CN117764340A - New energy electric automobile charging guiding grading regulation and control method - Google Patents
New energy electric automobile charging guiding grading regulation and control method Download PDFInfo
- Publication number
- CN117764340A CN117764340A CN202311779158.9A CN202311779158A CN117764340A CN 117764340 A CN117764340 A CN 117764340A CN 202311779158 A CN202311779158 A CN 202311779158A CN 117764340 A CN117764340 A CN 117764340A
- Authority
- CN
- China
- Prior art keywords
- charging
- vehicle
- path
- target
- cost
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000005457 optimization Methods 0.000 claims abstract description 21
- 238000009826 distribution Methods 0.000 claims abstract description 15
- 238000003064 k means clustering Methods 0.000 claims abstract description 6
- 230000006870 function Effects 0.000 claims description 39
- 239000011159 matrix material Substances 0.000 claims description 22
- 238000004590 computer program Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 12
- 238000012216 screening Methods 0.000 claims description 10
- 238000003860 storage Methods 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 4
- 238000011156 evaluation Methods 0.000 abstract description 3
- 230000001105 regulatory effect Effects 0.000 abstract description 3
- 230000001276 controlling effect Effects 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 7
- 238000007689 inspection Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000028838 turning behavior Effects 0.000 description 2
- 206010033799 Paralysis Diseases 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention discloses a new energy electric automobile charging guiding grading regulation and control method, which belongs to the technical field of electric automobile dispatching charging, and comprises the following steps: according to the real-time position state of the entering vehicle, selecting an initial scheduling level of a scheduling scheme, and sending a regulating and controlling instruction corresponding to the initial scheduling level to the entering vehicle; the invention has three-level dispatching levels, and in the primary dispatching of preliminary steering, a GRU steering decision model is established, so that intelligent adjustment in complex and dynamic traffic environment is realized; in the secondary scheduling of path planning, a K-means clustering and normal distribution modeling technology is utilized to determine an optimal path and a stable charging area point set with higher reliability; in the Pareto non-dominant multi-objective optimization model, comprehensively considering path cost, congestion cost and turning cost, and providing multi-dimensional path evaluation; in the three-stage scheduling of vehicle pile driving, the charging pile with the minimum Euclidean distance is calculated and selected as a target charging pile, so that the vehicle charging is realized.
Description
Technical Field
The invention belongs to the technical field of electric vehicle charging scheduling, and particularly relates to a new energy electric vehicle charging guiding grading regulation method.
Background
In recent years, with the increase of global environmental awareness, various countries are actively taking out related policies for promoting development of electric vehicles, electrified traffic with electric vehicles as cores is continuously developed at a high speed, urban charging demands are continuously increased, and construction and management of electric vehicle charging stations are becoming increasingly important. However, charging behavior within electric vehicle charging stations is random in time and space, which can negatively impact the power supply of the charging station and the urban traffic system, which is often difficult to deal with by conventional charging station management methods.
Charging stations are used as hubs for the conversion and supply of electrical energy in electric traffic systems, whose operation needs to be carefully designed, intelligently scheduled and resource-efficient managed to ensure their high efficiency, sustainability and excellent safety standards. However, the research of the existing charging station scheduling strategy still has the following problems: (1) in the process of scheduling decision, complex and variable factors cannot be fully integrated and dealt with, so that a relatively low decision fault tolerance rate is caused; (2) decision response speeds of electric vehicles are relatively limited, which may lead to congested scenarios within charging stations; (3) when the path planning is carried out on the vehicles with multiple charging stations in the station at the same time, the condition of path conflict exists, so that local traffic is paralyzed.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a new energy electric automobile charging guiding grading regulation method.
The invention provides the following technical scheme:
according to the first aspect, a new energy electric automobile charging guiding grading regulation method is provided, according to the real-time position state of an incoming vehicle, the initial scheduling level of a scheduling scheme is selected, and a regulation instruction corresponding to the initial scheduling level is sent to the incoming vehicle; the regulation and control instruction corresponding to the initial scheduling level comprises operations corresponding to the initial scheduling level and operations corresponding to all scheduling levels positioned behind the initial scheduling level;
the scheduling scheme comprises a primary scheduling level of preliminary steering, a secondary scheduling level of path planning and a tertiary scheduling level of vehicle piling, which are distributed from front to back;
the selecting the initial scheduling level of the scheduling scheme according to the real-time position state of the entering vehicle comprises the following steps:
if the incoming vehicle is in a position state of just entering the charging station, the initial scheduling level of the incoming vehicle is a primary scheduling level of preliminary steering;
if the entering vehicle is in a position state of entering the charging station and traveling in the direction of the charging area, the initial scheduling level of the entering vehicle is a secondary scheduling level of path planning;
if the entering vehicle is in a position state of entering one of the charging areas, the initial dispatching level of the entering vehicle is a three-level dispatching level of the entering vehicle.
Optionally, the operation corresponding to the primary scheduling level of the preliminary steering is:
predicting a preliminary steering direction and an optimal steering area of the incoming vehicle according to the historical data in the charging station;
transmitting the preliminary steering direction and the running information of the optimal steering area to the entering vehicle;
the operation corresponding to the secondary scheduling level of the path planning is as follows:
acquiring the number of idle stake bits of each charging area and area traffic flow data according to sensing equipment in the charging station;
processing the number of idle pile bits and regional traffic flow data of each charging region, and screening out all the steady charging regions;
planning an optimal path from the vehicle to each robust charging area according to the real-time position of the vehicle;
optimizing the optimal path of each stable charging area by multiple target paths, and determining a target path and a target charging area corresponding to the target path;
transmitting the driving information of the target charging area and the target path to the entering vehicle;
the operation corresponding to the three-level scheduling level of the vehicle pile driving is as follows:
according to the real-time position of the entering vehicle, determining the distance from all idle charging piles to the entering vehicle in a charging area where the vehicle is located, and taking the idle charging pile corresponding to the minimum distance as a target charging pile;
planning a path reaching a target charging pile according to the real-time position of the entering vehicle;
and transmitting the driving information of the path reaching the target charging pile to the entering vehicle.
Optionally, the predicting the preliminary steering direction and the optimal steering area of the incoming vehicle according to the historical data in the charging station comprises:
preprocessing service condition data of the charging piles and traffic flow data of each road section in each time period in the history in the charging station;
training the GRU steering decision model by utilizing the preprocessed service condition data and the traffic flow data of each road section;
and predicting the initial steering direction and the optimal steering area of the driven vehicle by using the trained GRU steering decision model.
Optionally, the processing the number of idle piles and the regional traffic data of each charging region, and screening all the robust charging regions includes:
constructing a judgment matrix, and determining a feature vector weight value of each charging area judgment matrix, wherein the judgment matrix is characterized by the number of local idle piles and the regional traffic flow;
determining the comprehensive grading value of each charging area in a weight summation mode according to the eigenvector weight value of each charging area judgment matrix;
clustering all the charging areas according to the comprehensive grading value by using K-means clustering;
determining the mean value and standard deviation of each cluster, and establishing a normal distribution model for the comprehensive grading value in each cluster;
determining a confidence interval of each cluster based on the normal distribution model;
the charging area located in the confidence interval is taken as a steady charging area.
Optionally, the determining formula of the comprehensive scoring value in the comprehensive scoring value of each charging area by determining the weight value of the eigenvector of each charging area judgment matrix according to the mode of weight summation is:
Score i ＝w c ·s c +w Q ·s Q
wherein Score i To synthesize the value of the score, w c Weight s for the number of local idle piles c Score for number of local idle piles, w Q Is the weight of regional traffic flow, s Q Is a region shape score.
Optionally, according to the real-time position of the vehicle, planning an optimal path from the vehicle to each robust charging area, using an a-TD path optimization algorithm, and introducing a turning cost and a congestion cost;
the planning the optimal path of the vehicle to each robust charging area according to the real-time position of the vehicle comprises:
converting the charging station model into a grid map, wherein charging piles in the charging station are used as nodes of the grid map, and a driving channel is used as a side of the grid map;
taking the position of the vehicle entering the charging station and turning for the first time as a starting point, taking the steady charging area as a target node, and establishing a cost function system from the starting point to the target node;
and taking the path of the minimum total cost from the vehicle to the target point as the optimal path for reaching each robust charging area according to the cost function system from the starting point to the target node.
Optionally, the position where the vehicle enters the charging station and turns for the first time is taken as a starting point, the robust charging area is taken as a target node, and a cost function system from the starting point to the target node is established, wherein the cost function system from the starting point to the target node is as follows:
g l (n)＝g(n)*γ
wherein,for a cost function system from a starting point to a neighbor node, g (n) is the actual cost value of an initial node, gamma is a congestion cost coefficient, g l (n) is an actual cost function after updating the congestion cost, c (n) is a turning cost function, h (n) is a heuristic cost estimation value of an initial node, and alpha is a weight function of the actual cost function and the estimated cost function; (x) n-1 ，y n-1 ) Parent node coordinates for the current node, (x n+1 ，y n+1 ) The child node coordinates of the current node; θ is the included angle between the current node and the child node and between the current node and the father node respectively when the vehicle turns; e is the cost value of a 45 turn of the vehicle.
Optionally, the optimizing the optimal path of each robust charging area for multiple target paths, and determining the target path and the target charging area corresponding to the target path includes:
determining a path comprehensive index of each optimal path by using a Pareto multi-target path optimization model according to the path cost, turning cost and path comprehensive scoring value of the optimal path reaching each robust charging area, taking the minimum value of the charging path comprehensive index as a target path and taking a target node corresponding to the target path as a target charging area;
the objective function of the Pareto multi-objective path optimization model is as follows:
wherein, sigma i Is the path comprehensive index of the ith path,to take into account the costs in congestion and cornering situations, C i (n) Score is the total turn cost of the ith path i And comprehensively scoring the path of the ith path.
In a second aspect, a computer device is provided, comprising a processor and a memory; the step of the new energy electric vehicle charging guidance grading regulation method according to any one of the first aspect is realized when the processor executes the computer program stored in the memory.
In a third aspect, a computer readable storage medium is provided for storing a computer program; the steps of the new energy electric vehicle charging guidance grading regulation method according to any one of the first aspect are realized when the computer program is executed by a processor.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, from the practical application of charging scheduling of the electric vehicle in the charging station, the process of the electric vehicle reaching the target charging pile is subjected to grading treatment, a GRU steering decision model is established in primary scheduling of preliminary steering, and the optimal steering area is predicted for the vehicle by learning the service condition data of the charging pile in each time period and the traffic flow data of each road section in the charging station, so that the preliminary steering of the vehicle is facilitated; in the two-stage scheduling of path planning, a charging area is introduced as a reference point, the charging area of a vehicle is determined, a K-means clustering method is used for screening a robust charging area, the path of the vehicle reaching the robust reference area is planned through an A-TD optimization algorithm, a leading edge path solution is obtained by using a Pareto multi-target optimization path on the basis of considering and calculating the driving distance, the congestion cost and the turning cost of different paths, and the target path planning reaching a target charging area is completed, so that the vehicle can go to the target charging area; in three-stage scheduling of vehicle pile-in, a charging pile with the minimum Euclidean distance is calculated and selected as a target charging pile, and a final path is planned through a simplified A-TD path optimization algorithm, so that the whole process of charging the vehicle is completed.
The invention realizes procedural decision-making through hierarchical scheduling, and realizes intelligent adjustment in complex and dynamic traffic environment by utilizing real-time decision-making of GRU steering decision-making model, thereby achieving the effects of optimizing vehicle steering, reducing congestion and improving traffic smoothness; the K-means clustering and normal distribution modeling technology are utilized to determine the optimal path and a stable charging area point set with higher reliability, so that the path planning efficiency and reliability are improved; in the Pareto non-dominant multi-objective optimization model, path cost, congestion cost and turning cost are comprehensively considered, multi-dimensional path evaluation is provided, different path selection factors are weighed, and the purposes of reducing path conflict, realizing system-level path optimization, improving traffic passing efficiency and reducing resource waste are achieved.
Drawings
FIG. 1 is a general flow chart of the new energy electric vehicle charging guidance hierarchical regulation and control method according to the present invention, if the initial dispatch level of the incoming vehicle is the primary dispatch level of the preliminary steering;
fig. 2 is a schematic diagram of a primary scheduling flow of preliminary steering in the new energy electric vehicle charging guidance hierarchical regulation method of the invention;
fig. 3 is a schematic diagram of a secondary scheduling flow of path planning in the new energy electric vehicle charging guidance hierarchical regulation method of the invention;
fig. 4 is a schematic diagram of a three-level dispatching flow of vehicle pile-in the new energy electric vehicle charging guiding hierarchical regulation method;
fig. 5 is a schematic flow chart of screening all robust charging areas in the new energy electric vehicle charging guidance hierarchical regulation method of the invention;
FIG. 6 is a schematic flow chart of an optimal path planned to each robust charging area in the new energy electric vehicle charging guidance hierarchical regulation method of the invention;
fig. 7 is a schematic flow chart of determining a target path in the new energy electric vehicle charging guidance hierarchical regulation method of the invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Embodiment 1,
The utility model provides a new energy electric automobile charging guidance grading regulation and control method, which comprises the following steps: according to the real-time position state of the entering vehicle, selecting an initial scheduling level of a scheduling scheme, and sending a regulating and controlling instruction corresponding to the initial scheduling level to the entering vehicle; the regulation and control instruction corresponding to the initial scheduling level comprises operations corresponding to the initial scheduling level and operations corresponding to all scheduling levels positioned behind the initial scheduling level; the detection of the real-time position status of the vehicle may depend on the sensing device of the vehicle itself or the sensing device within the charging station.
The scheduling scheme comprises a primary scheduling level of preliminary steering, a secondary scheduling level of path planning and a tertiary scheduling level of vehicle piling, wherein the primary scheduling level is distributed from front to back.
The selecting the initial scheduling level of the scheduling scheme according to the real-time position state of the entering vehicle comprises the following steps:
if the entering vehicle is in the position state of the charging station just entering, the initial scheduling level of the entering vehicle is the primary scheduling level of the preliminary steering, namely, the primary scheduling instruction corresponding to the preliminary steering is: primary steering operation of a primary scheduling level, path planning operation of a secondary scheduling level and vehicle piling operation of a tertiary scheduling level.
If the entering vehicle is in a position state of entering the charging station and traveling in the direction of the charging area, the initial scheduling level of the entering vehicle is a secondary scheduling level of the path planning, namely, a scheduling instruction of the secondary scheduling level corresponding to the path planning is: the operation of the secondary scheduling level of path planning and the operation of the tertiary scheduling level of vehicle pile driving.
If the driving-in vehicle is in a position state of entering one of the charging areas, the initial dispatching level of the driving-in vehicle is a three-level dispatching level of the driving-in vehicle, namely, a dispatching instruction corresponding to the three-level dispatching level of the driving-in vehicle is as follows: three-level dispatch level operation of vehicle driving.
The three-level dispatching of the entering vehicle can be regulated and controlled according to the real-time position of the vehicle, but as a preferable mode, the operation of the primary dispatching level of primary steering, the operation of the secondary dispatching level of path planning and the operation of the tertiary dispatching level of the vehicle entering pile are sequentially carried out on the entering vehicle, and the initial dispatching level is not a dispatching instruction of the primary dispatching level, so that the entering vehicle does not travel according to a set scheme path, and the condition of congestion in a charging station is avoided.
Specifically, as shown in fig. 2, the operation corresponding to the primary scheduling level of the preliminary steering is:
d1: predicting a preliminary steering direction and an optimal steering area of the incoming vehicle according to the historical data in the charging station;
d2: and transmitting the driving information of the initial steering direction and the optimal steering area to the entering vehicle.
More specifically, the D1 includes:
d11: and preprocessing service condition data of the charging piles in each historical time period and traffic flow data of each road section in the charging station.
The preprocessing comprises the steps of removing abnormal data points which are obviously deviated from normal values in the service condition data of the charging piles in each time period and the traffic flow data of each road section by adopting a multidimensional data abnormal value detection and linear interpolation processing method based on a mean square error threshold value.
D12: training the GRU steering decision model by utilizing the preprocessed service condition data and the traffic flow data of each road section, namely taking the data processed in the step D11 as a training set and a testing set of input sequences and target outputs, and completing training the GRU steering decision model by iterating and adjusting model parameters for a plurality of times.
D13: and predicting the initial steering direction and the optimal steering area of the driven vehicle by using the trained GRU steering decision model.
The real-time state of the vehicle is input into the GRU steering decision model to predict the initial steering direction and the optimal steering area of the incoming vehicle.
When the vehicle runs according to the received initial steering direction and the running information of the optimal steering area, the GRU steering decision module continuously collects new data at the same time and updates the model so as to realize real-time adjustment in complex and dynamic traffic environments.
As shown in fig. 3, the operations corresponding to the secondary scheduling level of the path planning are as follows:
s1: and acquiring the number of idle stake positions and regional traffic flow data of each charging region according to sensing equipment in the charging station.
The entrance of each charging area is provided with a reference point, the position of the reference point is used for referring to each charging area, and the sensing equipment can be a detector or a sensor and the like.
S2: and processing the number of idle pile bits and regional traffic flow data of each charging region, and screening out all the steady charging regions.
The processing of the idle pile number and the regional traffic flow data of each charging region comprises data cleaning and normalization processing, and the method for screening the robust charging region mainly comprises local clustering and a normal distribution model.
Specifically, as shown in fig. 5, step S2 includes:
s21: constructing a judgment matrix, and determining a feature vector weight value of each charging area judgment matrix, wherein the judgment matrix is characterized by the number of local idle piles and the regional traffic flow.
Selecting the number C of local idle pile bits and the local traffic flow Q as reference point quantitative evaluation characteristics, and creating a characteristic vector (omega) C ，ω Q ) By analyzing the related data, filling a judgment matrix and evaluating whether the matrix achieves consistency through consistency proportion; and if the judgment matrix passes the inspection, carrying out standardization processing on the judgment matrix, and if the judgment matrix does not pass the inspection, re-inspecting the judgment matrix, adjusting the weight until the judgment matrix passes the consistency inspection, and carrying out standardization processing on the judgment matrix.
S22: and determining the comprehensive grading value of each charging area in a weighted sum mode according to the eigenvector weight value of each charging area judgment matrix.
Specifically, the formula for determining the composite score value is:
Score i ＝w c ·s c +w Q ·s Q
wherein Score i To synthesize the value of the score, w c Weight s for the number of local idle piles c Score for number of local idle piles, w Q Is the weight of regional traffic flow, s Q Is a region shape score.
S23: and clustering all the charging areas according to the comprehensive grading value by using K-means clustering.
S24: and determining the mean value and standard deviation of each cluster, and establishing a normal distribution model for the comprehensive grading value in each cluster.
Calculating the mean value and standard deviation of each cluster, using the mean value of the intra-cluster scores as the mean value mu of the normal distribution, using the standard deviation as the standard deviation sigma of the normal distribution, and drawing a distribution model established by the visualization of the normal distribution curve.
S25: confidence intervals for each cluster are determined based on a normal distribution model.
At a 95% confidence level, two quantiles of a Cumulative Distribution Function (CDF) of the normal distribution corresponding to the lower and upper limits of the confidence interval, respectively, are found to determine the confidence interval of the reference point score.
S26: the charging area located in the confidence interval is taken as a steady charging area.
The multiple stable charging areas in the confidence interval are target reference point sets, and the target reference point sets have small fluctuation and high robustness.
S3: the optimal path of the vehicle to each robust charging area is planned according to the real-time position of the vehicle.
Step S3 uses an A-TD path optimization algorithm, and introduces turning cost and congestion cost.
Specifically, as shown in fig. 6, step S3 includes:
s31: and converting the charging station model into a grid map, wherein charging piles in the charging station are used as nodes of the grid map, and the driving channel is used as a side of the grid map.
S32: and taking the position of the vehicle entering the charging station and turning for the first time as a starting point, taking the steady charging area as a target node, and establishing a cost function system from the starting point to the target node.
The cost function system from the starting point to the target node is as follows:
g l (n)＝g(n)*γ
wherein,for a cost function system from a starting point to a neighbor node, g (n) is the actual cost value of an initial node, gamma is a congestion cost coefficient, g l (n) is an actual cost function after updating the congestion cost, c (n) is a turning cost function, h (n) is a heuristic cost estimation value of an initial node, and alpha is an actual cost function and estimationA weight function of the cost function; (x) n-1 ，y n-1 ) Parent node coordinates for the current node, (x n+1 ，y n+1 ) The child node coordinates of the current node; θ is the included angle between the current node and the child node and between the current node and the father node respectively when the vehicle turns; e is the cost value of a 45 turn of the vehicle.
Specifically, initialize a×td path optimization algorithm: the initial node is added to the open list and a heuristic cost estimate h (n) of the initial node is calculated, here using euclidean distance.
And (3) defining the form of the running cost existing when the A-TD optimization algorithm plans the path: heuristic cost, congestion cost and completion cost.
The turning cost is as follows:
multiple charging pile areas are divided in one charging station, and the grid distribution can enable the entering vehicle to turn for multiple times, so that the turning behavior needs to be contained in a cost system. First, the turning behavior of the vehicle is determined using the following equation:
wherein, (x) n-1 ，y n-1 ) A parent node of the current node; (x) n+1 ，y n+1 ) Is a child node of the current node.
(a) In the case of turning the vehicle, assuming that the 45 ° turning cost of the vehicle is E, a turning cost c (n) at the time of turning the vehicle is constructed:
and when the vehicle turns, the included angle between the current node and the child node and the father node respectively are theta.
(b) If the vehicle is traveling straight, the turning penalty is 0.
Based on the above analysis, the cornering cost c (n) is constructed as follows:
the congestion cost is:
when the traffic congestion occurs, the traffic needs to stop for waiting or re-planning the path to detour, which increases the running time of the traffic and causes bad driving experience for the driver. Therefore, a congestion cost coefficient gamma is introduced, and an actual cost function g (n) is updated, namely:
g l (n)＝g(n)*γ
in the invention, the traffic density is selected as a parameter for judging the congestion degree of a path, the traffic density (load) is defined as the number of vehicles passing through a certain passable grid in a period of time, if one vehicle stays on one grid, the number is increased by one after one second, and the load of the grid 60s is counted.
Specifically, the degree of traffic congestion is classified into 4 stages according to the test effect: when load is less than 10, no congestion exists, the cost is 0, and the congestion cost coefficient gamma is set to be 1; when the load is more than 10 and less than 15, the grid is a slightly congested road section, and if other roads have the same path cost and the congestion degree is lower, other paths should be selected, and the congestion cost coefficient gamma is set to be 2; when load is less than or equal to 15 and less than 20, the grid is a medium-degree congestion road section, at the moment, the vehicle should be made to bypass the point as much as possible to avoid further congestion, and the congestion cost coefficient gamma is set to be 5; when the load is more than or equal to 20, the grid is a severely congested road section, and if no other paths exist, the vehicle is forbidden to pass through the grid, and the congestion cost coefficient gamma is set to 20.
The actual cost g (n) and the heuristic cost h (n) are dynamically weighted, and turning cost and congestion cost are introduced in the process of heuristically searching the target path, so that a final evaluation function is obtained:
s33: and taking the path of the minimum total cost from the vehicle to the target point as the optimal path for reaching each robust charging area according to the cost function system from the starting point to the target node.
Starting from the initial node, expanding the neighbor nodes by selecting the minimum total cost as a target, updating the path cost and the father node until the optimal path is found, and then backtracking to obtain the complete path.
S4: and optimizing the optimal path of each robust charging area by multiple target paths, and determining the target path and the target charging area corresponding to the target path.
Specifically, step S4 includes:
and determining the path comprehensive index of each optimal path by using a Pareto multi-target path optimization model according to the path cost, turning cost and path comprehensive scoring value of the optimal path reaching each robust charging area, taking the minimum value of the charging path comprehensive index as a target path, and taking a target node corresponding to the target path as a target charging area.
The objective function of the Pareto multi-objective path optimization model is as follows:
wherein, sigma i Is the path comprehensive index of the ith path,to take into account the costs in congestion and cornering situations, C i (n) Score is the total turn cost of the ith path i And comprehensively scoring the path of the ith path.
More specifically, as shown in fig. 7, when robust charging area screening is performed, we obtain a robust charging area target point set, so that a×td optimization algorithm plans paths reaching different robust reference points, and the planned paths need to be further subjected to preferential treatment.
Objective function:
constraint conditions:
the objective function is synthesized by the charging path comprehensive index sigma i Constructing; wherein: alpha is a weight function of g (n) and h (n),c is the path cost of the ith path (without regard to congestion and cost in cornering situations) i (n) Score is the total turn cost of the ith path i Grade fraction for reference point for ith path,/->Representing comprehensive variable and SOC of ith vehicle of electric vehicle in process of in-station path planning i，t For the remaining battery capacity after the ith vehicle turn, SOC lower Lower limit of discharge capacity of battery E a Average energy consumption per kilometer of the electric automobile; min and Max represent the minimum and maximum cases of the respective functions, respectively.
Taking multi-regional property in the charging station into consideration by using an A-TD optimization algorithm, and introducing turning cost and congestion cost into a cost function system; pareto non-dominant multi-objective decisions further preferential paths defined by the a-TD optimization algorithm.
S5: and transmitting the driving information of the target charging area and the target path to the entering vehicle.
The entering vehicle reaches the target charging area according to the received target path traveling information.
As shown in fig. 4, the operation corresponding to the three-level scheduling level of the vehicle pile driving is as follows:
e1: and determining the distance from all the idle charging piles to the driven vehicle in the charging area where the vehicle is located according to the real-time position of the driven vehicle, and taking the idle charging pile corresponding to the minimum distance as the target charging pile.
And when the distance from the idle charging pile to the driving-in vehicle is calculated, the Euclidean distance is adopted.
E2: and planning a path reaching the target charging pile according to the real-time position of the entering vehicle.
A simplified a-TD path optimization algorithm may be used to plan the path to the target charging stake.
E3: and transmitting the driving information of the path reaching the target charging pile to the entering vehicle.
The invention provides procedural decisions for vehicles by using the three-level dispatching system, and improves dispatching efficiency to the greatest extent. In order to further ensure the optimality of the path, a robust reference point is introduced, and the secondary scheduling and the tertiary scheduling are coordinated to obtain an optimal coordinated path decision; in the process of path planning, turning and congestion cost of vehicles are considered, a scheme which is closer to real-time traffic conditions in a station is provided, the prediction condition of decision and actual disjoint is avoided, the method is applied to actual charging scheduling, accurate and efficient charging scheduling can be realized, and the problems that a charging scheduling scheme is inconsistent with the actual condition and the prediction efficiency is low are solved.
Embodiment II,
As shown in fig. 1, a method for guiding and grading regulation and control of charging of a new energy electric automobile is provided, which comprises the following steps:
step 1: predicting a preliminary steering direction and an optimal steering area of the incoming vehicle according to the historical data in the charging station;
step 2: transmitting the preliminary steering direction and the running information of the optimal steering area to the entering vehicle so as to realize primary scheduling of preliminary steering;
step 3: acquiring the number of idle stake bits of each charging area and area traffic flow data according to sensing equipment in the charging station;
step 4: processing the number of idle pile bits and regional traffic flow data of each charging region, and screening out all the steady charging regions;
step 5: planning an optimal path from the vehicle to each robust charging area according to the real-time position of the vehicle;
step 6: optimizing the optimal path of each stable charging area by multiple target paths, and determining a target path and a target charging area corresponding to the target path;
step 7: transmitting the driving information of the target charging area and the target path to the entering vehicle so as to realize the secondary scheduling of path planning;
step 8: according to the real-time position of the entering vehicle, determining the distance from all idle charging piles to the entering vehicle in a charging area where the vehicle is located, and taking the idle charging pile corresponding to the minimum distance as a target charging pile;
step 9: planning a path reaching a target charging pile according to the real-time position of the entering vehicle;
step 10: and transmitting the driving information of the path reaching the target charging pile to the driving-in vehicle so as to realize the three-level dispatching level of the driving-in vehicle.
In this embodiment, for a more specific process of the above method, reference may be made to the corresponding content disclosed in the foregoing embodiment, and no further description is given here.
In another embodiment, the invention provides a computer device comprising a processor and a memory; the method comprises the steps of realizing the new energy electric automobile charging guiding grading regulation method when a processor executes a computer program stored in a memory.
For more specific procedures of the above method, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no further description is given here.
In another embodiment, the present invention provides a computer-readable storage medium storing a computer program; and the step of realizing the new energy electric automobile charging guiding grading regulation method when the computer program is executed by the processor.
For more specific procedures of the above method, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no further description is given here.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the system, apparatus and storage medium disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant points refer to the description of the method section.
It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in essence or what contributes 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.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application; it will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
Claims (10)
1. The new energy electric automobile charging guiding grading regulation method is characterized in that according to the real-time position state of an incoming vehicle, the starting scheduling level of a scheduling scheme is selected, and a regulation command corresponding to the starting scheduling level is sent to the incoming vehicle; the regulation and control instruction corresponding to the initial scheduling level comprises operations corresponding to the initial scheduling level and operations corresponding to all scheduling levels positioned behind the initial scheduling level;
the scheduling scheme comprises a primary scheduling level of preliminary steering, a secondary scheduling level of path planning and a tertiary scheduling level of vehicle piling, which are distributed from front to back;
the selecting the initial scheduling level of the scheduling scheme according to the real-time position state of the entering vehicle comprises the following steps:
if the incoming vehicle is in a position state of just entering the charging station, the initial scheduling level of the incoming vehicle is a primary scheduling level of preliminary steering;
if the entering vehicle is in a position state of entering the charging station and traveling in the direction of the charging area, the initial scheduling level of the entering vehicle is a secondary scheduling level of path planning;
if the entering vehicle is in a position state of entering one of the charging areas, the initial dispatching level of the entering vehicle is a three-level dispatching level of the entering vehicle.
2. The method for guiding and grading regulation and control of charging of the new energy electric automobile according to claim 1, wherein the operation corresponding to the primary scheduling level of the preliminary steering is as follows:
predicting a preliminary steering direction and an optimal steering area of the incoming vehicle according to the historical data in the charging station;
transmitting the preliminary steering direction and the running information of the optimal steering area to the entering vehicle;
the operation corresponding to the secondary scheduling level of the path planning is as follows:
acquiring the number of idle stake bits of each charging area and area traffic flow data according to sensing equipment in the charging station;
processing the number of idle pile bits and regional traffic flow data of each charging region, and screening out all the steady charging regions;
planning an optimal path from the vehicle to each robust charging area according to the real-time position of the vehicle;
optimizing the optimal path of each stable charging area by multiple target paths, and determining a target path and a target charging area corresponding to the target path;
transmitting the driving information of the target charging area and the target path to the entering vehicle;
the operation corresponding to the three-level scheduling level of the vehicle pile driving is as follows:
according to the real-time position of the entering vehicle, determining the distance from all idle charging piles to the entering vehicle in a charging area where the vehicle is located, and taking the idle charging pile corresponding to the minimum distance as a target charging pile;
planning a path reaching a target charging pile according to the real-time position of the entering vehicle;
and transmitting the driving information of the path reaching the target charging pile to the entering vehicle.
3. The method for hierarchical regulation of charging guidance of a new energy electric vehicle according to claim 2, wherein predicting a preliminary steering direction and an optimal steering area of an incoming vehicle according to historical data in a charging station comprises:
preprocessing service condition data of the charging piles and traffic flow data of each road section in each time period in the history in the charging station;
training the GRU steering decision model by utilizing the preprocessed service condition data and the traffic flow data of each road section;
and predicting the initial steering direction and the optimal steering area of the driven vehicle by using the trained GRU steering decision model.
4. The method for guiding and grading regulation and control of charging of new energy electric vehicles according to claim 2, wherein the steps of processing the number of idle pile bits and regional traffic flow data of each charging region and screening out all the stable charging regions comprise:
constructing a judgment matrix, and determining a feature vector weight value of each charging area judgment matrix, wherein the judgment matrix is characterized by the number of local idle piles and the regional traffic flow;
determining the comprehensive grading value of each charging area in a weight summation mode according to the eigenvector weight value of each charging area judgment matrix;
clustering all the charging areas according to the comprehensive grading value by using K-means clustering;
determining the mean value and standard deviation of each cluster, and establishing a normal distribution model for the comprehensive grading value in each cluster;
determining a confidence interval of each cluster based on the normal distribution model;
the charging area located in the confidence interval is taken as a steady charging area.
5. The method for guiding and grading regulation of charging of new energy electric vehicles according to claim 4, wherein the characteristic vector weight value of the judgment matrix of each charging area is determined by a weight summation method, and the determination formula of the comprehensive score value is as follows:
Score i ＝w c ·s c +w Q ·s Q
wherein Score i To synthesize the value of the score, w c Weight s for the number of local idle piles c Score for number of local idle piles, w Q Is the weight of regional traffic flow, s Q Is a region shape score.
6. The method for guiding and grading the charge of the new energy electric automobile according to claim 2, wherein the method is characterized in that according to the real-time position of the vehicle, an optimal path from the vehicle to each robust charging area is planned, an A-TD path optimization algorithm is used, and turning cost and congestion cost are introduced;
the planning the optimal path of the vehicle to each robust charging area according to the real-time position of the vehicle comprises:
converting the charging station model into a grid map, wherein charging piles in the charging station are used as nodes of the grid map, and a driving channel is used as a side of the grid map;
taking the position of the vehicle entering the charging station and turning for the first time as a starting point, taking the steady charging area as a target node, and establishing a cost function system from the starting point to the target node;
and taking the path of the minimum total cost from the vehicle to the target point as the optimal path for reaching each robust charging area according to the cost function system from the starting point to the target node.
7. The method for hierarchical regulation and control of charging guidance of a new energy electric vehicle according to claim 6, wherein the starting point is a position where the vehicle enters a charging station and turns for the first time, the target node is a robust charging area, and a cost function system from the starting point to the target node is established, and the cost function system from the starting point to the target node is:
g l (n)＝g(n)*γ
wherein,for a cost function system from a starting point to a neighbor node, g (n) is the actual cost value of an initial node, gamma is a congestion cost coefficient, g l (n) after updating the congestion costC (n) is a turning cost function, h (n) is a heuristic cost estimation value of an initial node, and alpha is a weight function of the actual cost function and the estimated cost function; (x) n-1 ,y n-1 ) Parent node coordinates for the current node, (x n+1 ,y n+1 ) The child node coordinates of the current node; θ is the included angle between the current node and the child node and between the current node and the father node respectively when the vehicle turns; e is the cost value of a 45 turn of the vehicle.
8. The method for guiding and grading regulation and control of charging of new energy electric vehicles according to claim 2, wherein optimizing the optimal path of each robust charging area for multiple target paths, determining the target path and the target charging area corresponding to the target path comprises:
determining a path comprehensive index of each optimal path by using a Pareto multi-target path optimization model according to the path cost, turning cost and path comprehensive scoring value of the optimal path reaching each robust charging area, taking the minimum value of the charging path comprehensive index as a target path and taking a target node corresponding to the target path as a target charging area;
the objective function of the Pareto multi-objective path optimization model is as follows:
wherein Σ is i Is the path comprehensive index of the ith path,to take into account the costs in congestion and cornering situations, C i (n) Score is the total turn cost of the ith path i And comprehensively scoring the path of the ith path.
9. A computer device comprising a processor and a memory; the steps of the new energy electric automobile charging guiding grading regulation method are realized when the processor executes the computer program stored in the memory.
10. A computer-readable storage medium storing a computer program; the steps of the new energy electric vehicle charging guidance hierarchical regulation method according to any one of claims 1 to 8 are realized when the computer program is executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311779158.9A CN117764340A (en) | 2023-12-21 | 2023-12-21 | New energy electric automobile charging guiding grading regulation and control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311779158.9A CN117764340A (en) | 2023-12-21 | 2023-12-21 | New energy electric automobile charging guiding grading regulation and control method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117764340A true CN117764340A (en) | 2024-03-26 |
Family
ID=90311885
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311779158.9A Pending CN117764340A (en) | 2023-12-21 | 2023-12-21 | New energy electric automobile charging guiding grading regulation and control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117764340A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118014331A (en) * | 2024-04-10 | 2024-05-10 | 苏州澳昆智能机器人技术有限公司 | Scheduling method and device for automatically guiding vehicles |
-
2023
- 2023-12-21 CN CN202311779158.9A patent/CN117764340A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118014331A (en) * | 2024-04-10 | 2024-05-10 | 苏州澳昆智能机器人技术有限公司 | Scheduling method and device for automatically guiding vehicles |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102693633B (en) | Short-term traffic flow weighted combination prediction method | |
| CN107346460B (en) | Following operating mode Forecasting Methodology based on the lower front truck operation information of intelligent network contact system | |
| CN103871246A (en) | Short-term traffic flow forecasting method based on road network space relation constraint Lasso | |
| CN105118294A (en) | Short-time traffic flow prediction method based on state pattern | |
| CN113299107B (en) | Multi-target fusion intersection dynamic vehicle internet speed guiding control method | |
| CN117764340A (en) | New energy electric automobile charging guiding grading regulation and control method | |
| CN102087788A (en) | Method for estimating traffic state parameter based on confidence of speed of float car | |
| CN113222387A (en) | Multi-objective scheduling and collaborative optimization method for hydrogen fuel vehicle | |
| CN107284452A (en) | Merge the following operating mode forecasting system of hybrid vehicle of intelligent communication information | |
| CN116700296A (en) | Intelligent planning method and system for autonomous charging unmanned electric vehicle based on deep learning | |
| Montazeri-Gh et al. | Driving condition recognition for genetic-fuzzy HEV control | |
| CN116402420A (en) | Intelligent network management and scheduling method and system for transport vehicle | |
| Chen et al. | Online eco-routing for electric vehicles using combinatorial multi-armed bandit with estimated covariance | |
| CN112149888A (en) | Real-time travel time prediction method for unmanned mine card for strip mine | |
| CN113390430B (en) | Electric vehicle dynamic path planning and charging method for multi-warp stop point trip | |
| Miao et al. | Highly Automated Electric Vehicle (HAEV)-based mobility-on-demand system modeling and optimization framework in restricted geographical areas | |
| Samoili et al. | Investigation of lane flow distribution on hard shoulder running freeways | |
| CN104331746A (en) | Separate-type dynamic path optimization system and method thereof | |
| CN112330025B (en) | Prediction method of space-time charging load for urban electric vehicle | |
| CN117408598A (en) | Expressway large-piece transportation path rapid decision method based on dynamic road network | |
| CN112381470A (en) | Intelligent agent-based traffic flow distribution method, equipment and storage medium | |
| Gu et al. | A review of intelligent road preview methods for energy management of hybrid vehicles | |
| Zhang et al. | An optimal vehicle speed planning algorithm for regenerative braking at traffic lights intersections based on reinforcement learning | |
| CN113222285B (en) | Strip mine charging pile site selection method based on self-adaptive disturbance goblet-sea squirt algorithm | |
| Zhang et al. | Neural network based vehicle speed prediction for specific urban driving |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |