CN113665402B - Ordered charging method for charging pile clusters based on battery charging characteristics - Google Patents

Ordered charging method for charging pile clusters based on battery charging characteristics Download PDF

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CN113665402B
CN113665402B CN202110845668.6A CN202110845668A CN113665402B CN 113665402 B CN113665402 B CN 113665402B CN 202110845668 A CN202110845668 A CN 202110845668A CN 113665402 B CN113665402 B CN 113665402B
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charging
soc
power
clusters
charging pile
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CN113665402A (en
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王智东
周星月
张勇军
张紫凡
朱勇志
邓丰强
胡宇凡
赵小楠
朱兆杰
李福乾
冯倩怡
胡晨光
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South China University of Technology SCUT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • 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
    • B60L53/67—Controlling two or more charging stations
    • 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
    • B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
    • 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

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention discloses a charging pile cluster ordered charging method based on battery charging characteristics, which comprises the following steps: reading charging information of the electric vehicle corresponding to each charging pile through a communication line; calculating historical charging information of the electric vehicles by using a k clustering method to obtain expected parking time of each electric vehicle; taking the real-time load intensity of the power grid into consideration, and properly adjusting the total charging power of the charging pile clusters; the maximum charging power of the corresponding points on the charging characteristic curves of the charging piles is accumulated to obtain the total value of the maximum charging power required by the clusters, the total value Ps of the maximum charging power required by the clusters is compared with the total power upper limit value of the clusters of the charging piles, and the charging piles are charged after power distribution according to the comparison result.

Description

Ordered charging method for charging pile clusters based on battery charging characteristics
Technical Field
The invention belongs to the field of charging pile foundation construction, and particularly relates to a charging pile cluster ordered charging method based on battery charging characteristics.
Background
The reasonable charging pile clusters are orderly charged, and the method has important significance for reducing the construction cost of initial charging piles and reducing the waste of resources. At present, the charging pile clusters are orderly charged, and the charging pile clusters are divided into bus large charging stations with fixed charging rules and public charging pile clusters with large charging load fluctuation according to the service environment of the charging pile clusters. According to the charging strategies of the charging pile clusters, the charging strategies are divided into two types: one is traditional disordered charging, and the total power required by a charging pile cluster is predicted at the initial stage of construction of the cluster, and power equipment with corresponding power is constructed to support the charging pile cluster to work; the other is to set corresponding coefficients according to the total power required by the charging pile cluster and the rated power of the charging pile cluster so as to determine the power distribution of each charging pile.
The current widespread chaotic charging method leads to three main disadvantages through early prediction of charging demand: 1. because the total power required by the charging pile clusters is obtained by prediction, in order to ensure that the total power of the clusters can meet the requirements, the predicted value is set to be higher so as to ensure that the charging pile clusters under higher load work normally, and the waste of resources is caused to a certain extent; 2. because the charging pile clusters are charged in disorder, under the condition that human intervention is not performed, the power equipment is burnt out due to the fact that the instantaneous total power is overlarge; 3. unordered charging of the charging pile without intervention is easy to burden the power grid, and affects the quality of electric energy. The pareto optimal algorithm (Li Qing. Improvement of multi-objective optimized genetic algorithm and application study in ordered charging [ D ]. University of Shandong science and technology 2020.) there is a pareto improvement procedure before the pareto optimal solution is achieved. Pareto improvement specifically refers to the fact that in one environment, there is a population and some available resources that, when one wants to change from one state to another, at least one individual may become better without either individual becoming worse. In ordered charging, three objective functions, namely, the minimum impact on a power grid, the minimum charging time and the minimum charging cost, are needed to approach an optimal solution under various limiting conditions, and for a multi-objective optimal solution, pareto optimal is usually realized through a hypersurface, so that the operation amount is large, the portability is weak, and the realization difficulty is also increased.
Disclosure of Invention
Aiming at the problems, the invention provides a charging pile cluster ordered charging method based on battery charging characteristics, and the method adjusts the charging power of each charging pile in real time according to the rated total power of the charging pile cluster and the battery charging characteristics, so that ordered charging of the charging piles is realized, and the method has important significance for reducing the cost of early construction, prolonging the service life of power equipment and guaranteeing the quality of electric energy.
The invention is realized at least by one of the following technical schemes.
A charging pile cluster ordered charging method based on battery charging characteristics comprises the following steps:
1) Reading the corresponding data of each charging pile to generate the SOC when charging is started s -T s Map and SOC at the time of moving e -T e A figure;
2) Clustering the battery charging characteristic curves when charging is started, and carrying out SOC (state of charge) when the electric vehicle is started s Time T s Clustering the formed data points;
3) Mapping all points of the category to SOC at time of move e -T e In the figure, and map the SOC of the point e And T e Respectively averaging to obtain the moving time T for charging the electric vehicle end And at the end of the battery state of charge SOC end Is a predicted value of (2); according to the predicted value T of the moving time of each electric vehicle end Sequencing from early to late to obtain a sequence K;
4) Acquiring power grid load data in real time, and adjusting the total power upper limit value of the charging pile cluster;
5) Accumulating the maximum charging power of the corresponding points on the charging characteristic curves of the charging piles to obtain a total value Ps of the maximum charging power required by the clusters, and adding the total value P of the maximum charging power required by the clusterss and the upper limit PR mu of the total power of the charging pile clusters 1 And comparing, and charging after distributing power to the charging pile according to the comparison result.
Preferably, step 1) comprises the steps of:
s11, SOC at the beginning of charging acquired for each electric vehicle s Time T s And SOC during moving e Time T e Generating SOC at start of charge s -T s Map and SOC at the time of moving e -T e A figure;
s12, charging according to the power-driven vehicle access charging a peak time points and the SOC of the power-driven vehicle in charging at SOC s -T s A cluster initial points are generated in the figure.
Preferably, step 2) comprises the steps of:
s21, pair SOC s -T s SOC of map s And T s After the normalization processing, calculating the Euclidean distance from each data point to the initial point of the clustering, and recalculating the mass center of each set according to the set to which the data point belongs;
s22, if the distance between the newly calculated centroid and the original centroid is smaller than a certain set threshold, the clustering is considered to reach the expected result, the calculation is ended, and if the distance between the new centroid and the original centroid is still larger than the expected set threshold, the steps S21-S22 are needed to be repeated.
Preferably, step S21 includes the steps of:
s1, shortening the transverse axis to be original
Figure BDA0003180452800000031
Normalizing the transverse and longitudinal axes;
s2, calculating the Euclidean distance from each point to the mass center:
Figure BDA0003180452800000032
wherein T is 1 、T 2 、SOC 1 、SOC 2 Respectively represent the starting charging time and the charging state of two pointsA state.
Preferably, step 4) comprises the steps of:
(3) acquiring current power grid load intensity P in real time gird And according to the average load intensity P of the power grid gridave Calculating current power grid load intensity
Figure BDA0003180452800000041
(4) Calculating the maximum load coefficient of the current charging pile cluster
Figure BDA0003180452800000042
Preferably, the maximum load coefficient of the charging piles is subjected to limit value processing according to the preset maximum load coefficient beta of the charging pile clusters at the peak of the power grid, so as to ensure mu 1 ∈[β,1]；
Preferably, step 5) comprises the steps of:
(5) maximum charging power Pm of corresponding point of battery charging characteristic curve i The total value Ps of the maximum charging power required by the clusters is accumulated to obtain a value of the total value Ps of the maximum charging power required by the clusters i I is {1, 2, 3, … …, n }, i is the number of the charging pile;
(6) the total value Ps of the maximum charging power required by the clusters and the total power upper limit value PR mu of the charging pile clusters are calculated 1 Comparing;
(7) if Ps<PR·μ 1 Each charging pile uses the maximum charging power Pm of the corresponding point of the battery charging characteristic curve of each electric vehicle i Charging;
(8) if Ps>PR·μ 1 Then the charging pile cluster is led to charge the total power Pps= Σpmpreferentially n The electric vehicle is charged, and Pps is less than or equal to PR mu while n is as large as possible 1 。
Preferably, in step 4, K represents the number of the charging pile in the sequence K, so that the previous K bits are charged with priority for the prediction end time.
Preferably, the SOC at the beginning of charging is determined by a K-means clustering algorithm s -T s The graphs are clustered.
Preferably, if a charging pile in the charging pile cluster ends floating charging, stops charging, starts charging or performs power distribution again after 1-3min from the last power distribution.
Compared with the prior art, the invention has the beneficial effects that:
the ordered charging is realized, and meanwhile, the actual charging is ensured to be in accordance with the battery charging characteristic curve, so that the charging is efficient and safe, and the service life of the battery is prolonged. The condition that power equipment burns out caused by disordered charging of the charging pile clusters is prevented. In addition, the method has less algorithm operation amount and low requirement on the cluster controller, so the method has strong portability and popularization. The charging power is reasonably reduced in the algorithm through predicting the moving time, so that vehicles with shorter predicted charging time can be charged with priority. The actual requirements of the user on vehicle demand and off-peak charging are fully considered, orderly charging is realized on the premise of not sacrificing the user charging experience, and peak clipping and valley leveling of the power grid are realized.
Drawings
Fig. 1 is a flowchart of a method for orderly charging a group of charging piles based on battery charging characteristics according to an embodiment of the present invention;
FIG. 2 illustrates an SOC according to an embodiment of the present invention s -T s Map to SOC e -T e Map of the graph;
fig. 3 is a graph illustrating a characteristic of a main current charging strategy of an electric vehicle according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following examples and the accompanying drawings.
The ordered charging method of the charging pile clusters based on the battery charging characteristics shown in fig. 1 comprises the following steps:
s1, reading the percentage SOC of the residual electric quantity of each charging pile corresponding to the electric vehicle through a CAN communication line i Historical charging data, maximum charging power Pm of corresponding point of battery charging characteristic curve i I is {1, 2, 3, … …, n }, i is the number of the charging pile;
s2, SOC at the beginning of charging acquired for each electric vehicle s Time T s And SOC during moving e Time T e Generating SOCs at the start of charging respectively s -T s Map and SOC at the time of moving e -T e In the figure.
S3, using a K-means clustering algorithm to start charging the SOC s -T s Clustering the graphs to obtain a clustering result, and then obtaining the SOC of the electric vehicle at the beginning of charging s Time T s Substituting the clustered SOC s -T s The diagram is classified, and the specific steps are as follows:
s31, pair SOC s -T s SOC of map s And T s (SOC s And T s Respectively representing the corresponding charge state and charge time when the vehicle starts to charge), calculating the Euclidean distance from each data point to the initial point of clustering, dividing the Euclidean distance to the set to which the data point belongs when the data point is close to the initial data point, forming a sets, and then recalculating the mass center of each set;
s32, if the distance between the newly calculated centroid and the original centroid is smaller than a certain set threshold value, the clustering can be considered to reach the expected result, and the calculation is finished. If the new centroid is still more than the threshold value that is expected to be set from the original centroid, steps S31-S32 need to be repeated.
S4, as shown in FIG. 2, mapping all points of the category to SOC when moving after obtaining the category e -T e In the figure, and map the SOC of the point e And T e Respectively averaging to obtain the moving time T for charging the electric vehicle end And at the end of the battery state of charge SOC end Is a predicted value of (a).
S5, according to the predicted value T of the moving time of each electric vehicle end Sequencing from early to late to obtain a sequence K;
s6, acquiring power grid load data in real time, and adjusting the total power upper limit value of the charging pile cluster, wherein the specific steps are as follows:
s61, acquiring current power grid load intensity P in real time gird And according to the average load intensity P of the power grid gridave Calculating current power grid load intensity
Figure BDA0003180452800000061
S62, calculating the maximum load coefficient of the current charging pile cluster
Figure BDA0003180452800000062
S63, carrying out limit value processing on the maximum load coefficient of the charging pile according to the preset maximum load coefficient beta of the charging pile cluster at the peak of the power grid, and ensuring mu 1 ∈[β,1]；
S7, accumulating the maximum charging power of the corresponding points on the charging characteristic curves of the charging piles to obtain a total value Ps of the maximum charging power required by the clusters, and combining the total value Ps of the maximum charging power required by the clusters with an upper limit PR mu of the total power of the charging piles 1 Comparing, and charging after power distribution is carried out on the charging pile according to a comparison result, wherein the specific steps are as follows:
s71, maximum charging power Pm of corresponding point of battery charging characteristic curve i The total value Ps of the maximum charging power required by the clusters is accumulated to obtain a value of the total value Ps of the maximum charging power required by the clusters i I is {1, 2, 3, … …, n }, i is the number of the charging pile;
s72, combining the total value Ps of the maximum charging power required by the clusters with the upper limit value PR mu of the total power of the charging pile clusters 1 Comparing;
s73, if Ps<PR·μ 1 Each charging pile uses the maximum charging power Pm of the corresponding point of the battery charging characteristic curve of each electric vehicle i Charging;
s74, if Ps>PR·μ 1 Then the charging pile cluster is led to charge the total power Pps= Σpmpreferentially n It is necessary to ensure that Pps is less than or equal to PR.mu while n is as large as possible 1 。
And if one charging pile in the charging pile cluster finishes floating charging, stops charging, starts charging or carries out power distribution again after being 1-3min away from the last power distribution.
As another preferred embodiment, a method for orderly charging a cluster of charging piles based on battery charging characteristics includes the steps of:
step 1, reading through CAN communication lineGet each electric pile and correspond to electric motor car residual capacity percentage SOC i Historical charging data, maximum charging power Pm of corresponding point of battery charging characteristic curve i I is {1, 2, 3, … …, n }, i is the number of the charging pile;
step 2, obtaining current power grid load intensity P in real time gird And according to the average load intensity P of the power grid gridave Calculating current power grid load intensity
Figure BDA0003180452800000071
Step 3, calculating the maximum load coefficient of the current charging pile cluster
Figure BDA0003180452800000072
Step 4, processing the limit value of the maximum load coefficient of the charging pile according to the preset maximum load coefficient beta of the charging pile cluster at the peak of the power grid, and ensuring mu 1 ∈[β,1]；
Step 5, maximum charging power Pm of corresponding point of battery charging characteristic curve i The total value Ps of the maximum charging power required by the clusters is accumulated to obtain a value of the total value Ps of the maximum charging power required by the clusters i I is {1, 2, 3, … …, n }, i is the number of the charging pile;
step 6, if Ps<PR·μ 1 Each charging pile uses the maximum charging power Pm of the corresponding point of the battery charging characteristic curve of each electric vehicle i Charging is performed.
As another preferred embodiment, the euclidean distance of the cluster initiation point is obtained, comprising the steps of:
s1, generating SOC at the beginning of charging s -T s Map and SOC at the time of moving e -T e The graph shows that the SOC is in the SOC according to a peak time points of charging of the electric vehicle and the SOC of most vehicle owners in charging s -T s Generating a cluster initial points in the graph;
s2, shortening the transverse axis to be original
Figure BDA0003180452800000081
Normalizing the transverse and longitudinal axes, calculating eachEuclidean distance of one data point to the cluster initial point:
Figure BDA0003180452800000082
dividing the data points to the set to which the data points belong when the data points are close to the initial data points, and recalculating the mass center of each set after forming a sets;
and S3, when the distance between the newly calculated centroid and the original centroid is smaller than a preset threshold value of 0.05, the clustering can be considered to reach the expected result, and the calculation is finished. If the new centroid is still greater than the threshold value that is expected to be set, steps S2-3S need to be repeated.
As another preferable embodiment, the time T for moving the electric vehicle in the present charging is the same as the present charging end And at the end of the battery state of charge SOC end The method for obtaining the predicted value of the (c) comprises the following steps:
1) Substituting the charging access time T and the initial SOC of the electric vehicle into the vehicle SOC s -T s In the figure, the category is calculated;
2) Mapping each data point of the classification to the vehicle SOC e -T e Data points of the graph, and SOC e -T e SOC of map point e And T e Respectively calculating average values as the moving time T of the current charging of the electric vehicle end And at the end of the battery state of charge SOC end Is a predicted value of (a).
As another preferred embodiment, a method for orderly charging a cluster of charging piles based on battery charging characteristics includes the steps of:
a) Reading the percentage SOC of the residual electric quantity of each charging pile corresponding to the electric vehicle through a CAN communication line i Current state of charge S i Maximum charging power Pm at corresponding point of battery charging characteristic curve i I is {1, 2, 3, … …, n }, i is the number of the charging pile;
b) By SOC at the start of charge acquired for each electric vehicle s Time T s And SOC during moving e Time T e Generating SOCs at the start of charging respectively s -T s Map and SOC at the time of moving e -T e In the figure;
c) SOC at start of charging by using K-means clustering algorithm s -T s Clustering the graphs to obtain a clustering result, and then obtaining the SOC of the electric vehicle at the beginning of charging s Time T s Substituting the clustered SOC s -T s The graph is subjected to classification processing.
d) After obtaining the belonging category, mapping all points of the category to SOC when moving e -T e In the figure, and map the SOC of the point e And T e Respectively averaging to obtain the moving time T for charging the electric vehicle end And at the end of the battery state of charge SOC end Is a predicted value of (a).
e) According to the predicted value T of the moving time of each electric vehicle end Sequencing from early to late to obtain a sequence K;
f) Acquiring power grid load data in real time, and adjusting the total power upper limit value of the charging pile cluster;
g) While Ps>PR·μ 1 Then the charging pile cluster is led to charge the total power Pps= Σpmpreferentially n It is necessary to ensure that Pps is less than or equal to PR.mu while n is as large as possible 1 ；
h) Charging piles of the first n charging piles in the sequence K are respectively charged with the corresponding maximum charging power in the charging characteristic curves of the charging piles, and the rest charging piles are kept in a floating charging state;
i) And if one charging pile in the charging pile cluster finishes floating charging, stops charging, starts charging or carries out power distribution again after being 1-3min away from the last power distribution.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.

Claims (7)

1. The ordered charging method for the charging pile clusters based on the battery charging characteristics is characterized by comprising the following steps of:
1) Reading the corresponding data of each charging pile to generate the SOC when charging is started s -T s Map and SOC at the time of moving e -T e A figure;
2) Clustering the battery charging characteristic curves when charging is started, and carrying out SOC (state of charge) when the electric vehicle is started s Time T s The formed data points are clustered, comprising the following steps:
s21, pair SOC s -T s SOC of map s And T s After the normalization processing, calculating the Euclidean distance from each data point to the initial point of the clustering, and recalculating the mass center of each set according to the set to which the data point belongs;
s22, if the distance between the newly calculated centroid and the original centroid is smaller than a certain set threshold, the clustering is considered to reach a desired result, calculation is finished, and if the distance between the new centroid and the original centroid is still larger than the expected set threshold, the steps S21-S22 are required to be repeated;
step S21 includes the steps of:
s1, shortening the transverse axis to be original
Figure FDA0004154697400000011
Normalizing the transverse and longitudinal axes;
s2, calculating the Euclidean distance from each point to the mass center:
Figure FDA0004154697400000012
wherein T is 1 、T 2 、SOC 1 、SOC 2 Respectively represent the starting charging time and the starting charging time of two pointsState of charge;
3) After obtaining the belonging category, mapping all points of the category to SOC when moving e -T e In the figure, and map the SOC of the point e And T e Respectively averaging to obtain the moving time T for charging the electric vehicle end And at the end of the battery state of charge SOC end Is a predicted value of (2); according to the predicted value T of the moving time of each electric vehicle end Sequencing from early to late to obtain a sequence K;
4) Acquiring power grid load data in real time, and adjusting the total power upper limit value of the charging pile cluster;
5) Accumulating the maximum charging power of the corresponding points on the charging characteristic curves of the charging piles to obtain a total value Ps of the maximum charging power required by the clusters, and combining the total value Ps of the maximum charging power required by the clusters with the total power upper limit PR mu of the charging piles 1 Comparing, and charging after power distribution is carried out on the charging pile according to a comparison result, wherein the method comprises the following steps:
(1) maximum charging power Pm of corresponding point of battery charging characteristic curve i The total value Ps of the maximum charging power required by the clusters is accumulated to obtain a value of the total value Ps of the maximum charging power required by the clusters i I is {1, 2, 3, … …, n }, i is the number of the charging pile;
(2) the total value Ps of the maximum charging power required by the clusters and the total power upper limit value PR mu of the charging pile clusters are calculated 1 Comparing;
(3) if Ps<PR·μ 1 Each charging pile uses the maximum charging power Pm of the corresponding point of the battery charging characteristic curve of each electric vehicle i Charging;
(4) if Ps>PR·μ 1 Then the charging pile cluster is led to charge the total power Pps= Σpmpreferentially n The electric vehicle is charged, and Pps is less than or equal to PR mu while n is as large as possible 1 。
2. The method for orderly charging a battery charging pile cluster based on battery charging characteristics according to claim 1, wherein the step 1) comprises the steps of:
s11, SOC at the beginning of charging acquired for each electric vehicle s Time T s And SOC during moving e Time T e Generating SOC at start of charge s -T s Map and SOC at the time of moving e -T e A figure;
s12, charging according to the power-driven vehicle access charging a peak time points and the SOC of the power-driven vehicle in charging at SOC s -T s A cluster initial points are generated in the figure.
3. The method for orderly charging a battery charging pile cluster based on the battery charging characteristics according to claim 2, wherein the step 4) comprises the steps of:
(1) acquiring current power grid load intensity P in real time gird And according to the average load intensity P of the power grid gridave Calculating current power grid load intensity
Figure FDA0004154697400000021
(2) Calculating the maximum load coefficient of the current charging pile cluster
Figure FDA0004154697400000022
4. The ordered charging method of charging pile clusters based on battery charging characteristics according to claim 3, wherein the maximum load factor of the charging pile clusters at the peak of the power grid is subjected to limit processing according to the preset maximum load factor beta of the charging pile clusters, so as to ensure mu 1 ∈[β,1]。
5. The method for orderly charging a cluster of charging posts based on battery charging characteristics of claim 4, wherein in step 3), K represents a serial number of the charging posts in the sequence K, so that the K bits are charged preferentially before the prediction end time.
6. The method for orderly charging a battery charging pile cluster based on battery charging characteristics according to claim 5, wherein the K-means clustering algorithm is used for starting chargingSOC of (2) s -T s The graphs are clustered.
7. The method for orderly charging a group of charging piles based on battery charging characteristics according to claim 6, wherein if a certain charging pile in the group of charging piles ends float charging, stops charging, starts charging or resumes power distribution 1-3min from the last power distribution.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108053058A (en) * 2017-11-29 2018-05-18 东南大学 A kind of electric taxi charging pile site selecting method based on big data
CN109353245A (en) * 2018-10-29 2019-02-19 河南英开电气股份有限公司 A kind of electric car charging cluster automatic power distribution method
CN111284347A (en) * 2020-02-21 2020-06-16 安徽师范大学 State clustering coding method in charging station vehicle access control
DE102019201712A1 (en) * 2019-02-11 2020-08-13 Novacon GbR (vertretungsberechtigter Gesellschafter: Dipl.-Des. Holger Pilz, 70176 Stuttgart) Charging station for electric vehicles with at least two charging stations

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108053058A (en) * 2017-11-29 2018-05-18 东南大学 A kind of electric taxi charging pile site selecting method based on big data
CN109353245A (en) * 2018-10-29 2019-02-19 河南英开电气股份有限公司 A kind of electric car charging cluster automatic power distribution method
DE102019201712A1 (en) * 2019-02-11 2020-08-13 Novacon GbR (vertretungsberechtigter Gesellschafter: Dipl.-Des. Holger Pilz, 70176 Stuttgart) Charging station for electric vehicles with at least two charging stations
CN111284347A (en) * 2020-02-21 2020-06-16 安徽师范大学 State clustering coding method in charging station vehicle access control

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