CN109435757B - Charging pile number prediction method based on school electric vehicle travel data - Google Patents

Charging pile number prediction method based on school electric vehicle travel data Download PDF

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CN109435757B
CN109435757B CN201811286673.2A CN201811286673A CN109435757B CN 109435757 B CN109435757 B CN 109435757B CN 201811286673 A CN201811286673 A CN 201811286673A CN 109435757 B CN109435757 B CN 109435757B
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charging
time
electric
leaving
unit time
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CN109435757A (en
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顾菊平
姜旭
张新松
朱建红
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Nantong University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/44—Control modes by parameter estimation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • 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
    • 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
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles

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Abstract

The invention discloses a charging pile number prediction method based on school electric vehicle travel data, and belongs to the technical field of charging pile number optimization configuration. The method not only analyzes the quantity of the charging facilities through the measured data of the campus, gets rid of the limitation of the previous method on the macroscopic analysis of the automobile holding quantity, has practical functional area application significance, but also has practical significance on the research of the quantity of the charging facilities in other areas; meanwhile, in the charging model, a curve of the change of the charging quantity along with time is fitted, the peak value of the curve represents the number of the charging piles, the curve can provide practical significance for the number of the charging piles configured in unit time, and the concept of a negative electric automobile is introduced into the charging model, wherein the negative electric automobile is equivalent to an inhibition signal in a system, can simulate an emergency situation in the actual charging process, and has practical significance.

Description

Charging pile number prediction method based on school electric vehicle travel data
Technical Field
The invention relates to a charging pile number prediction method based on school electric vehicle travel data, and belongs to the technical field of charging pile number optimal configuration.
Background
At present, the electric automobile with low pollution and zero emission has good development prospect, and the establishment of a perfect electric automobile charging system is the premise and the foundation of the development. With the large-scale construction of charging facilities, how to scientifically and reasonably configure the charging facilities of the electric automobile becomes one of the problems which are urgently needed to be solved at present. In recent years, most demand analysis for charging facilities has focused on charging station capacity determination or site selection optimization. With the progress of research, part of researchers turn to the analysis of the number of charging piles, and the main research angles include the following three aspects: firstly, the quantity of charging piles is determined by analyzing the holding capacity of the electric automobile by using a prediction method. The method can predict the quantity of the electric automobile charging piles in a certain city from a macroscopic view, and the quantity demand of the charging piles in a specific certain area cannot be obtained. Secondly, the daily power consumption of the electric automobile and the State of Charge (SOC) of the battery after the electric automobile arrives at home are analyzed, so that the requirement of the electric automobile charging pile in the residential area is obtained. Thirdly, analyzing the single-journey destination trip taking the residential area as the starting point to obtain the matching degree of the charging piles at all the destinations. The limitations that this approach makes on vehicle travel may bias the results from reality.
Disclosure of Invention
The invention aims to solve the technical problem that the limit on the vehicle journey causes the deviation between the result and the actual situation, and provides a charging pile number prediction method based on the travel data of the electric vehicles in the school.
The invention adopts the following technical scheme for realizing the aim of the invention:
the charging pile number prediction method based on the travel data of the electric vehicles in the school comprises the following steps:
step 1, fitting the charging start-stop time length and the parking time length of the electric automobile in the school, and enabling the parking time length of the electric automobile in the school to be equal to the charging time length;
step 2, carrying out statistical analysis on the charge range of the SOC value of the electric vehicle to obtain the charge range of the SOC value of the electric vehicle and the parking time required to be fully charged;
step 3, selecting a charging mode according to the fully charged parking time and the charge range of the SOC value of the charge state, and calculating the leaving time of the fully charged electric automobile;
step 4, dividing the available charging time in the school into a plurality of unit time periods, and counting the number of the electric vehicles estimated to leave in each unit time period according to the leaving time of each electric vehicle when being fully charged;
step 5, counting the leaving time and the leaving number of the negative electric vehicles, wherein the negative electric vehicles refer to vehicles which are not charged fully, and calculating the leaving number of the negative electric vehicles in each unit time period and the estimated fully charged leaving number of the negative electric vehicles according to the leaving time and the leaving number of the negative electric vehicles;
step 6, counting the number of the electric automobiles which actually leave in each unit time period according to the number of the electric automobiles which are estimated to leave in each unit time period, the leaving number of the negative electric automobiles and the estimated fully charged leaving number of the negative electric automobiles;
step 7, counting the number of the electric automobiles arriving in each unit time period and the number of the electric automobiles actually leaving from each unit time period, and obtaining the charging number N of the electric automobiles in each unit time period;
and 8, performing function continuity on each unit time period and the online electric vehicle charging quantity N, fitting a time-varying curve of the charging quantity, and obtaining the number of charging piles based on the school electric vehicle travel data according to the time-varying curve of the charging quantity.
As a preferred technical scheme of the invention: the specific steps of step 1 are as follows: step 1.1, selecting 0: 00-24: 00 the vehicle information of the electric vehicle entering and leaving recorded by the charging terminal is used as a data source; step 1.2, fitting the charging starting time and the parking time, and considering that the parking time of the electric automobile in a school is equal to the charging time; and step 1.3, fitting the parking time length and the charging time length of the electric automobile in a school by adopting a nuclear density estimation method.
As a preferred technical scheme of the invention: the specific steps of step 2 are as follows: step 2.1, inputting an initial state of charge SOC value of the electric automobile into a charging terminal; step 2.2, fitting probability distribution of the initial SOC value and the SOC value provided by the charging terminal by utilizing multiple statistical results of the previous SOC value of the electric vehicle provided by the charging terminal; 2.3, extracting a random number by adopting Monte Carlo random simulation, and counting and analyzing the charge range of the SOC of the electric vehicle; and 2.4, calculating the charging time of the maximum SOC value of the electric vehicle.
As a preferred technical scheme of the invention: the specific steps of step 3 are as follows: step 3.1, when the parking time is less than 20 minutes and the SOC residual capacity is more than 50%, no charging is carried out; when the charging time reaching the maximum SOC value is less than or equal to the parking time within the parking time range, selecting a slow charging mode; when the charging time reaching the maximum SOC value is longer than the parking time within the parking time range, selecting a quick charging mode; step 3.2, obtaining the charging time length of the electric automobile when the electric automobile is fully charged according to the slow charging mode or the fast charging mode; and 3.3, obtaining the automobile leaving time according to the automobile starting charging time and the charging time when the electric automobile is fully charged.
As a preferred technical scheme of the invention: the specific steps of step 4 are as follows: step 4.1, charging time available in the school is 0: 00-24: 00 into i unit time segments; step 4.2, counting the leaving time of each electric automobile; and 4.3, counting the number of the electric vehicles which are estimated to be fully charged and leave in each unit time period.
As a preferred technical scheme of the invention: the specific steps of step 5 are as follows: step 5.1, counting the leaving time of each negative electric automobile and the leaving number of the negative electric automobiles; step 5.2, obtaining the estimated fully-charged leaving time of each negative electric vehicle according to the leaving time of each electric vehicle when being fully charged and the leaving time of the electric vehicle changed into the negative electric vehicle; and 5.3, counting the leaving number of the negative electric vehicles in each unit time period and the estimated fully charged leaving number of the negative electric vehicles according to the leaving number of the negative electric vehicles and the estimated fully charged leaving time of each negative electric vehicle.
As a preferred technical scheme of the invention: the specific steps of step 6 are as follows: 6.1, subtracting the estimated number of the electric automobiles which leave in each unit time period from the estimated number of the negative electric automobiles which leave in each unit time period to obtain the actual number of the electric automobiles which leave in each unit time period in a full charging mode; and 6.2, adding the number of the electric automobiles which are actually fully charged and leave in each unit time period to the number of the negative electric automobiles which leave in each unit time period to obtain the number of the electric automobiles which actually leave in each unit time period.
As a preferred technical scheme of the invention: the specific steps of step 7 are as follows: 7.1, counting the number of the electric vehicles arriving and the number of the electric vehicles actually leaving in each unit time period; 7.2, obtaining the charging quantity N of the electric automobiles in each unit time period; and 7.3, for any time point, the number of the charging vehicles of the online electric vehicle charging number N in the ith time is equal to the difference value between the arrival number and the actual departure number of the vehicles in the previous ith-1 time.
As a preferred technical scheme of the invention: the specific steps of step 8 are as follows: 8.1, performing function continuity on each unit time period and the online electric vehicle charging quantity N; 8.2, continuously fitting a change curve of the charging quantity along with time according to the function, wherein the peak value of the curve represents the number of the charging piles; and 8.3, obtaining the number of the charging piles based on the travel data of the electric vehicles in the school according to the change curve of the charging quantity along with the time.
Compared with the prior art, the charging pile number prediction method based on the school electric vehicle travel data has the following technical effects:
(1) the number of the charging facilities is analyzed through the measured data of the campus, the limitation of the traditional method on the macroscopic analysis of the automobile holding quantity is eliminated, the method has practical functional area application significance, and the method has universality on the number research of the charging facilities in other areas.
(2) In the charging model, a curve of the change of the charging quantity with time is fitted, the peak value of the curve represents the number of the charging piles, and the curve can provide practical significance for the number of the charging piles configured in unit time.
(3) The concept of a negative electric automobile is introduced into the charging model, which is equivalent to a suppression signal in a system, can simulate an emergency in the actual charging process, and has practical significance.
Drawings
FIG. 1 is a design flow chart of a charging pile number prediction method based on school electric vehicle travel data, which is designed by the invention;
FIG. 2 is a user parking duration profile;
fig. 3 is a graph of the change in the amount of charge over time.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings:
as shown in fig. 1, the method for predicting the number of charging piles based on the travel data of the electric vehicles in the school, which is disclosed by the invention, comprises the following steps: step 1, fitting the charging start-stop time length and the parking time length of the electric automobile in the school, and enabling the parking time length of the electric automobile in the school to be equal to the charging time length; step 2, carrying out statistical analysis on the charge range of the SOC value of the electric vehicle to obtain the charge range of the SOC value of the electric vehicle and the parking time required to be fully charged; step 3, selecting a charging mode according to the fully charged parking time and the charge range of the SOC value of the charge state, and calculating the leaving time of the fully charged electric automobile; step 4, dividing the available charging time in the school into a plurality of unit time periods, and counting the number of the electric vehicles estimated to leave in each unit time period according to the leaving time of each electric vehicle when being fully charged; step 5, counting the leaving time and the leaving number of the negative electric vehicles, wherein the negative electric vehicles refer to vehicles which are not charged fully, and calculating the leaving number of the negative electric vehicles in each unit time period and the estimated fully charged leaving number of the negative electric vehicles according to the leaving time and the leaving number of the negative electric vehicles; step 6, counting the number of the electric automobiles which actually leave in each unit time period according to the number of the electric automobiles which are estimated to leave in each unit time period, the estimated fully charged number of the negative electric automobiles and the estimated leaving number of the negative electric automobiles; step 7, counting the number of the electric automobiles arriving in each unit time period and the number of the electric automobiles actually leaving from each unit time period, and obtaining the charging number N of the electric automobiles in each unit time period; and 8, performing function continuity on each unit time period and the online electric vehicle charging quantity N, fitting a time-varying curve of the charging quantity, and obtaining the number of charging piles based on the school electric vehicle travel data according to the time-varying curve of the charging quantity.
The specific steps of step 1 are as follows: step 1.1, selecting 0: 00-24: 00 the vehicle information of the electric vehicle entering and leaving recorded by the charging terminal is used as a data source; step 1.2, fitting the charging starting time and the parking time, and considering that the parking time of the electric automobile in a school is equal to the charging time; and step 1.3, fitting the parking time length and the charging time length of the electric automobile in a school by adopting a nuclear density estimation method.
The specific steps of step 2 are as follows: step 2.1, inputting an initial state of charge SOC value of the electric automobile into a charging terminal; step 2.2, fitting probability distribution of the initial SOC value and the SOC value provided by the charging terminal by utilizing multiple statistical results of the previous SOC value of the electric vehicle provided by the charging terminal; 2.3, extracting a random number by adopting Monte Carlo random simulation, and counting and analyzing the charge range of the SOC of the electric vehicle; and 2.4, calculating the charging time of the maximum SOC value of the electric vehicle.
The specific steps of step 3 are as follows: step 3.1, when the parking time is less than 20 minutes and the SOC residual capacity is more than 50%, no charging is carried out; when the charging time reaching the maximum SOC value is less than or equal to the parking time within the parking time range, selecting a slow charging mode; when the charging time reaching the maximum SOC value is longer than the parking time within the parking time range, selecting a quick charging mode; step 3.2, obtaining the charging time length of the electric automobile when the electric automobile is fully charged according to the slow charging mode or the fast charging mode; and 3.3, obtaining the automobile leaving time according to the automobile starting charging time and the charging time when the electric automobile is fully charged.
The specific steps of step 4 are as follows: step 4.1, charging time available in the school is 0: 00-24: 00 into i unit time segments; step 4.2, counting the leaving time of each electric automobile; and 4.3, counting the number of the electric vehicles which are estimated to be fully charged and leave in each unit time period.
The specific steps of step 5 are as follows: step 5.1, counting the leaving time of each negative electric automobile and the leaving number of the negative electric automobiles; step 5.2, obtaining the estimated fully-charged leaving time of each negative electric vehicle according to the leaving time of each electric vehicle when being fully charged and the leaving time of the electric vehicle changed into the negative electric vehicle; and 5.3, counting the leaving number of the negative electric vehicles in each unit time period and the estimated fully charged leaving number of the negative electric vehicles according to the leaving number of the negative electric vehicles and the estimated fully charged leaving time of each negative electric vehicle.
The specific steps of step 6 are as follows: 6.1, subtracting the estimated number of the electric automobiles which leave in each unit time period from the estimated number of the negative electric automobiles which leave in each unit time period to obtain the actual number of the electric automobiles which leave in each unit time period in a full charging mode; and 6.2, adding the number of the electric automobiles which are actually fully charged and leave in each unit time period to the number of the negative electric automobiles which leave in each unit time period to obtain the number of the electric automobiles which actually leave in each unit time period.
The specific steps of step 7 are as follows: 7.1, counting the number of the electric vehicles arriving and the number of the electric vehicles actually leaving in each unit time period; 7.2, obtaining the charging quantity N of the electric automobiles in each unit time period; and 7.3, for any time point, the number of the charging vehicles of the online electric vehicle charging number N in the ith time is equal to the difference value between the arrival number and the actual departure number of the vehicles in the previous ith-1 time.
The specific steps of step 8 are as follows: 8.1, performing function continuity on each unit time period and the online electric vehicle charging quantity N; 8.2, continuously fitting a change curve of the charging quantity along with time according to the function, wherein the peak value of the curve represents the number of the charging piles; and 8.3, obtaining the number of the charging piles based on the travel data of the electric vehicles in the school according to the change curve of the charging quantity along with the time.
As shown in fig. 2, the maximum parking time is found out by sorting, and the frequency of the parking time in each unit time is sorted out by taking 20 minutes as a time period.
As shown in fig. 3, the available charging time in the school is 0: 00-24: 00 is divided into i unit time periods, 24 hours are divided into 24 unit times, the vehicle arrival number and the vehicle actual departure number in each time period are determined, and the charging vehicle number of the online electric vehicle charging number N in the ith time period is equal to the difference value of the vehicle arrival number and the vehicle actual departure number in the previous i-1 time period for any time point. Counting the charging quantity N of the electric automobile in each unit time, continuously combining the time with the function of the charging automobile, fitting a curve of the charging quantity along with the time, wherein the peak value of the curve represents the number of the charging piles, the curve can provide practical significance for the number of the charging piles configured in the unit time, and finally obtaining the number of the charging piles based on the trip data of the electric automobile in the school.
During work, the charging terminal selects the ratio of 0:00-24 in a certain comprehensive university: 00, the vehicle information of the entering and exiting pure electric vehicle, recorded by the terminal, is used as a data source to fit the charging starting time and the parking time, for the convenience of analysis, the parking time of the electric vehicle in the school is considered to be equal to the charging time, and the vehicle in the school is fitted by using a nuclear density estimation method because the parking does not meet the conventional probability distribution; when an electric vehicle enters a certain general university, the number of the online electric vehicle charging quantity N is stored in a terminal, the number of the entering electric vehicle is e =1, and e is a constant of 1 start. Then, a user inputs the initial SOC value of the electric automobile into a charging terminal and the terminal performs statistical analysis on the acceptance range of SOC change. Because the maximum driving mileage is different, the user is difficult to quantify the acceptance range of the change of the SOC value, so that the probability distribution of the SOC value of the electric vehicle and the multiple statistical results of the SOC value of the electric vehicle provided by the terminal is fitted for Monte Carlo random simulation extraction of random numbers, and the charging range of the SOC of the electric vehicle and the charging time when the electric vehicle is fully charged are statistically analyzed. When the parking time is less than 20 minutes and the SOC residual capacity is more than 50%, the vehicle is not charged; when the charging time reaching the maximum SOC value is less than or equal to the parking time within the parking time range, selecting a slow charging mode; when the charging time reaching the maximum SOC value is longer than the parking time within the parking time range, selecting a quick charging mode; obtaining the charging time of the electric automobile according to the slow charging mode or the fast charging mode; and obtaining the automobile leaving time according to the automobile starting charging time and the fully charged charging time of the electric automobile. And (3) charging time available in the school is 0: 00-24: 00 is divided into i unit time periods, the leaving time of each electric automobile is counted, and then the estimated quantity of the electric automobiles leaving after being fully charged in each unit time period is calculated. The leaving time of each negative electric automobile and the leaving number of the negative electric automobiles are counted, the negative electric automobiles refer to vehicles which are not charged fully, the estimated fully charged leaving time of each negative electric automobile is obtained according to the leaving time of each electric automobile when being charged fully and the leaving time of the electric automobile changing into the negative electric automobile, and the leaving number of the negative electric automobiles in each unit time period and the estimated fully charged leaving number of the negative electric automobiles are counted according to the leaving number of the negative electric automobiles and the estimated fully charged leaving time of each negative electric automobile.
And subtracting the estimated fully-charged leaving number of the negative electric vehicles in each unit time period from the estimated leaving number of the electric vehicles in each unit time period to obtain the actual fully-charged leaving number of the electric vehicles in each unit time period, and adding the actual fully-charged leaving number of the electric vehicles in each unit time period to the leaving number of the negative electric vehicles in each unit time period to obtain the actual leaving number of the electric vehicles in each unit time period.
The whole charging process is a process of researching state change generated in the system along with time, generally, for a charging system working stably for a long time, the probability of vehicle arrival and departure tends to be a constant in unit time, a transition expected value and a transition expected value in unit time are determined, the number of vehicles arriving and the number of vehicles actually leaving in each time period are determined, and for any time point, the number of charging vehicles in the charging number N of the online electric vehicle in the ith time period is equal to the difference value between the number of vehicles arriving and the number of vehicles leaving in the previous i-1 time period. Counting the charging quantity N of the electric automobiles in each unit time, and continuously combining the time with the function of the charging vehicles, fitting a curve of the charging quantity along with the time, wherein the peak value of the curve represents the number of the charging piles, the curve can provide practical significance for the number of the charging piles configured in the unit time, and finally obtaining the number of the charging piles based on the travel data of the electric automobiles in the school.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention, and are not intended to limit the scope of the present invention, and any person skilled in the art should understand that equivalent changes and modifications made without departing from the concept and principle of the present invention should fall within the protection scope of the present invention.

Claims (6)

1. The charging pile number prediction method based on the school electric vehicle travel data is characterized by comprising the following steps of:
step 1, fitting the charging start-stop time length and the parking time length of the electric automobile in the school, and enabling the parking time length of the electric automobile in the school to be equal to the charging time length;
step 2, carrying out statistical analysis on the charge range of the SOC value of the electric vehicle to obtain the charge range of the SOC value of the electric vehicle and the parking time required to be fully charged; the specific steps of step 2 are as follows: step 2.1, inputting an initial state of charge SOC value of the electric automobile into a charging terminal; step 2.2, fitting probability distribution of the initial SOC value and the SOC value provided by the charging terminal by utilizing multiple statistical results of the previous SOC value of the electric vehicle provided by the charging terminal; 2.3, extracting a random number by adopting Monte Carlo random simulation, and counting and analyzing the charge range of the SOC of the electric vehicle; step 2.4, calculating the charging time of the maximum SOC value of the electric vehicle;
step 3, selecting a charging mode according to the fully charged parking time and the charge range of the SOC value of the charge state, and calculating the leaving time of the fully charged electric automobile;
step 4, dividing the available charging time in the school into a plurality of unit time periods, and counting the number of the electric vehicles which are estimated to be fully charged and leave in each unit time period according to the leaving time of each electric vehicle when being fully charged;
step 5, counting the leaving time and the leaving number of the negative electric vehicles, wherein the negative electric vehicles refer to vehicles which are not charged fully, and calculating the leaving number of the negative electric vehicles in each unit time period and the estimated fully charged leaving number of the negative electric vehicles according to the leaving time and the leaving number of the negative electric vehicles; the specific steps of step 5 are as follows: step 5.1, counting the leaving time of each negative electric automobile and the number of the negative electric automobiles; step 5.2, obtaining the estimated fully-charged leaving time of each negative electric vehicle according to the leaving time of each electric vehicle when being fully charged and the time of the electric vehicle changing into the negative electric vehicle; step 5.3, counting the leaving number of the negative electric vehicles in each unit time period and the estimated fully-charged leaving number of the negative electric vehicles according to the number of the negative electric vehicles and the estimated fully-charged leaving time of each negative electric vehicle;
step 6, counting the number of the electric automobiles actually leaving in each unit time period according to the estimated number of the electric automobiles which are fully charged and leave in each unit time period, the estimated number of the negative electric automobiles which are fully charged and leave and the leaving number of the negative electric automobiles; the specific steps of step 6 are as follows: 6.1, subtracting the estimated fully-charged leaving number of the negative electric vehicles in each unit time period from the estimated fully-charged leaving number of the electric vehicles in each unit time period to obtain the actual fully-charged leaving number of the electric vehicles in each unit time period; step 6.2, adding the number of the electric automobiles which are actually fully charged and leave in each unit time period to the number of the negative electric automobiles which leave in each unit time period to obtain the number of the electric automobiles which actually leave in each unit time period;
step 7, counting the number of the electric automobiles arriving in each unit time period and the number of the electric automobiles actually leaving from each unit time period, and obtaining the charging number N of the electric automobiles in each unit time period;
and 8, performing function continuity on the charging quantity N of the electric vehicles in each unit time period, fitting a time-dependent change curve of the charging quantity, and obtaining the number of charging piles based on the school electric vehicle travel data according to the time-dependent change curve of the charging quantity.
2. The campus electric vehicle travel data-based charging pile number prediction method according to claim 1, wherein the specific steps of step 1 are as follows:
step 1.1, selecting 0: 00-24: 00 the vehicle information of the electric vehicle entering and leaving recorded by the charging terminal is used as a data source;
step 1.2, fitting the charging starting time and the parking time, and considering that the parking time of the electric automobile in a school is equal to the charging time;
and step 1.3, fitting the parking time length and the charging time length of the electric automobile in a school by adopting a nuclear density estimation method.
3. The method for predicting the number of charging piles based on the travel data of the electric vehicles at school according to claim 1, wherein the specific steps of step 3 are as follows:
step 3.1, when the parking time is less than 20 minutes and the SOC residual capacity is more than 50%, no charging is carried out; when the charging time reaching the maximum SOC value is less than or equal to the parking time within the parking time range, selecting a slow charging mode; when the charging time reaching the maximum SOC value is longer than the parking time within the parking time range, selecting a quick charging mode;
step 3.2, obtaining the charging time of the electric automobile according to the slow charging mode or the fast charging mode;
and 3.3, obtaining the leaving time of the fully charged automobile according to the starting charging time of the automobile and the charging time of the fully charged electric automobile.
4. The method for predicting the number of charging piles based on the travel data of the electric vehicles at school according to claim 1, wherein the specific steps of the step 4 are as follows:
step 4.1, charging time available in the school is 0: 00-24: 00 into i unit time segments;
4.2, counting the leaving time of each electric automobile when the electric automobile is fully charged;
and 4.3, counting the number of the electric vehicles which are estimated to be fully charged and leave in each unit time period.
5. The method for predicting the number of charging piles based on the travel data of the electric vehicles at school according to claim 1, wherein the specific steps of step 7 are as follows:
7.1, counting the number of the electric vehicles arriving and the number of the electric vehicles actually leaving in each unit time period;
7.2, obtaining the charging quantity N of the electric automobiles in each unit time period;
and 7.3, for any time point, the number of the charging vehicles of the online electric vehicle charging number N in the ith time is equal to the difference value between the arrival number and the actual departure number of the vehicles in the previous ith-1 time.
6. The method for predicting the number of charging piles based on the travel data of the electric vehicles at school according to claim 1, wherein the specific steps of step 8 are as follows:
8.1, carrying out function continuity on the charging quantity N of the electric automobiles in each unit time period;
8.2, continuously fitting a change curve of the charging quantity along with time according to the function, wherein the peak value of the curve represents the number of the charging piles;
and 8.3, obtaining the number of the charging piles based on the travel data of the electric vehicles in the school according to the change curve of the charging quantity along with the time.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5644212A (en) * 1994-11-11 1997-07-01 Fuji Jukogyo Kabushiki Kaisha Traction battery management system
CN105160449A (en) * 2015-07-06 2015-12-16 国家电网公司 Electric automobile charging pile optimization layout method
CN106447129A (en) * 2016-10-31 2017-02-22 北京小飞快充网络科技有限公司 High-efficiency charging station recommendation method based on quick charging piles

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8143842B2 (en) * 2008-12-05 2012-03-27 Lava Four, Llc Dynamic load management for use in recharging vehicles equipped with electrically powered propulsion systems

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5644212A (en) * 1994-11-11 1997-07-01 Fuji Jukogyo Kabushiki Kaisha Traction battery management system
CN105160449A (en) * 2015-07-06 2015-12-16 国家电网公司 Electric automobile charging pile optimization layout method
CN106447129A (en) * 2016-10-31 2017-02-22 北京小飞快充网络科技有限公司 High-efficiency charging station recommendation method based on quick charging piles

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