are satisfied by is stated, wherein, described first
Condition is less than first threshold for the current battery charge of the electric automobile, and the second condition is the electric automobile described
The estimated down time of current location exceedes Second Threshold.
4. method according to claim 3, it is characterised in that the charge requirement probability is according to Bayes law using such as
Lower formula is calculated：
P (D)=P (C) * P (S | D) * P (T | D), wherein, P (C) represents that a certain electric automobile should in phase in historical data
The probability that the preceding time is charged, and P (S | D) represent the electric automobile its corresponding present battery when being charged in historical data
Electricity is less than the probability of the first threshold, P (T | D) represent in historical data the electric automobile when being charged its accordingly stop
The car time exceedes the probability of the Second Threshold.
5. method according to claim 3, it is characterised in that the first threshold is that each electric automobile carries out charging phase
The average value of battery electric quantity is answered, the Second Threshold is the average value of corresponding down time when each electric automobile is charged.
6. method according to claim 2, it is characterised in that in the step c), the projected service time represents
Attendant or service vehicle respond the charge request of the potential Rechargeable vehicle until the potential Rechargeable vehicle completes charging
And it is back to the duration of user.
7. method according to claim 2, it is characterised in that in the step d), if the projected service time is full
Sufficient service standard, then active warning user charging；, will be described if the projected service time can not meet the service standard
The projected service time informs user, and waits in the very first time window instruction of user, and based on the instruction to dispatch
State attendant or service vehicle responds the charge request for the potential Rechargeable vehicle for sending the instruction.
8. method according to claim 2, it is characterised in that in the step d), according to each potential charging vapour
The sequence of the charge requirement probability of car, the preferential and potential Rechargeable vehicle actively high with the charge requirement probability enters
Row communication.
9. method according to claim 2, it is characterised in that also including step e)：
If the potential Rechargeable vehicle passes through communication without receiving charging service, or the potential Rechargeable vehicle has completed charging clothes
Business, then add the potential Rechargeable vehicle by some Rechargeable vehicles of the charge requirement probability highest in the monitored area
List, and return to the step c) and continue executing with.
10. a kind of charging electric vehicle demand forecast system, including：
Area monitoring module, for each electric automobile in monitored area, determines its battery electric quantity, in the estimated of current location respectively
Down time, and its probability charged in current time is determined according to historical data；And
Probability evaluation entity, the charge requirement probability for calculating each electric automobile respectively, and to each electric automobile
Charge requirement probability be ranked up to form the list of potential Rechargeable vehicle；Wherein, the charge requirement probability is the electricity
The conditional probability that electrical automobile is charged when at least first condition is met.
11. system according to claim 10, it is characterised in that the system also includes：
Time estimated module, for estimating that each potential Rechargeable vehicle is needed for respective service point is charged in the list
The projected service time；And
User communication module, with each potential Rechargeable vehicle with regard to whether implementing charging service and being communicated.
12. system according to claim 10, it is characterised in that the probability evaluation entity calculates the electric automobile and existed
The conditional probability that the first condition and second condition are charged when being satisfied by as the electric automobile charge requirement
Probability, wherein, the first condition is less than first threshold for the current battery charge of the electric automobile, and the second condition is
Estimated down time of the electric automobile in the current location exceedes Second Threshold.
13. a kind of computer system, including memory, processor and it is stored on the memory and is run by the processor
Computer program, it is characterised in that described in the computing device during computer program, any one of claim 1 to 9 institute
The method stated is performed.
14. a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the computer program
When being executed by processor, the method any one of claim 1 to 9 is performed.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710351396.8A CN107316097A (en) | 2017-05-18 | 2017-05-18 | Charging electric vehicle needing forecasting method and system |
| PCT/CN2017/094511 WO2018209810A1 (en) | 2017-05-18 | 2017-07-26 | Method and system for predicting charging requirements of electric vehicles |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710351396.8A CN107316097A (en) | 2017-05-18 | 2017-05-18 | Charging electric vehicle needing forecasting method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107316097A true CN107316097A (en) | 2017-11-03 |
Family
ID=60182000
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710351396.8A Pending CN107316097A (en) | 2017-05-18 | 2017-05-18 | Charging electric vehicle needing forecasting method and system |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN107316097A (en) |
| WO (1) | WO2018209810A1 (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108336749A (en) * | 2017-12-15 | 2018-07-27 | 蔚来汽车有限公司 | Electrical equipment idle power-on method and energy consumption equipment idle complementary energy method |
| WO2019105065A1 (en) * | 2017-11-28 | 2019-06-06 | 蔚来汽车有限公司 | Electronic map based charging request initiating time prediction method and device |
| CN111967698A (en) * | 2020-10-23 | 2020-11-20 | 北京国新智电新能源科技有限责任公司 | Electric automobile charging system and device based on mobile charging pile scheduling |
| CN114368319A (en) * | 2020-10-15 | 2022-04-19 | 丰田自动车株式会社 | Server, mobile system, and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103870888A (en) * | 2014-03-10 | 2014-06-18 | 国家电网公司 | Load forecasting method for electric vehicle |
| CN104778263A (en) * | 2015-04-23 | 2015-07-15 | 储盈新能源科技（上海）有限公司 | Simulating data mining method for electric vehicle charging station system |
| CN105719030A (en) * | 2016-03-29 | 2016-06-29 | 武汉大学 | Method for electric vehicle load prediction based on efficiency maximization principle |
-
2017
- 2017-05-18 CN CN201710351396.8A patent/CN107316097A/en active Pending
- 2017-07-26 WO PCT/CN2017/094511 patent/WO2018209810A1/en active Application Filing
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103870888A (en) * | 2014-03-10 | 2014-06-18 | 国家电网公司 | Load forecasting method for electric vehicle |
| CN104778263A (en) * | 2015-04-23 | 2015-07-15 | 储盈新能源科技（上海）有限公司 | Simulating data mining method for electric vehicle charging station system |
| CN105719030A (en) * | 2016-03-29 | 2016-06-29 | 武汉大学 | Method for electric vehicle load prediction based on efficiency maximization principle |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019105065A1 (en) * | 2017-11-28 | 2019-06-06 | 蔚来汽车有限公司 | Electronic map based charging request initiating time prediction method and device |
| CN108336749A (en) * | 2017-12-15 | 2018-07-27 | 蔚来汽车有限公司 | Electrical equipment idle power-on method and energy consumption equipment idle complementary energy method |
| TWI682608B (en) * | 2017-12-15 | 2020-01-11 | 香港商蔚來汽車有限公司 | Idle time charging method of power consumption equipment and idle time energy supplement method of energy-consuming equipment |
| CN114368319A (en) * | 2020-10-15 | 2022-04-19 | 丰田自动车株式会社 | Server, mobile system, and storage medium |
| CN114368319B (en) * | 2020-10-15 | 2024-03-01 | 丰田自动车株式会社 | Server, mobile system, and storage medium |
| CN111967698A (en) * | 2020-10-23 | 2020-11-20 | 北京国新智电新能源科技有限责任公司 | Electric automobile charging system and device based on mobile charging pile scheduling |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2018209810A1 (en) | 2018-11-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107316097A (en) | Charging electric vehicle needing forecasting method and system | |
| CN109177802B (en) | Electric automobile ordered charging system and method based on wireless communication | |
| JP6124157B2 (en) | Battery usable time estimation apparatus and method | |
| US11881060B2 (en) | Electric vehicle predictive range estimating systems and methods | |
| CN103620907B (en) | Charge/discharge assist device | |
| CN105398347A (en) | Electric vehicle intelligent queuing method capable of improving charging efficiency | |
| CN109523087B (en) | Electric automobile quick charging station energy storage monitoring system and method based on deep learning | |
| CN107103383A (en) | A kind of dynamic share-car dispatching method based on focus of calling a taxi | |
| CN105139222A (en) | Self adaptive electric vehicle charging service rate floating calculation system and method | |
| CN112519631B (en) | Battery control device, charge/discharge system, parking lot system, secondary battery recycling system, battery control method, and storage medium | |
| CN110605983B (en) | Method and device for determining charging speed of electric automobile | |
| CN105140977A (en) | Electric automobile battery replacement method based on power network dispatching and battery replacement service Internet of things | |
| CN106594131A (en) | Brake pad abrasion early-warning method and system | |
| Wang et al. | Event-based electric vehicle scheduling considering random user behaviors | |
| KR20130048413A (en) | Intelligent charging management method and apparatus for electronic vehicles | |
| CN114066040A (en) | Charging scheduling method, device and system and transport vehicle | |
| CN112600194A (en) | Automobile charging management method and device, automobile, storage medium and processor | |
| CN110675044B (en) | Method and system for determining participation auxiliary service type of electric automobile | |
| JP2015171189A (en) | Device and method for charge/discharge management, and program | |
| CN113580981A (en) | Charging detection method, terminal and system | |
| CN117076761A (en) | Intelligent management method and system for charging pile of new energy automobile | |
| CN116011663A (en) | Order receiving time measuring and calculating method and device, electronic equipment and storage medium | |
| CN116243196A (en) | Power battery health state evaluation method, device, equipment and storage medium | |
| CN115742781A (en) | Battery charging method and device, electronic equipment and storage medium thereof | |
| CN113682181A (en) | Method for improving utilization rate of charging pile and related equipment |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20200804 Address after: Susong Road West and Shenzhen Road North, Hefei Economic and Technological Development Zone, Anhui Province Applicant after: Weilai (Anhui) Holding Co., Ltd Address before: Room 502, Minsheng Bank Building, 12 Cecil Harcourt Road, central, Hongkong, China Applicant before: NIO NEXTEV Ltd. |
|
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20171103 |