CN111967698A - Electric automobile charging system and device based on mobile charging pile scheduling - Google Patents

Electric automobile charging system and device based on mobile charging pile scheduling Download PDF

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Publication number
CN111967698A
CN111967698A CN202011143097.3A CN202011143097A CN111967698A CN 111967698 A CN111967698 A CN 111967698A CN 202011143097 A CN202011143097 A CN 202011143097A CN 111967698 A CN111967698 A CN 111967698A
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charging
charging pile
scheduling
mobile
model
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CN111967698B (en
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张冰洁
刘峰
杨俊强
刘然
高洋
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Beijing Guoxin Intelligent Power New Energy Technology Co ltd
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    • 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"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/66—Data transfer between charging stations and vehicles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • 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
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
    • 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

Abstract

The invention belongs to the technical field of electric vehicle charging, and particularly relates to an electric vehicle charging system and device based on mobile charging pile scheduling, aiming at solving the problems that the conventional mobile charging pile scheduling method is small in scheduling range, charging cars cannot be found, and the charging cars are inconvenient to recover and maintain, so that the electric vehicles are inconvenient to charge. The invention comprises the following steps: the information interaction unit acquires charging request/reservation/charging completion information; the data acquisition unit is used for acquiring the current charging demand, the current mobile and fixed charging pile distribution diagram and the residual electric quantity information of each mobile charging pile; a charging demand prediction unit that predicts a charging demand for a next time period; the charging pile scheduling unit generates a charging demand prediction distribution map and schedules the charging piles by combining the data of the data acquisition unit; and the charging unit is used for selecting the optimal charging pile to charge the electric automobile. The method and the system have the advantages that the mobile charging pile is dispatched in advance on the basis of predicting the charging demand, and the electric automobile is high in charging efficiency, high in accuracy and good in instantaneity.

Description

Electric automobile charging system and device based on mobile charging pile scheduling
Technical Field
The invention belongs to the technical field of electric automobile charging, and particularly relates to an electric automobile charging system and device based on mobile charging pile scheduling.
Background
In the face of global energy shortage, increasingly severe environmental pollution and continuously improved requirements on energy conservation and emission reduction, new energy automobiles are greatly developed. In recent years, the number of new energy electric vehicles in China is continuously increased, when the urban charging pile reaches a certain order of magnitude, the impact of the urban charging pile on a power grid is huge, particularly, the electric vehicles have a certain rule in charging, the electric vehicles are charged in the first half part of the night and overlapped with the urban power consumption peak, the burden on the power grid is the heaviest, and most of the electric vehicles are charged after the urban power consumption peak in the second half night is passed. The charging has serious charging disorder, so that the urban power supply pressure is increased while the resource waste is caused.
However, the construction speed of the charging pile in China is far behind the sale speed of the electric automobile, and the daily charging requirement of the electric automobile cannot be completely met. In addition, the existing charging service system is not perfect enough, the existing charging pile cannot completely realize intelligent charging, most of the charging piles adopt a manpower service mode, a large amount of manpower and material resources are consumed, meanwhile, charging stations are scattered, managers cannot well play a role in management, and the management difficulty is very high.
The appearance that fills electric pile has been alleviated to a certain extent the contradiction between the demand that charges of increasing electric automobile and the electric wire netting burden to a certain extent to the removal, but present removal fills electric pile, adopt to fill electric pile and integrative storage battery car of car and set up the management backstage, be applicable to the district parking area, small range region such as market parking area, storage battery car quantity is not many, the power transmission person probably is security personnel or property staff, and storage battery car does not set up positioner, the management backstage acquires electric automobile's detailed position through parking stall serial number etc. in the charging request of customer end, contact the power transmission person and deliver, this kind of charging system has following shortcoming: 1. the charging vehicle has limited transportation distance, small application range and limitation, and cannot be popularized in a large range; 2. a special charging network cannot be established in a cell or a market, so that the storage cost of the charging vehicle is high; 3. the charging car is not provided with a positioning device, the background judges the charging address through the parking space number uploaded by the client, the rearranged power transmitter moves the charging car to the charging address, the scheduling mode is simple, but the situation that the charging car cannot be found exists, and the charging car is inconvenient to recover, maintain and manage. Therefore, the invention provides an electric vehicle charging system based on mobile charging pile scheduling to solve the problems.
Disclosure of Invention
In order to solve the problems in the prior art, namely the problems that the scheduling range of the conventional mobile charging pile scheduling method is small, a charging car cannot be found, and the charging of an electric vehicle is inconvenient due to inconvenience in recovery and maintenance, the invention provides an electric vehicle charging system based on mobile charging pile scheduling, which comprises the following units:
the information interaction unit is used for submitting a charging request or charging reservation information to the charging pile scheduling management center through the wireless network by the electric automobile; the charging management center is also used for submitting charging completion information to the charging pile scheduling management center through a wireless network;
the mobile charging scheduling unit is used for predicting charging requirements of the mobile charging scheduling area and scheduling the mobile charging pile; the mobile charging pile distribution diagram, the residual electric quantity information of each mobile charging pile and the current fixed charging pile distribution diagram of the area to be processed are also obtained;
the charging demand prediction unit is used for acquiring the predicted charging demand of the next time period through a trained charging demand prediction model based on a neural network based on the current charging demand of the area to be processed;
the charging pile scheduling unit is used for generating a charging demand prediction distribution map of the next time period based on the predicted charging demand of the next time period, and combining the mobile charging pile distribution map, the residual electric quantity information of each mobile charging pile and the mobile charging pile scheduling tool information to carry out mobile charging pile scheduling task grouping and scheduling path planning and mobile charging pile scheduling;
and the charging unit is used for selecting the optimal charging pile by combining the mobile charging pile and the fixed charging pile distribution map after scheduling according to the charging request or the charging reservation information submitted by the information interaction unit, entering a charging pile parking space for charging if the optimal charging pile is the fixed charging pile, planning a path between the electric automobile and the optimal charging pile if the optimal charging pile is the mobile charging pile, and moving the mobile charging pile to the electric automobile parking space for charging.
In some preferred embodiments, the charging pile scheduling unit includes the following modules:
the prediction distribution map generation module is used for generating a charging demand prediction distribution map of the next time period based on the predicted charging demand of the next time period;
the scheduling task generating module is used for comparing the charging demand prediction distribution map with the fixed charging pile distribution map and generating a scheduling starting point and a scheduling terminal point of each mobile charging pile by combining the mobile charging pile distribution map and the residual electric quantity information of each mobile charging pile;
the grouping and path planning module is used for acquiring the information of the mobile charging pile scheduling tool and combining the scheduling start point and the scheduling end point of each mobile charging pile to carry out grouping and scheduling path planning on the scheduling tasks of the mobile charging piles;
and the scheduling module is used for scheduling the mobile charging piles based on the mobile charging pile scheduling task groups and the scheduling path planning result.
In some preferred embodiments, the charging pile scheduling unit further includes a screening module;
and the screening module is used for acquiring the screened electric quantity threshold value of each mobile charging pile by combining the charging demand prediction distribution map of the next time period, removing the mobile charging piles lower than the threshold value, and updating the mobile charging pile distribution map through the rest mobile charging piles.
In some preferred embodiments, the training process of the neural network-based charging demand prediction model includes:
step B10, dividing the area to be processed into grids with different sizes; setting an activation function, a loss function and a cost function of the model;
step B20, acquiring mobile charging pile information, fixed charging pile information and electric vehicle charging requirements corresponding to each grid of a set historical time period as a model training data set, and acquiring corresponding weather forecast information and holiday information as a model auxiliary training data set;
step B30, respectively carrying out preprocessing on the training data set and the auxiliary training data set to obtain a preprocessed training data set and an auxiliary training data set;
step B40, setting an activation function, a loss function and a cost function of the charging demand prediction model, taking a batch of data in the preprocessing training data set and the auxiliary training data set as model input, and calculating the loss function and the cost function value by forward propagation from an input layer of the model to a hidden layer;
step B50, judging whether the loss function meets the requirement for error and the cost function meets the requirement for cost, and jumping to step B60 if both the loss function and the cost function meet the requirement for cost; otherwise, the model weight matrix is subjected to gradient descent method
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And a bias matrix
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Making corrections and jumping to step B40 using the newly calculated weight matrix
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Selecting a new batch of data for recalculation;
step B60, the weight matrix obtained by training is used
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And a bias matrix
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And applying the model to obtain a trained charging demand prediction model based on the neural network.
In some preferred embodiments, the number of model hidden layer nodes in the process of building the charging demand prediction model based on the neural network is obtained by the following formula:
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wherein the content of the first and second substances,
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representing the number of nodes of the sought model hidden layer,
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and
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representing the number of neurons in the input and output layers of the model respectively,
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representing the number of samples of the model training,
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、
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is a preset variable constant.
In some preferred embodiments, step B30 includes:
step B31, carrying out random sampling on the training data set and the auxiliary training data set through Monte Carlo to obtain sampling data;
step B32, normalizing the sampling data to obtain a pre-processing training data set and an auxiliary training data set; the calculation formula of the normalization process is as follows:
Figure 523471DEST_PATH_IMAGE010
wherein the content of the first and second substances,
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and
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respectively representing the second of the normalized and pre-normalized data sets
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The number of the data is one,
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and
Figure 295032DEST_PATH_IMAGE015
representing the maximum and minimum values of data in the data set, respectively.
In some preferred embodiments, the activation function of the charge demand prediction model is:
Figure 526293DEST_PATH_IMAGE016
wherein the content of the first and second substances,
Figure 176717DEST_PATH_IMAGE017
，
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is a transpose of the model weight matrix,
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in order to model the bias matrix of the model,
Figure 520739DEST_PATH_IMAGE011
is the first in the normalized data set
Figure 25670DEST_PATH_IMAGE013
A piece of data;
Figure 386244DEST_PATH_IMAGE019
and
Figure 242204DEST_PATH_IMAGE020
is a hyper-parameter.
In some preferred embodiments, the cost function of the charge demand prediction model is:
Figure 64536DEST_PATH_IMAGE021
wherein the content of the first and second substances,
Figure 689552DEST_PATH_IMAGE001
and
Figure 955449DEST_PATH_IMAGE002
respectively a weight matrix and a bias matrix of the model,
Figure 298705DEST_PATH_IMAGE022
is the total amount of data in the data set,
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to pass the data through the predicted values obtained by the model,
Figure 171294DEST_PATH_IMAGE024
is true of data correspondenceAnd (4) real value.
In another aspect of the present invention, a storage device is provided, in which a plurality of programs are stored, and the programs are suitable for being loaded and executed by a processor to implement the above-mentioned electric vehicle charging system based on mobile charging pile scheduling.
In a third aspect of the present invention, a processing apparatus is provided, which includes a processor, a storage device; the processor is suitable for executing various programs; the storage device is suitable for storing a plurality of programs; the program is suitable for being loaded and executed by a processor to realize the electric vehicle charging system based on the mobile charging pile scheduling.
The invention has the beneficial effects that:
(1) according to the electric vehicle charging system based on the mobile charging pile scheduling, the mobile charging piles with lower electric quantity in the obtained mobile charging piles are removed by combining the distribution information of the existing fixed charging piles based on the obtained predicted charging demand of the area to be measured, the rest mobile charging piles are distributed to corresponding positions, and task grouping and path planning are combined, so that the charging pile scheduling efficiency is high, the accuracy is high, and the electric vehicle charging efficiency is further improved.
(2) According to the electric vehicle charging system based on the mobile charging pile scheduling, when the charging requirement is predicted, the mobile charging pile information, the fixed charging pile information and the electric vehicle charging requirement of a historical time period are used as training data of a model, corresponding weather forecast information and holiday information are used as auxiliary data of model training, and the obtained model is high in precision and accuracy when the electric vehicle charging requirement of an area to be predicted is predicted, so that the precision and accuracy of the charging pile scheduling are improved, and the precision and accuracy of an electric vehicle charging execution result are further improved.
(3) According to the electric vehicle charging system based on the mobile charging pile scheduling, when the charging requirement is predicted, the region to be measured is divided into grids of different sizes according to the historical charging requirement distribution, the grids are smaller in places where the historical charging requirement distribution is dense, and the grids are larger in places where the historical charging requirement distribution is sparse, on the premise that the calculation speed is guaranteed, the calculation precision is further improved, the model can be applied to occasions with higher requirements for real-time performance, and the precision, the accuracy and the efficiency of the electric vehicle charging execution result are further improved.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
fig. 1 is a schematic diagram of a framework of an electric vehicle charging system based on mobile charging pile scheduling according to the present invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The invention discloses an electric vehicle charging system based on mobile charging pile scheduling, which comprises the following units:
the information interaction unit is used for submitting a charging request or charging reservation information to the charging pile scheduling management center through the wireless network by the electric automobile; the charging management center is also used for submitting charging completion information to the charging pile scheduling management center through a wireless network;
the mobile charging scheduling unit is used for predicting charging requirements of the mobile charging scheduling area and scheduling the mobile charging pile; the mobile charging pile distribution diagram, the residual electric quantity information of each mobile charging pile and the current fixed charging pile distribution diagram of the area to be processed are also obtained;
the charging demand prediction unit is used for acquiring the predicted charging demand of the next time period through a trained charging demand prediction model based on a neural network based on the current charging demand of the area to be processed;
the charging pile scheduling unit is used for generating a charging demand prediction distribution map of the next time period based on the predicted charging demand of the next time period, and combining the mobile charging pile distribution map, the residual electric quantity information of each mobile charging pile and the mobile charging pile scheduling tool information to carry out mobile charging pile scheduling task grouping and scheduling path planning and mobile charging pile scheduling;
and the charging unit is used for selecting the optimal charging pile by combining the mobile charging pile and the fixed charging pile distribution map after scheduling according to the charging request or the charging reservation information submitted by the information interaction unit, entering a charging pile parking space for charging if the optimal charging pile is the fixed charging pile, planning a path between the electric automobile and the optimal charging pile if the optimal charging pile is the mobile charging pile, and moving the mobile charging pile to the electric automobile parking space for charging.
In order to more clearly describe the electric vehicle charging system based on mobile charging pile scheduling, the modules in the embodiment of the present invention are described in detail below with reference to fig. 1.
The electric vehicle charging system based on mobile charging pile scheduling comprises an information interaction unit, a data acquisition unit, a charging demand prediction unit, a charging pile scheduling unit and a charging unit, wherein the modules are described in detail as follows:
the information interaction unit is used for submitting a charging request or charging reservation information to the charging pile scheduling management center through the wireless network by the electric automobile; and the charging completion information is submitted to a charging pile dispatching management center through a wireless network.
The mobile charging scheduling unit is used for predicting charging requirements of the mobile charging scheduling area and scheduling the mobile charging pile; and the mobile charging pile distribution diagram, the residual electric quantity information of each mobile charging pile and the current fixed charging pile distribution diagram of the area to be processed are also obtained.
And the charging demand prediction unit is used for acquiring the predicted charging demand of the next time period through a trained charging demand prediction model based on the neural network based on the current charging demand of the area to be processed.
The charging demand prediction model based on the neural network comprises the following training processes:
step B10, dividing the area to be processed into grids with different sizes; an activation function, a loss function, and a cost function of the model are set.
The method comprises the steps of dividing a region to be processed into grids with different sizes, obtaining data representing the grids by averaging the data in the grids, wherein the size of the grids directly influences the precision and speed of subsequent calculation, when the grids are large, the calculation speed is high, but the precision is reduced, when the grids are small, the precision is high, but the calculation speed is low, and the real-time performance of the algorithm is difficult to guarantee.
The method has the advantages that the regions to be processed are divided into networks with different sizes according to historical charging requirements, the networks are dense in the historical charging requirements, the grids are small, the networks are sparse in the historical charging requirements, the grids are large, on the premise that the calculation speed is guaranteed, the calculation accuracy is further improved, and the model can be applied to occasions with high real-time requirements.
In one embodiment of the present invention, based on the charging demand, the self-adaptive size of the grid is adjusted by a clustering method, and the specific process is as follows:
firstly, acquiring charging demands of a set historical time period of an area to be predicted, and acquiring a charging demand point set by taking the position of each charging demand as a charging demand point;
then, setting a grid region charging demand point threshold value after grid division;
then, dividing the charging demand point set into K clustering clusters by a K-means clustering method, wherein the number of the charging demand points in each clustering cluster is not more than the threshold of the charging demand points;
and finally, taking the edges of each cluster as the edges of the grids, and performing fusion of the non-coincident edges to complete regional grid division.
And fusing the non-coincident edge lines, namely taking line segments formed by two cross points of the non-coincident edge lines as the fused edge lines.
In another embodiment of the present invention, the mesh adaptive size is adjusted by a mesh splitting and merging method, which specifically comprises the following steps:
firstly, acquiring charging demands of a set historical time period of an area to be predicted, and acquiring a charging demand point set by taking the position of each charging demand as a charging demand point;
then, setting an upper threshold and a lower threshold of a grid region charging demand point after grid division;
then, averagely dividing the area to be predicted into grids with set sizes, judging the number of charging demand points in the current grid and executing the following steps:
if the number of the charging demand points in the current grid is larger than the upper limit threshold, averagely splitting the current grid into two sub-grids with the same number of the charging demand points (or with the number difference of 1) according to the positions of the charging demand points, and iteratively judging the number of the charging demand points in the split sub-grids and splitting the sub-grids until the number of the charging demand points in each sub-grid after splitting the current grid is not larger than the upper limit threshold;
if the number of the charging demand points in the current grid is greater than or equal to a lower threshold and less than or equal to an upper threshold, reserving the current grid;
if the number of the charging demand points in the current grid is smaller than the lower limit threshold, acquiring each adjacent grid of the current grid, respectively judging whether the sum of the charging demand points of the current grid and each adjacent grid is between the upper limit threshold and the lower limit threshold, selecting the adjacent grid between the upper limit threshold and the lower limit threshold to be combined with the current grid, and if the sum is smaller than the lower limit threshold, iteratively judging and combining until the number of the charging demand points in the grid is between the upper limit threshold and the lower limit threshold;
after the grids are split and combined, the number of the charging demand points of each grid area is between the upper limit threshold and the lower limit threshold, and the area grid division is completed.
In other embodiments, other methods may be selected for grid adaptive adjustment according to needs, for example, a probability distribution method, a random forest, a decision tree, and the like, which are not described in detail herein.
In the process of building a charging demand prediction model based on a neural network, the number of nodes of a hidden layer of the model can be obtained by the following formula (1):
Figure 608092DEST_PATH_IMAGE025
wherein the content of the first and second substances,
Figure 438645DEST_PATH_IMAGE004
representing the number of nodes of the sought model hidden layer,
Figure 353511DEST_PATH_IMAGE005
and
Figure 202387DEST_PATH_IMAGE006
representing the number of neurons in the input and output layers of the model respectively,
Figure 75665DEST_PATH_IMAGE007
representing the number of samples of the model training,
Figure 127935DEST_PATH_IMAGE008
、
Figure 846492DEST_PATH_IMAGE009
is a preset variable constant.
In one embodiment of the present invention, the predetermined variable constant is
Figure 300607DEST_PATH_IMAGE008
In the range of [2-10]Predetermined variable constant
Figure 829940DEST_PATH_IMAGE009
In the range of [2-10]。
The number of nodes of the hidden layer of the model can be obtained by other methods, such as any one of the calculation methods in the formulas (2) to (6), and the calculation method for obtaining the number of nodes of the hidden layer of the optimal model in each calculation method can also be obtained by a genetic algorithm, a particle swarm optimization algorithm and the like.
Figure 635085DEST_PATH_IMAGE026
Figure 157333DEST_PATH_IMAGE027
Figure 465955DEST_PATH_IMAGE028
Figure 664724DEST_PATH_IMAGE029
Figure 426006DEST_PATH_IMAGE030
Wherein the content of the first and second substances,
Figure 17525DEST_PATH_IMAGE031
representing the number of nodes of the sought model hidden layer,
Figure 180653DEST_PATH_IMAGE032
and
Figure 301056DEST_PATH_IMAGE033
representing the number of nodes of the model input layer and output layer respectively,
Figure 565946DEST_PATH_IMAGE034
which represents the operation of square root calculation,
Figure 695576DEST_PATH_IMAGE035
is a preset variable constant with the value range of [1-10 ]]。
The activation function of the charging demand prediction model is shown in equation (7):
Figure 713211DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure 270094DEST_PATH_IMAGE017
，
Figure 255236DEST_PATH_IMAGE037
is a transpose of the model weight matrix,
Figure 922978DEST_PATH_IMAGE002
in order to model the bias matrix of the model,
Figure 60698DEST_PATH_IMAGE011
is the first in the normalized data set
Figure 788483DEST_PATH_IMAGE013
A piece of data;
Figure 746074DEST_PATH_IMAGE019
and
Figure 968240DEST_PATH_IMAGE020
is a hyper-parameter.
In one embodiment of the invention, the hyper-parameter
Figure 226046DEST_PATH_IMAGE019
1.0507, hyperparameter
Figure 859152DEST_PATH_IMAGE020
Is 1.67326.
The cost function of the charge demand prediction model is shown in equation (8):
Figure 569619DEST_PATH_IMAGE039
wherein the content of the first and second substances,
Figure 828431DEST_PATH_IMAGE001
and
Figure 940744DEST_PATH_IMAGE002
respectively a weight matrix and a bias matrix of the model,
Figure 10331DEST_PATH_IMAGE022
is the total amount of data in the data set,
Figure 942515DEST_PATH_IMAGE024
to pass the data through the predicted values obtained by the model,
Figure 755750DEST_PATH_IMAGE024
the actual value corresponding to the data.
And step B20, acquiring mobile charging pile information, fixed charging pile information and electric vehicle charging requirements corresponding to each grid of a set historical time period as a model training data set, and acquiring corresponding weather forecast information and holiday information as a model auxiliary training data set.
The weather forecast information and the holiday information contained in the auxiliary training data are used for training the auxiliary model, for example, when the weather is good or the holiday is holiday, the traffic is greatly increased, and the charging demand is greatly increased.
And step B30, respectively carrying out preprocessing on the training data set and the auxiliary training data set to obtain a preprocessed training data set and an auxiliary training data set.
And step B31, randomly sampling the training data set and the auxiliary training data set through Monte Carlo to obtain sampling data.
Monte Carlo random sampling can convert large data types and large data quantities into representative data quantities which can meet the requirement of neural network training.
Step B32, normalizing the sampling data to obtain a pre-processing training data set and an auxiliary training data set; the calculation formula of the normalization process is shown in formula (9):
Figure 473301DEST_PATH_IMAGE040
wherein the content of the first and second substances,
Figure 713790DEST_PATH_IMAGE011
and
Figure 398849DEST_PATH_IMAGE012
respectively representing the second of the normalized and pre-normalized data sets
Figure 750196DEST_PATH_IMAGE013
The number of the data is one,
Figure 820789DEST_PATH_IMAGE014
and
Figure 966600DEST_PATH_IMAGE015
representing the maximum and minimum values of data in the data set, respectively.
And step B40, setting an activation function, a loss function and a cost function of the charging demand prediction model, taking a batch of data in the preprocessing training data set and the auxiliary training data set as model input, and calculating the loss function and the cost function value by forward propagation from an input layer of the model to a hidden layer.
Step B50, judging whether the loss function meets the requirement for error and the cost function meets the requirement for cost, and jumping to step B60 if both the loss function and the cost function meet the requirement for cost; otherwise, the model weight matrix is subjected to gradient descent method
Figure 873376DEST_PATH_IMAGE001
And a bias matrix
Figure 293993DEST_PATH_IMAGE002
Making corrections and jumping to step B40 using the newly calculated weight matrix
Figure 969825DEST_PATH_IMAGE001
Select a new batchAccording to the calculation again.
Weight matrix
Figure 302848DEST_PATH_IMAGE001
The correction method (2) is represented by the following formula (10):
Figure 962500DEST_PATH_IMAGE041
wherein the content of the first and second substances,
Figure 655649DEST_PATH_IMAGE042
in order to obtain a learning rate,
Figure 451567DEST_PATH_IMAGE043
is a loss function of the model.
In one embodiment of the invention, the learning rate
Figure 454027DEST_PATH_IMAGE042
Is 0.05.
The loss function of the present invention is shown in equation (11):
Figure 69816DEST_PATH_IMAGE044
wherein the content of the first and second substances,
Figure 832236DEST_PATH_IMAGE045
to pass the data through the predicted values obtained by the model,
Figure 482660DEST_PATH_IMAGE046
the actual value corresponding to the data.
Step B60, the weight matrix obtained by training is used
Figure 406753DEST_PATH_IMAGE001
And a bias matrix
Figure 537869DEST_PATH_IMAGE002
Applied to models to obtain trained neural network-basedA charging demand prediction model.
And the charging pile scheduling unit is used for generating a charging demand prediction distribution map of the next time period based on the predicted charging demand of the next time period, and performing scheduling task grouping and scheduling path planning of the mobile charging piles and scheduling of the mobile charging piles by combining the mobile charging pile distribution map, the residual electric quantity information of each mobile charging pile and the scheduling tool information of the mobile charging piles.
Fill electric pile dispatch unit and include the following module:
the prediction distribution map generation module is used for generating a charging demand prediction distribution map of the next time period based on the predicted charging demand of the next time period;
the scheduling task generating module is used for comparing the charging demand prediction distribution map with the fixed charging pile distribution map and generating a scheduling starting point and a scheduling terminal point of each mobile charging pile by combining the mobile charging pile distribution map and the residual electric quantity information of each mobile charging pile;
the grouping and path planning module is used for acquiring the information of the mobile charging pile scheduling tool and combining the scheduling start point and the scheduling end point of each mobile charging pile to carry out grouping and scheduling path planning on the scheduling tasks of the mobile charging piles;
and the scheduling module is used for scheduling the mobile charging piles based on the mobile charging pile scheduling task groups and the scheduling path planning result.
The charging pile scheduling unit also comprises a screening module;
and the screening module is used for acquiring the screened electric quantity threshold of each mobile charging pile by combining the charging demand prediction distribution map of the next time period, removing the mobile charging piles lower than the threshold, and updating the mobile charging pile distribution map through the rest mobile charging piles.
And the charging unit is used for selecting the optimal charging pile by combining the mobile charging pile and the fixed charging pile distribution map after scheduling according to the charging request or the charging reservation information submitted by the information interaction unit, entering a charging pile parking space for charging if the optimal charging pile is the fixed charging pile, planning a path between the electric automobile and the optimal charging pile if the optimal charging pile is the mobile charging pile, and moving the mobile charging pile to the electric automobile parking space for charging.
According to the method, on the basis of charging demand prediction, the current fixed charging piles and the distribution of the mobile charging piles are combined, the mobile charging piles are scheduled, the mobile charging piles can be arranged in a charging place with corresponding demands in advance, for example, the charging demand predicted in the next day of an underground parking lot is 30 cars to be charged, the parking lot is only provided with 20 fixed charging piles, the nearby mobile charging piles are transferred in advance through a scheduling tool by a scheduling method of the mobile charging piles (for example, the mobile charging piles are transported through a truck, an electric ferry vehicle and the like), and the charging demands of the electric cars are met in the next day. Because the quantity, the distribution places and the residual electric quantity of the mobile charging piles can influence the automobile charging, scheduling task grouping and path planning of the mobile charging piles are carried out by combining the information, scheduling tools are reasonably arranged, the optimal matching of the charging demand and the charging piles is realized, the scheduling precision, the accuracy and the scheduling efficiency of the mobile charging piles are improved, and the method can also be applied to occasions with higher real-time requirements.
A storage device according to a second embodiment of the present invention stores a plurality of programs, and the programs are suitable for being loaded and executed by a processor to realize the above-mentioned electric vehicle charging system based on mobile charging pile scheduling.
A processing apparatus according to a third embodiment of the present invention includes a processor, a storage device; a processor adapted to execute various programs; a storage device adapted to store a plurality of programs; the program is suitable for being loaded and executed by a processor to realize the electric vehicle charging system based on the mobile charging pile scheduling.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative modules, method steps, and modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.

Claims (10)

1. The utility model provides an electric automobile charging system based on electric pile dispatch that fills removes which characterized in that, this system includes the following unit:
the information interaction unit is used for submitting a charging request or charging reservation information to the charging pile scheduling management center through the wireless network by the electric automobile; the charging management center is also used for submitting charging completion information to the charging pile scheduling management center through a wireless network;
the mobile charging scheduling unit is used for predicting charging requirements of the mobile charging scheduling area and scheduling the mobile charging pile; the mobile charging pile distribution diagram, the residual electric quantity information of each mobile charging pile and the current fixed charging pile distribution diagram of the area to be processed are also obtained;
the charging demand prediction unit is used for acquiring the predicted charging demand of the next time period through a trained charging demand prediction model based on a neural network based on the current charging demand of the area to be processed;
the charging pile scheduling unit is used for generating a charging demand prediction distribution map of the next time period based on the predicted charging demand of the next time period, and combining the mobile charging pile distribution map, the residual electric quantity information of each mobile charging pile and the mobile charging pile scheduling tool information to carry out mobile charging pile scheduling task grouping and scheduling path planning and mobile charging pile scheduling;
and the charging unit is used for selecting the optimal charging pile by combining the mobile charging pile and the fixed charging pile distribution map after scheduling according to the charging request or the charging reservation information submitted by the information interaction unit, entering a charging pile parking space for charging if the optimal charging pile is the fixed charging pile, planning a path between the electric automobile and the optimal charging pile if the optimal charging pile is the mobile charging pile, and moving the mobile charging pile to the electric automobile parking space for charging.
2. The electric vehicle charging system based on mobile charging pile scheduling of claim 1, wherein the charging pile scheduling unit comprises the following modules:
the prediction distribution map generation module is used for generating a charging demand prediction distribution map of the next time period based on the predicted charging demand of the next time period;
the scheduling task generating module is used for comparing the charging demand prediction distribution map with the fixed charging pile distribution map and generating a scheduling starting point and a scheduling terminal point of each mobile charging pile by combining the mobile charging pile distribution map and the residual electric quantity information of each mobile charging pile;
the grouping and path planning module is used for acquiring the information of the mobile charging pile scheduling tool and combining the scheduling start point and the scheduling end point of each mobile charging pile to carry out grouping and scheduling path planning on the scheduling tasks of the mobile charging piles;
and the scheduling module is used for scheduling the mobile charging piles based on the mobile charging pile scheduling task groups and the scheduling path planning result.
3. The electric vehicle charging system based on mobile charging pile scheduling of claim 2, wherein the charging pile scheduling unit further comprises a screening module;
and the screening module is used for acquiring the screened electric quantity threshold value of each mobile charging pile by combining the charging demand prediction distribution map of the next time period, removing the mobile charging piles lower than the threshold value, and updating the mobile charging pile distribution map through the rest mobile charging piles.
4. The electric vehicle charging system based on mobile charging pile scheduling of claim 2, wherein the neural network-based charging demand prediction model is trained by the following steps:
step B10, dividing the area to be processed into grids with different sizes; setting an activation function, a loss function and a cost function of the model;
step B20, acquiring mobile charging pile information, fixed charging pile information and electric vehicle charging requirements corresponding to each grid of a set historical time period as a model training data set, and acquiring corresponding weather forecast information and holiday information as a model auxiliary training data set;
step B30, respectively carrying out preprocessing on the training data set and the auxiliary training data set to obtain a preprocessed training data set and an auxiliary training data set;
step B40, setting an activation function, a loss function and a cost function of the charging demand prediction model, taking a batch of data in the preprocessing training data set and the auxiliary training data set as model input, and calculating the loss function and the cost function value by forward propagation from an input layer of the model to a hidden layer;
step B50, judging whether the loss function meets the requirement for error and the cost function meets the requirement for cost, and jumping to step B60 if both the loss function and the cost function meet the requirement for cost; otherwise, the model weight matrix is subjected to gradient descent method
Figure 322582DEST_PATH_IMAGE001
And a bias matrix
Figure 911827DEST_PATH_IMAGE002
Making corrections and jumping to step B40 using the newly calculated weight matrix
Figure 160405DEST_PATH_IMAGE001
Selecting a new batch of data for recalculation;
step B60, the weight matrix obtained by training is used
Figure 24456DEST_PATH_IMAGE001
And a bias matrix
Figure 307670DEST_PATH_IMAGE002
And applying the model to obtain a trained charging demand prediction model based on the neural network.
5. The electric vehicle charging system based on mobile charging pile scheduling of claim 4, wherein the number of model hidden layer nodes in the process of building the charging demand prediction model based on the neural network is obtained through the following formula:
Figure 598974DEST_PATH_IMAGE003
wherein the content of the first and second substances,
Figure 600428DEST_PATH_IMAGE004
representing the number of nodes of the sought model hidden layer,
Figure 999661DEST_PATH_IMAGE005
and
Figure 137381DEST_PATH_IMAGE006
representing the number of neurons in the input and output layers of the model respectively,
Figure 865166DEST_PATH_IMAGE007
representing the number of samples of the model training,
Figure 760441DEST_PATH_IMAGE008
、
Figure 966294DEST_PATH_IMAGE009
is a preset variable constant.
6. The electric vehicle charging system based on mobile charging pile scheduling of claim 4, wherein step B30 includes:
step B31, carrying out random sampling on the training data set and the auxiliary training data set through Monte Carlo to obtain sampling data;
step B32, normalizing the sampling data to obtain a pre-processing training data set and an auxiliary training data set; the calculation formula of the normalization process is as follows:
Figure 958521DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 857207DEST_PATH_IMAGE011
and
Figure 567674DEST_PATH_IMAGE012
respectively representing the second of the normalized and pre-normalized data sets
Figure 311639DEST_PATH_IMAGE013
The number of the data is one,
Figure 423951DEST_PATH_IMAGE014
and
Figure 493538DEST_PATH_IMAGE015
representing the maximum and minimum values of data in the data set, respectively.
7. The electric vehicle charging system based on mobile charging pile scheduling of claim 4, wherein the activation function of the charging demand prediction model is:
Figure 691302DEST_PATH_IMAGE016
wherein the content of the first and second substances,
Figure 238958DEST_PATH_IMAGE017
，
Figure 205777DEST_PATH_IMAGE018
is a transpose of the model weight matrix,
Figure 443335DEST_PATH_IMAGE002
in order to model the bias matrix of the model,
Figure 862815DEST_PATH_IMAGE011
is the first in the normalized data set
Figure 214162DEST_PATH_IMAGE013
A piece of data;
Figure 301067DEST_PATH_IMAGE019
and
Figure 712457DEST_PATH_IMAGE020
is a hyper-parameter.
8. The electric vehicle charging system based on mobile charging pile scheduling of claim 4, wherein the cost function of the charging demand prediction model is:
Figure 619233DEST_PATH_IMAGE021
wherein the content of the first and second substances,
Figure 508692DEST_PATH_IMAGE001
and
Figure 450103DEST_PATH_IMAGE002
respectively a weight matrix and a bias matrix of the model,
Figure 32394DEST_PATH_IMAGE022
is the total amount of data in the data set,
Figure 426466DEST_PATH_IMAGE023
to pass the data through the predicted values obtained by the model,
Figure 119616DEST_PATH_IMAGE024
the actual value corresponding to the data.
9. A storage device having a plurality of programs stored therein, wherein the programs are adapted to be loaded and executed by a processor to implement the mobile charging post dispatch-based electric vehicle charging system of any of claims 1-8.
10. A treatment apparatus comprises
A processor adapted to execute various programs; and
a storage device adapted to store a plurality of programs;
wherein the program is adapted to be loaded and executed by a processor to perform:
the electric vehicle charging system based on mobile charging pile scheduling of any one of claims 1-7.
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