CN116118532B - Charging pile network layout and power cooperation method based on traffic travel chain - Google Patents
Charging pile network layout and power cooperation method based on traffic travel chain Download PDFInfo
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- 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
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Abstract
The invention provides a charging pile network layout and power cooperation method based on a traffic chain. The method comprises the following steps: extracting traffic chain data of the mobile terminal from mobile terminal signaling data of an electric vehicle owner, identifying the stay behavior of the mobile terminal, and calculating the total stay time of single-day parking of stay points in a base station coverage area and the average single-point-of-month parking time of the electric vehicle per day; the method comprises the steps of calculating the daily charging power of a month-average single point position of an electric vehicle according to the charging requirement of the electric vehicle and the daily parking time of the month-average single point position of a parking point, determining the charging mode of the electric vehicle, and determining the power and the quantity of charging piles which should be distributed at the parking point according to the charging modes of all electric vehicles in the parking point. According to the invention, urban trip chain feature mining is carried out through signaling data, so that the identification of parking features of the electric vehicle is realized, and optimization of electric vehicle charging pile network layout and power coordination are carried out based on the identification.
Description
Technical Field
The invention relates to the technical field of electric automobile charging, in particular to a charging pile network layout and power cooperation method based on a traffic travel chain.
Background
Under the development demand of energy saving and environmental protection, the light electric automobile is rapid due to the characteristic development of environmental protection, the market permeability is continuously improved, but the practical problems of high time cost, poor convenience, mismatched time and space resources of charging supporting facilities, idle waste of charging piles and the like of users in cities are limited to a certain extent, the popularization and promotion of the electric automobile are limited to a certain extent, and the specific use situations of time saving, temperature change and the like (such as winter, night low temperature, reduction of the endurance mileage of the electric automobile caused by the behavior of opening an air conditioner in summer and the like) are increased.
Meanwhile, the current urban power consumption distribution shows obvious peak-valley distribution characteristics, the power consumption in the peak period has higher requirements on the power load, the power utilization efficiency in the power low-load period such as at night is greatly reduced, the energy waste is serious, and the whole power dispatching consumption strategy needs to be optimized.
The optimization of the charging pile network and the charging mode of the electric vehicle is one of effective solutions to the difficult problems of development of the electric vehicle and power grid load optimization. The charging pile is an infrastructure supporting the development of the whole electric automobile industry, and is an effective measure for solving the problem of mismatching of charging pile resources of the electric automobile, relieving large peak-valley difference and optimizing electric power coordination aiming at the optimization of the layout of the charging pile. Based on the power characteristics of the electric vehicle, the charging point position layout and the charging strategy of the electric vehicle are greatly different from those of the conventional oil vehicle, a large amount of new energy vehicles in the city range are used as mobile energy storage and energy utilization units to generate the level-striking interaction with the power grid system in the city, the influence of the charging requirement on the power grid is quite considerable, if the charging requirement can be combined with the travel chain characteristics of the urban travel crowd, the charging pile network which is convenient, efficient and moderately advanced is constructed, the current or even higher and more abundant electric vehicle travel requirements can be met, meanwhile, the optimization of the urban power dispatching consumption strategy is realized, and the peak clipping and valley filling of power dispatching are realized.
In the aspect of traffic chain feature mining, mobile phones are widely used mobile communication tools at present, wireless signaling data generated by the mobile phones can continuously acquire position and time information of mobile phone use groups, the acquisition cost of the data is low, and the data is continuous long-term data and can be used as a high-quality and reliable data source for calculating traffic parameters.
At present, a charging pile network layout method based on a traffic travel chain does not exist in the prior art.
Disclosure of Invention
The invention provides a charging pile network layout and power cooperation method based on a traffic travel chain, which aims to solve the problem of electric vehicle charging optimization under high conservation rate.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
A charging pile network layout and power cooperation method based on a traffic travel chain comprises the following steps:
collecting mobile terminal signaling data of an electric vehicle owner, performing data cleaning and data smoothing on the collected mobile terminal signaling data, and extracting traffic chain data of the mobile terminal;
identifying the stay behavior of the mobile terminal according to the traffic chain data of the mobile terminal, and calculating the total stay time of a single day of stay points in the coverage area of the base station;
calculating the monthly average single-point-position daily electric vehicle parking time of the parking points in the coverage area of the base station according to the proportion of the collected mobile terminal signaling data to the total signaling data and the total single-day parking time;
and calculating the monthly average single-point daily charging power of the electric vehicle according to the charging requirement of the electric vehicle and the monthly average single-point daily electric vehicle parking time of the stay points in the coverage area of the base station, determining the charging mode of the electric vehicle, and determining the power and the quantity of charging piles which should be distributed by the stay points according to the charging modes of all the electric vehicles in the stay points.
Optionally, the collecting mobile terminal signaling data of the electric vehicle owner, performing data cleaning and data smoothing on the collected mobile terminal signaling data, and extracting traffic chain data of the mobile terminal, including:
collecting mobile terminal signaling data of an electric vehicle owner, performing data cleaning and data tag processing on the collected mobile terminal signaling data, and obtaining a spatial position parameter (X) of a base station based on a LAC position area code field and a mapping relation of the mobile terminal signaling data 1 ，Y 1 ) Spatial location parameters (X 1 ，Y 1 ) Smoothing is performed, and the processed spatial data (X 2 ，Y 2 ) And reversely carrying in base station data, matching the base station data to the nearest base station position by using a look up function, obtaining a two-dimensional 0-1 daily travel data matrix of urban residents with time as an abscissa and the updated base station number as an ordinate, and taking the daily travel data matrix as single-day travel chain data of the mobile terminal.
Preferably, the identifying the stay behavior of the mobile terminal according to the traffic chain data of the mobile terminal, and calculating the total stay time of the stay point in the coverage area of the base station includes:
calculating the TIME difference delta t of switching of each point position of the mobile terminal based on the TIME TIME field in the single-day trip chain data of the mobile terminal of the electric vehicle owner d The method comprises the steps of carrying out a first treatment on the surface of the According to the space distance difference S and time difference Deltat between base stations d Calculating to obtain the switching speed V of the mobile terminal;
(1) If the switching speed V is greater than the judgment threshold V i But the spatial distance difference S is smaller than the judgment threshold S j Judging that the mobile terminal is in the stay behavior at the point position;
(2) If the switching speed V is greater than the judgment threshold V i And the space distance difference S is greater than the judgment threshold S j Judging that the mobile terminal does not stay at the point position;
(3) If the switching speed V is smaller than the judgment threshold V i Judging that the mobile terminal is in the stay behavior at the point position;
combining the data of the residence time of all the mobile terminals by taking the month data sample size as a unit to obtain the month average single-day residence time delta t of a certain residence point in the coverage area of a base station b Frequency of stay c and position of stay (X 3 ，Y 3 )；
In the formula Deltat a Single day dwell time, t, for a mobile terminal at a dwell point ai The sum of the single-day stay times of all mobile terminals at a certain stay point, n represents the number of days of the month, Δt b The average single-day residence time of a month is a certain residence point, and alpha is a single-day weight factor;
if Deltat b Greater than a threshold t set by judgment k The recognition requirement of long-time parking in the coverage area of the base station is met, and the parking data of a certain parking point is recorded as A= (X) 3 ，Y 3 ，△t b ,c)。
Preferably, the calculating the month average single-point-position daily electric vehicle parking duration of the parking points in the coverage area of the base station according to the proportion of the collected mobile terminal signaling data to the total signaling data and the total single-day parking residence time includes:
dwell data based on a certain dwell point in the coverage area of a base station is a= (X) 3 ，Y 3 ，△t b C) calculating to obtain the month average single-point position daily electric vehicle parking time delta t of a certain stay point in the coverage area of the base station c ；
Wherein a is the total daily electric vehicle travel total amount of a month average point of a certain stay point in the coverage area of the base station, b is the electric vehicle travel capacity of a collected mobile terminal signaling data sample of the certain stay point, and beta is a human mouth correction parameter, delta t c The monthly average single-point-position daily electric vehicle parking time length of a certain parking point in the coverage area of the base station is delta t c-max Represents the maximum value of the charging time of a bicycle on a single day, delta t c-min Indicating that the charging behaviour is not taken into account when the dwell time is below a certain threshold.
Preferably, the calculating the daily power of the electric vehicle according to the charging requirement of the electric vehicle and the daily parking time of the electric vehicle at the monthly average single point of the parking points in the coverage area of the base station, determining the charging mode of the electric vehicle, and determining the power and the number of the charging piles to be arranged at the parking points according to the charging modes of all the electric vehicles in the parking points includes:
and calculating the charging requirement of the electric vehicle based on the average travel power consumption of the electric vehicle and the accumulated mileage of the vehicle month.
In which W is i For the charging demand, gamma is the correction factor,the average power consumption of the electric vehicle per kilometer is calculated, and L is the average daily accumulated mileage of the electric vehicle;
for each electric vehicle in a certain stay point in the coverage area of the base station, calculating the daily charging power P of a month average single point position i
Wherein P is i Daily charging power for month average single point position, W i For charging demand, η is a reliability coefficient, Δt c The method comprises the steps of averaging the time length of parking of the single-point electric vehicle every day for a month of a certain parking point in the coverage area of a base station;
daily charging power P according to month average single point position of electric vehicle i And configuration information of various charging modes, selecting a charging mode which the electric vehicle should select at a stop point, and determining the types, the powers and the quantity of the charging piles which should be distributed at the stop point according to the charging modes of all the electric vehicles in a certain stop point.
Preferably, the charging mode of the electric vehicle includes:
according to the technical scheme provided by the embodiment of the invention, the urban trip chain feature is excavated through the signaling data, so that the identification of the parking feature of the electric vehicle is realized, the optimization of the network layout of the charging piles of the electric vehicle and the cooperation of the power are carried out based on the identification, and the method is reliable and the process reliability is high.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of implementation of a method for arranging a charging pile network and cooperating power based on a traffic chain according to an embodiment of the present invention;
fig. 2 is a process flow diagram of a method for arranging a charging pile network and cooperating power based on a traffic chain according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating data smoothing for signaling data of a mobile terminal according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a charging mode according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
According to the embodiment of the invention, urban trip chain feature mining is attempted through signaling data, so that the identification of the parking features of the electric vehicle is realized, the optimization of electric vehicle charging pile network layout and the cooperation of power are carried out based on the identification, and the method is reliable and the process reliability is high.
The implementation schematic diagram of the charging pile network layout and power cooperation method based on the traffic travel chain provided by the embodiment of the invention is shown in fig. 1, the specific processing flow is shown in fig. 2, and the implementation schematic diagram comprises the following steps:
step S10: and acquiring the signaling data of the mobile terminal, and performing data cleaning and data tag processing on the signaling data of the mobile terminal.
Compared with data obtained by other traffic collection systems, the extraction and analysis of the positioning traffic information of mobile terminals such as mobile phones based on base stations (cells) comprises: the method and the device have the advantages of large sample size, wide coverage range, good real-time property of acquired data and the like, so that the embodiment of the invention selects the signaling data of the mobile terminal as a data source for acquiring the characteristics of the travel chain.
The spatial displacement data in the basic data in the signaling data of the mobile terminal comprises Handover (HO) data, normal position update (Location area update, LAU) data and the like, and can be used as a reliable criterion for capturing travel chain characteristics. The signaling data of the mobile terminal specifically comprises the following indexes:
table 1 mobile terminal signaling data format
And collecting mobile terminal signaling data of an electric vehicle owner, and carrying out data cleaning and data tag processing on the collected mobile terminal signaling data. Data cleansing includes consistency checking, processing of invalid values and missing values (using methods such as interpolation processing), deletion of duplicate data, and the like; the data label is to make a pre-statistics on the signaling data, and make an advanced statistics on valuable parking time length and parking frequency information, so that the recognition efficiency of the following travel chain characteristics can be improved through the data label.
Step S20: urban crowd parking identification
Based on the urban resident trip job and residence separation phenomenon, the stay time-space characteristics of urban trip crowds are obtained through time-space threshold judgment by combining the criteria such as a mobile terminal signaling base transceiver station working mechanism from the trip chain angle.
S201: data smoothing
Fig. 3 is a schematic diagram illustrating data smoothing for signaling data of a mobile terminal according to an embodiment of the present invention. In theory, the acquisition of the spatial position of the mobile terminal user can form a complete travel chain, but because ping-pong switching phenomenon (that is, the mobile terminal user does not move and the uploaded data sequentially uses two or more surrounding base stations) occurs in the acquisition of the signaling data of the mobile terminal and noise data occurs in the data acquisition due to equipment precision, signal blocking and the like, the smoothing processing of the spatial data is necessary.
Obtaining the space position parameter (X) of the base station based on LAC (location area code, position area code) field of the mobile terminal signaling data and the mapping relation 1 ，Y 1 ) Spatial location parameters (X 1 ，Y 1 ) Smoothing is performed, and the processed spatial data (X 2 ，Y 2 ) And reversely carrying in base station data, and matching the base station data to the nearest base station position by using a look up function to obtain a two-dimensional 0-1 daily travel data matrix of urban residents with time as an abscissa and updated base station numbers as an ordinate, wherein the daily travel data matrix is single-day travel chain data. The data in the daily row data matrix takes the base station number as a division basis, the base stations represented by two adjacent rows have space connectivity, the column data takes the time granularity as a division basis, each column of data represents the result of expressing the space position according to the appointed time granularity, specifically, each column has only one non-null value, and the rest is null.
S202: stay identification
For the obtained daily travel chain data of urban residents, calculating the TIME difference delta t of each point position switching (occurrence event 4: position update) based on the TIME (TIME) field d The method comprises the steps of carrying out a first treatment on the surface of the According to the space distance difference S and time difference Deltat between base stations d And calculating the switching speed V of the mobile terminal, and identifying the stay behavior by taking the switching speed V as a criterion.
(1) If the speed V is greater than the set judgment threshold V i But the spatial distance difference S is smaller than the judgment threshold S j Is the stay behavior;
(2) If the speed V is greater than the judgment threshold V i And the space distance difference S is greater than the judgment threshold S j Not a stay behavior;
(3) If the speed V is smaller than the judgment threshold V i Is a stay;
multiple stay points may exist in a base station coverage area, and data combination is performed on the identified stay points by taking a month data sample size as a unit to obtain a month average single day stay time Deltat of a certain stay point in the base station coverage area b Frequency of stay c and stay position (cluster) (X 3 ，Y 3 )。
In the formula Deltat a Single day stop of a mobile terminal for a certain stop pointTime t ai The sum of the single-day residence time of all mobile terminals at a certain residence point, n represents the number of days of the month, deltat b The average single-day residence time of a certain residence point is month, alpha is a single-day weight factor, is influenced by factors such as working days, holidays and the like, and is not a fixed value.
If Deltat b Greater than a threshold t set by judgment k The recognition requirement of long-time parking in the coverage area of the base station is met, and the parking data of a certain parking point is recorded as A= (X) 3 ，Y 3 ，△t b ,c)。
Step S30: regional electric vehicle charging pile layout and charging power cooperation strategy based on trip stop points.
S301: available parking duration calculation
General data a= (X) of urban resident stay distribution in combination with step S20 3 ，Y 3 ，△t b And c), obtaining the proportion of the daily electric vehicle parking time length and different parking time lengths of the month average single point position in the coverage area of each base station.
△t c-min ≤△t c ≤△t c-max
Wherein a is the total daily electric vehicle travel total amount of a month average point of a certain stay point in the coverage area of the base station, b is the electric vehicle travel capacity of a collected mobile terminal signaling data sample of the certain stay point, and beta is a human mouth correction parameter, delta t c The monthly average single-point-position daily electric vehicle parking time length of a certain parking point in the coverage area of the base station is delta t c With upper limit requirement Deltat c-max (i.e. the single-cycle single-day charge time cannot be satisfied in unlimited amount) and the lower limit requirement Deltat c-min (i.e., consider the multi-day one-charge case).
The total daily electric vehicle travel total quantity a of the month average point in the coverage area of the base station can be obtained through big data statistics.
S302: charging demand calculation
And calculating the charging demands of different electric vehicles in the coverage areas of different base stations based on the average travel power consumption and the vehicle month-average accumulated mileage.
In which W is i For the charging demand, gamma is the correction factor,and L is the daily accumulated mileage, which is the average power consumption for traveling every kilometer.
S303: charge power calculation and charge mode coordination
According to the calculated charging power, the charging modes are adjusted and cooperated through the piecewise function in combination with the step electricity price, the related specification requirements, the charging will of the user and the like. The key of how the parking and the power cooperate is that the organization charging pile network takes portable slow charging as a main material, the charging pile is slow to charge as an auxiliary material to meet the charging requirements of most application scenes, the weight for adjusting the reliability coefficient can be considered by combining charging frequency c under the conditions of short parking time, multiple parking times, holidays, high-speed service areas and the like, the high-power fast charging channel is increased, the charging power required by single-day calculation is too small, and the charging pile is arranged by combining with SOC (state of charge) management.
For each electric vehicle in a certain stay point in the coverage area of the base station, calculating the daily charging power P of a month average single point position i
Wherein P is i Daily charging power for month average single point position, W i For charging demand, η is a reliability coefficient, Δt c And the average single-point-of-month electric vehicle parking time is the month average single-point-of-month electric vehicle parking time of a certain parking point in the coverage area of the base station. Equation (4) aims at taking into account the charge loss, season, by taking into account the charge demand and the available charge duration in combinationAnd controlling the difference, the charging operation time and other influencing factors through the reliability coefficient, and outputting to obtain a single-day charging power setting value which is more reasonable from the traffic characteristic level and the charging level of the electric vehicle.
Fig. 4 is a schematic diagram of a charging mode according to an embodiment of the present invention.
Daily charging power P according to month average single point position of electric vehicle i And the configuration information of the various charging modes, the charging mode which the electric vehicle should select at the stop point is selected, and the types, the powers and the numbers of the charging piles which the stop point should be provided with are determined according to the charging modes of all the electric vehicles in a certain stop point, for example, the proportion and the total number of the charging piles of different types such as portable slow charging, high-power fast charging and the like which are provided with a certain parking lot are determined.
According to the specification requirements of the standard NB/T33002-2010, the voltage connected to the charging pile network is used for making a standard criterion of a charging mode quantitative region. The optimization of the network layout of the charging piles and the cooperation of the charging power are reflected in that the method can process a large amount of data, and a charging mode which is more adaptive to a large amount of electric vehicles is obtained, so that the comprehensive electrodynamic performance of the motor vehicles is promoted, and the phenomena of mismatching of the charging piles and electric power resource waste are reduced.
In summary, the embodiment of the invention solves the problem of electric vehicle charging optimization under high conservation rate by utilizing the characteristic rule of the urban travel chain, utilizes the cooperation of the vehicle pile position network, the cooperation of the parking time and the charging power, and adjusts and separates the parking data of the base station area by combining the characteristic identification of the travel chain, and on the basis, the arrangement and the power cooperation of the charging piles are carried out, thereby being beneficial to the analysis and the grasp of the charging requirements of the urban electric vehicle, and relevant management and operation departments and the like can adjust the arrangement planning and the operation management of the charging piles in a targeted manner, better meet the endurance requirements of users of the electric vehicle, reduce the occurrence of the phenomenon of unbalanced space-time distribution of the charging piles, and agree with the trend of future large-scale traffic electric driving, thereby being beneficial to the comprehensive electric driving of the urban electric vehicle.
Aiming at the problem of mismatch of charging resources of new energy automobiles, the invention fully utilizes the travel chain characteristics of travel people and adjusts the network layout and the charging mode of the charging pile. According to the method, the charging pile network is managed, batch charging energy management and safety management can be achieved, on one hand, scheme planning of charging pile layout is provided, on the other hand, management of charging modes, utilization rate and reliability of the existing charging piles is improved, trough power resources of 4-5 hours are better utilized, and mismatch and waste of charging pile resources are avoided.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
From the above description of embodiments, it will be apparent to those skilled in the art that the present invention may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (6)
1. The utility model provides a charging pile network layout and power cooperation method based on traffic travel chain which is characterized by comprising the following steps:
collecting mobile terminal signaling data of an electric vehicle owner, performing data cleaning and data smoothing on the collected mobile terminal signaling data, and extracting traffic chain data of the mobile terminal;
identifying the stay behavior of the mobile terminal according to the traffic chain data of the mobile terminal, and calculating the total stay time of a single day of stay points in the coverage area of the base station;
calculating the monthly average single-point-position daily electric vehicle parking time of the parking points in the coverage area of the base station according to the proportion of the collected mobile terminal signaling data to the total signaling data and the total single-day parking time;
and calculating the monthly average single-point daily charging power of the electric vehicle according to the charging requirement of the electric vehicle and the monthly average single-point daily electric vehicle parking time of the stay points in the coverage area of the base station, determining the charging mode of the electric vehicle, and determining the power and the quantity of charging piles which should be distributed by the stay points according to the charging modes of all the electric vehicles in the stay points.
2. The method of claim 1, wherein the step of collecting the signaling data of the mobile terminal of the owner of the electric vehicle, performing data cleaning and data smoothing on the collected signaling data of the mobile terminal, and extracting the traffic chain data of the mobile terminal comprises the steps of:
collecting mobile terminal signaling data of an electric vehicle owner, performing data cleaning and data tag processing on the collected mobile terminal signaling data, and obtaining a spatial position parameter (X) of a base station based on a LAC position area code field and a mapping relation of the mobile terminal signaling data 1 ，Y 1 ) Spatial location parameters (X 1 ，Y 1 ) Smoothing is performed, and the processed spatial data (X 2 ，Y 2 ) And reversely carrying in base station data, matching the base station data to the nearest base station position by using a look up function, obtaining a two-dimensional 0-1 daily travel data matrix of urban residents with time as an abscissa and the updated base station number as an ordinate, and taking the daily travel data matrix as single-day travel chain data of the mobile terminal.
3. The method according to claim 1 or 2, wherein the step of identifying the stay behavior of the mobile terminal according to the traffic chain data of the mobile terminal, and calculating the total stay time of the stay point in the coverage area of the base station for a single day comprises the steps of:
calculating the TIME difference delta t of switching of each point position of the mobile terminal based on the TIME TIME field in the single-day trip chain data of the mobile terminal of the electric vehicle owner d The method comprises the steps of carrying out a first treatment on the surface of the According to the space distance difference S and time difference Deltat between base stations d Calculating to obtain the switching speed V of the mobile terminal;
(1) If the switching speed V is greater than the judgment threshold V i But the spatial distance difference S is smaller than the judgment threshold S j Judging that the mobile terminal is in the stay behavior at the point position;
(2) If the switching speed V is greater than the judgment threshold V i And the space distance difference S is greater than the judgment threshold S j Judging that the mobile terminal does not stay at the point position;
(3) If the switching speed V is smaller than the judgment threshold V i Judging that the mobile terminal is in the stay behavior at the point position;
combining the data of the residence time of all the mobile terminals by taking the month data sample size as a unit to obtain the month average single-day residence time delta t of a certain residence point in the coverage area of a base station b Frequency of stay c and position of stay (X 3 ，Y 3 )；
In the formula Deltat a Single day dwell time, t, for a mobile terminal at a dwell point ai The sum of the single-day stay times of all mobile terminals at a certain stay point, n represents the number of days of the month, Δt b The average single-day residence time of a month is a certain residence point, and alpha is a single-day weight factor;
if Deltat b Greater than a threshold t set by judgment k The recognition requirement of long-time parking in the coverage area of the base station is met, and the parking data of a certain parking point is recorded as A= (X) 3 ，Y 3 ，△t b ,c)。
4. The method of claim 3, wherein calculating a month average single-point-location daily electric vehicle parking duration of the parking points in the coverage area of the base station according to the proportion of the collected mobile terminal signaling data to the total signaling data and the total single-day parking residence time comprises:
dwell data based on a certain dwell point in the coverage area of a base station is a= (X) 3 ，Y 3 ，△t b C) calculating to obtain the month average single-point position daily electric vehicle parking time delta t of a certain stay point in the coverage area of the base station c ；
Wherein a is the total daily electric vehicle travel total amount at a month average point of a certain stay point in the coverage area of the base station, and b is a certain collected travel total amountElectric vehicle travel capacity of mobile terminal signaling data sample of stay point, beta is human mouth correction parameter, delta t c The monthly average single-point-position daily electric vehicle parking time length of a certain parking point in the coverage area of the base station is delta t c-max Represents the maximum value of the charging time of a bicycle on a single day, delta t c-min Indicating that the charging behaviour is not taken into account when the dwell time is below a certain threshold.
5. The method of claim 4, wherein the calculating the monthly average single-point daily charging power of the electric vehicle according to the charging requirement of the electric vehicle and the monthly average single-point daily electric vehicle parking time of the parking points in the coverage area of the base station, determining the charging mode of the electric vehicle, and determining the power and the number of charging piles to be arranged at the parking points according to the charging modes of all electric vehicles in the parking points comprises:
calculating the charging demand of the electric vehicle based on the average travel power consumption of the electric vehicle and the accumulated mileage of the vehicle month;
in which W is i For the charging demand, gamma is the correction factor,the average power consumption of the electric vehicle per kilometer is calculated, and L is the average daily accumulated mileage of the electric vehicle;
for each electric vehicle in a certain stay point in the coverage area of the base station, calculating the daily charging power P of a month average single point position i
Wherein P is i Daily charging power for month average single point position, W i For charging demand, η is a reliability coefficient, Δt c The method comprises the steps of averaging the time length of parking of the single-point electric vehicle every day for a month of a certain parking point in the coverage area of a base station;
daily charging power P according to month average single point position of electric vehicle i And configuration information of various charging modes, selecting a charging mode which the electric vehicle should select at a stop point, and determining the types, the powers and the quantity of the charging piles which should be distributed at the stop point according to the charging modes of all the electric vehicles in a certain stop point.
6. The method of claim 5, wherein the charging mode of the electric vehicle comprises:
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