CN117584790B - Capacity-free charging pile control system - Google Patents
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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
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The invention discloses a capacity-free charging pile control system, which relates to the technical field of charging pile control and comprises the following components: the communication module is used for establishing communication connection between monitoring equipment and the non-capacity-increasing charging pile control module, and the monitoring equipment comprises a power sensor at the input side of the transformer and the non-capacity-increasing charging pile; the capacity-free charging pile control module is used for controlling the charging power of the capacity-free charging pile in real time; the storage module is used for storing the charging power prediction model of the non-capacity-increasing charging pile and the adjustment record of the charging power of the non-capacity-increasing charging pile by the non-capacity-increasing charging pile control module; and the man-machine interaction module is used for checking or exporting the charging power adjustment record of the non-capacity-increasing charging pile stored by the storage module and importing a charging power prediction model of the non-capacity-increasing charging pile. The real-time state of the transformer is monitored, and the charging power of the charging pile is dynamically adjusted, so that the transformer always operates in a safe range, and the operation efficiency of the transformer is improved while the safe operation of the transformer is ensured.
Description
Technical Field
The invention relates to the technical field of charging pile control, in particular to a capacity-free charging pile control system.
Background
Along with the development of science and technology, the requirements of environmental protection are more and more, more and more are filled in electric automobile, more electricity consumption of the electric pile, more and more kinds of electric equipment and facilities under the transformer, more quantity, complicated change of electric power, and great pressure to the transformer. The capacity of the power grid equipped with the transformer is certain, and how to ensure the utilization rate of the charging pile and protect the safe operation of the transformer is a problem which needs to be solved by the personnel in the field.
Disclosure of Invention
The invention provides a capacity-free charging pile control system, which comprises: the system comprises a communication module, a capacity-free charging pile control module, a storage module and a man-machine interaction module;
The communication module is used for establishing communication connection between monitoring equipment and the non-capacity-increasing charging pile control module, and the monitoring equipment comprises a power sensor at the input side of the transformer and the non-capacity-increasing charging pile;
The capacity-free charging pile control module is used for controlling the charging power of the capacity-free charging pile in real time;
The storage module is used for storing the charging power prediction model of the non-capacity-increasing charging pile and the adjustment record of the charging power of the non-capacity-increasing charging pile by the non-capacity-increasing charging pile control module;
And the man-machine interaction module is used for checking or exporting the charging power adjustment record of the non-capacity-increasing charging pile stored by the storage module and importing a charging power prediction model of the non-capacity-increasing charging pile.
The capacity-free charging pile control system comprises the following sub-steps:
the real-time power of the transformer is obtained through the communication module, and the alarm rule of the transformer is set;
When the transformer alarm is detected, acquiring real-time charging power of the non-capacity-increasing charging pile through a communication module, inputting the acquired real-time charging power of the non-capacity-increasing charging pile into a non-capacity-increasing charging pile charging power prediction model, and outputting an adjustment strategy of the charging power of the non-capacity-increasing charging pile;
Intelligently adjusting the charging power of the capacity-free charging pile according to a capacity-free charging pile charging power adjustment strategy;
and storing the adjustment record of the charging power of the capacity-free charging pile each time.
The foregoing system for controlling a non-capacity-increasing charging pile, wherein a non-capacity-increasing charging pile charging power prediction model is formed according to analysis results of historical power data of the non-capacity-increasing charging pile and a transformer, and specifically comprises the following substeps:
establishing a capacity-free charging pile and transformer historical power data set C;
analyzing the relation between the transformer power and the capacity-free charging pile power according to the data set C;
and establishing a capacity-free charging pile charging power prediction model according to the relation between the power of the compressor and the capacity-free charging pile power.
The foregoing system for controlling a capacity-free charging pile, wherein the relationship between the transformer power and the capacity-free charging pile power is analyzed according to the data set C, specifically comprises the following substeps:
building a training data set according to the data set C;
model training is carried out by using a training data set, and a preliminary transformer power and capacity-free charging pile power relation model is obtained;
and evaluating the model, and optimizing parameters in the model to obtain a final relation model of the transformer power and the capacity-free charging pile power.
The capacity-free charging pile control system comprises the following sub-steps of evaluating a model and optimizing parameters in the model:
giving an initial parameter value for a parameter in the model;
and iterating the parameter values in the model by using a parameter set generated by model training, evaluating a prediction result by using an evaluation function, and optimizing the model parameters.
The capacity-free charging pile control system comprises the following capacity-free charging pile charging power prediction models: Wherein re (expression, return value) is a function of the return value to the right when the left expression is true, sta i is the power of the ith charging pile, sta i+1 is the power of the (i+1) th charging pile, n is the total number of charging piles, γ is the influence coefficient between the charging pile of sta i and the charging pile of sta i+1, b is a constant term of the model for adjusting the model deviation degree, δ is the deviation adjustment coefficient of the model,/> For the adjustment quantity of the charging power of the ith charging pile, ρ is the rated power of the transformer, and Y is the output capacity-free charging pile charging power adjustment strategy/>
The control system of the capacity-free charging pile comprisesFor the variation according toTo determine whether to change incrementally or incrementally.
The beneficial effects achieved by the invention are as follows: the real-time state of the transformer is monitored, and the charging power of the charging pile is dynamically adjusted, so that the transformer always operates in a safe range, the operation efficiency of the transformer is improved while the safe operation of the transformer is ensured, and more charging piles are charged.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
Fig. 1 is a schematic diagram of a control system for a capacity-free charging pile according to a first embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, a first embodiment of the present invention provides a capacity-free charging pile control system, including: the system comprises a communication module, a capacity-free charging pile control module, a storage module and a man-machine interaction module;
(1) The communication module is used for establishing communication connection between monitoring equipment and the non-capacity-increasing charging pile control module, and the monitoring equipment comprises a power sensor at the input side of the transformer and the non-capacity-increasing charging pile;
And a Modbus communication protocol is adopted, communication connection between the monitoring equipment and the capacity-free charging pile control module is established, communication parameters are configured according to related equipment documents and manuals, data exchange is realized, parameter data of the monitoring equipment can be obtained through the communication module after configuration is completed, and a control instruction is sent to the monitoring equipment.
(2) The capacity-free charging pile control module is used for controlling the charging power of the capacity-free charging pile in real time; specifically:
1. The real-time power of the transformer is obtained through the communication module, and the alarm rule of the transformer is set;
The real-time power of the transformer can be obtained in real time by setting a timer, and the alarm rule setting of the transformer refers to setting a threshold value for the power of the transformer, and when the real-time power of the transformer exceeds the threshold value, an alarm is triggered;
2. when the transformer alarm is detected, acquiring real-time charging power of the non-capacity-increasing charging pile through a communication module, inputting the acquired real-time charging power of the non-capacity-increasing charging pile into a non-capacity-increasing charging pile charging power prediction model, and outputting an adjustment strategy of the charging power of the non-capacity-increasing charging pile;
the capacity-free charging pile charging power prediction model is formed according to analysis results of capacity-free charging pile and transformer historical power data, and specifically:
① Establishing capacity-free charging pile and transformer historical power data set C
The capacity-free charging pile and transformer historical power dataset C＝{(ptra1,P1),(ptra2,P2),(ptra3,P3),…(ptratotal,Ptotal)},, wherein ptra t is transformer power in different histories, P t is a capacity-free charging pile power set in different histories, t is a value of 1-total, total is the total number of histories, P t＝{sta1,sta2,sta3,...,stan},stai represents the historical power of different charging piles, i is a value of 1-n, and n is the number of charging piles;
② Analyzing the relation between the transformer power and the capacity-free charging pile power according to the data set C
I. Building a training data set according to the data set C;
Extracting power set data of each capacity-free charging pile in the data set C, and finishing the power set data into an input set, wherein the input set is expressed as: s 1＝{P1,P2,P3,…Ptotal }, wherein P t is a power set of the capacity-free charging piles in different histories, t is 1-total, total is the total number of the histories, P t＝{sta1,sta2,sta3,…,stan},stai is the historic power of the different charging piles, i is 1-n, and n is the number of the charging piles;
Extracting historical power of each transformer in the data set C, and sorting the historical power into an output set, wherein the output set is expressed as: s 2＝{ptra1,ptra2,ptra3,...ptratotal }, where ptra t is transformer power in different histories.
Training a model by using a training data set to obtain a preliminary transformer power and capacity-free charging pile power relation model;
The primary transformer power and capacity-free charging pile power relation model is expressed as follows: Wherein T is the output transformer power, sta i is the power of the ith charging pile, sta i+1 is the power of the (i+1) th charging pile, n is the total number of charging piles, gamma is the influence coefficient between the charging piles of sta i and the charging pile of sta i+1 (when the power of a certain charging pile is changed, the power of the adjacent charging pile is also affected to a certain extent), b is a constant term of the model for adjusting the model deviation degree, and delta is the deviation degree adjustment coefficient of the model.
Thirdly, evaluating the model, and optimizing parameters in the model to obtain a final relation model of transformer power and capacity-free charging pile power;
i. giving an initial parameter value of parameters gamma, b and delta in the model;
the model generates a parameter set containing a plurality of groups of gamma, b and delta parameter values during the training process, and the initial parameters can be assigned as any one group of parameter values in the parameter set.
Iterating the parameter values in the model by using a parameter set generated by model training, evaluating a prediction result by using an evaluation function, and optimizing model parameters;
the evaluation function is: l= - (T '. Times.log (T) + (1-T '). Times.log (1-T)), where T is a predicted value and T ' is a true value.
And when the model parameter value changes, calculating the difference between the model predicted value and the true value by using an evaluation function, measuring the accuracy of the model, and determining the optimal model parameter value according to the model accuracy which is higher as the calculated difference is smaller, so as to form a final transformer power and capacity-free charging pile power relation model.
③ Establishing a capacity-free charging pile charging power prediction model according to the relation between the power of the compressor and the capacity-free charging pile power;
The charging power prediction model of the capacity-free charging pile is as follows: Wherein re (expression, return value) is a function of the return value to the right when the left expression is true, sta i is the power of the ith charging pile, sta i+1 is the power of the (i+1) th charging pile, n is the total number of charging piles, γ is the influence coefficient between the power of the sta i charging pile and the power of the sta i+1 charging pile (the power of an adjacent charging pile is also affected by a certain influence when the power of a certain charging pile is changed), b is a constant term of the model for adjusting the model deviation degree, δ is the deviation adjustment coefficient of the model,/> For the adjustment quantity of the charging power of the ith charging pile, ρ is the rated power of the transformer, and Y is the output capacity-free charging pile charging power adjustment strategy
It should be noted that the number of the substrates,As a variation, according to/>To determine whether to change incrementally or incrementally: first, calculate/>, respectivelyIn the case of the values of 1 and-1,As a result of (1)/>The result of the calculation at-1 is more approximate to ρ,/>Then the step size is reduced from-1, the step size value of the change can be preset and is 1,/>, by defaultEach change is firstly judged as expression/>Whether or not it is true, if not true/>Continuing to decrease; if/>The result of the calculation is closer to ρ when 1, and vice versa.
3. Intelligently adjusting the charging power of the capacity-free charging pile according to a capacity-free charging pile charging power adjustment strategy;
the charging power adjustment strategy of the capacity-free charging pile is as follows: 1-n respectively represent charging pile subscripts, and can unify the charging pile numbers with the charging pile subscripts, and can also find corresponding charging pile numbers according to the charging pile subscripts, and send charging power adjustment instructions to the charging piles through the communication module according to the charging pile numbers, wherein adjustment values are/>, respectively The positive number is to increase the charging power, and the negative number is to decrease the charging power.
4. Storing the adjustment record of the charging power of the capacity-free charging pile each time;
Each adjustment record includes: the number of the charging pile, the time of adjustment, the charging power before and after adjustment, the power of the transformer before and after adjustment, and the adjustment batch number, wherein the adjustment batch number is used for identifying adjustment records under the same adjustment strategy.
(3) The storage module is used for storing the charging power prediction model of the non-capacity-increasing charging pile and the adjustment record of the charging power of the non-capacity-increasing charging pile by the non-capacity-increasing charging pile control module;
(4) The man-machine interaction module is used for checking or exporting the charging power adjustment record of the non-capacity-increasing charging pile stored in the storage module and importing a charging power prediction model of the non-capacity-increasing charging pile;
The research on the utilization efficiency of the charging pile by the adjustment record of the charging power of the non-capacity-increasing charging pile can provide a certain reference value, and the adjustment record can also be used as training data of a non-capacity-increasing charging pile charging power prediction model, optimize model parameters and further improve the accuracy of the model;
The prediction model of the charging power of the capacity-free charging pile is put into use in an importing mode after the external computer is trained, and the calculation power and the storage of the operation environment are not required to be occupied for data mining and analysis, so that the system has low hardware requirements on the operation environment and low production investment cost.
Example two
The second embodiment of the invention provides a control method of a capacity-free charging pile, which comprises the following steps:
step S10: acquiring real-time power of a transformer and setting a transformer alarm rule;
The real-time power of the transformer can be obtained in real time by setting a timer, and the setting of the alarm rule of the transformer refers to setting a threshold value for the power of the transformer, and when the real-time power of the transformer exceeds the threshold value, an alarm is triggered.
Step S20: acquiring the real-time charging power of the capacity-free charging pile in real time, inputting the acquired real-time charging power of the capacity-free charging pile into a capacity-free charging pile charging power prediction model when a transformer alarm is detected, and outputting an adjustment strategy of the charging power of the capacity-free charging pile;
the capacity-free charging pile charging power prediction model is formed according to analysis results of capacity-free charging pile and transformer historical power data, and specifically:
① Establishing capacity-free charging pile and transformer historical power data set C
The capacity-free charging pile and transformer historical power dataset C＝{(ptra1,P1),(ptra2,P2),(ptra3,P3),...(ptratotal,Ptotal)},, wherein ptra t is transformer power in different histories, P t is a capacity-free charging pile power set in different histories, t is a value of 1-total, total is the total number of histories, P t＝{sta1,sta2,sta3,...,stan},stai represents the historical power of different charging piles, i is a value of 1-n, and n is the number of charging piles;
② Analyzing the relation between the transformer power and the capacity-free charging pile power according to the data set C
I. Building a training data set according to the data set C;
Extracting power set data of each capacity-free charging pile in the data set C, and finishing the power set data into an input set, wherein the input set is expressed as: s 1＝{P1,P2,P3,…Ptotal }, wherein P t is a power set of the capacity-free charging piles in different histories, t is 1-total, total is the total number of the histories, P t＝{sta1,sta2,sta3,…,stan},stai is the historic power of the different charging piles, i is 1-n, and n is the number of the charging piles;
Extracting historical power of each transformer in the data set C, and sorting the historical power into an output set, wherein the output set is expressed as: s 2＝{ptra1,ptra2,ptra3,…ptratotal }, where ptra t is transformer power in different histories.
Training a model by using a training data set to obtain a preliminary transformer power and capacity-free charging pile power relation model;
The primary transformer power and capacity-free charging pile power relation model is expressed as follows: Wherein T is the output transformer power, sta i is the power of the ith charging pile, sta i+1 is the power of the (i+1) th charging pile, n is the total number of charging piles, gamma is the influence coefficient between the charging piles of sta i and the charging pile of sta i+1 (when the power of a certain charging pile is changed, the power of the adjacent charging pile is also affected to a certain extent), b is a constant term of the model for adjusting the model deviation degree, and delta is the deviation degree adjustment coefficient of the model.
Thirdly, evaluating the model, and optimizing parameters in the model to obtain a final relation model of transformer power and capacity-free charging pile power;
i. giving an initial parameter value of parameters gamma, b and delta in the model;
the model generates a parameter set containing a plurality of groups of gamma, b and delta parameter values during the training process, and the initial parameters can be assigned as any one group of parameter values in the parameter set.
Iterating the parameter values in the model by using a parameter set generated by model training, evaluating a prediction result by using an evaluation function, and optimizing model parameters;
the evaluation function is: l= - (T '. Times.log (T) + (1-T '). Times.log (1-T)), where T is a predicted value and T ' is a true value.
And when the model parameter value changes, calculating the difference between the model predicted value and the true value by using an evaluation function, measuring the accuracy of the model, and determining the optimal model parameter value according to the model accuracy which is higher as the calculated difference is smaller, so as to form a final transformer power and capacity-free charging pile power relation model.
③ Establishing a capacity-free charging pile charging power prediction model according to the relation between the power of the compressor and the capacity-free charging pile power;
The charging power prediction model of the capacity-free charging pile is as follows: Wherein re (expression, return value) is a function of the return value to the right when the left expression is true, sta i is the power of the ith charging pile, sta i+1 is the power of the (i+1) th charging pile, n is the total number of charging piles, γ is the influence coefficient between the power of the sta i charging pile and the power of the sta i+1 charging pile (the power of an adjacent charging pile is also affected by a certain influence when the power of a certain charging pile is changed), b is a constant term of the model for adjusting the model deviation degree, δ is the deviation adjustment coefficient of the model,/> For the adjustment quantity of the charging power of the ith charging pile, ρ is the rated power of the transformer, and Y is the output capacity-free charging pile charging power adjustment strategy
It should be noted that the number of the substrates,As a variation, according to/>To determine whether to change incrementally or incrementally: first, calculate/>, respectivelyIn the case of the values of 1 and-1,As a result of (1)/>The result of the calculation at-1 is more approximate to ρ,/>Then the step size is reduced from-1, the step size value of the change can be preset and is 1,/>, by defaultEach change is firstly judged as expression/>Whether or not it is true, if not true/>Continuing to decrease; if/>The result of the calculation is closer to ρ when 1, and vice versa.
Step S30: intelligently adjusting the charging power of the capacity-free charging pile according to a capacity-free charging pile charging power adjustment strategy;
the charging power adjustment strategy of the capacity-free charging pile is as follows: 1-n respectively represent charging pile subscripts, which can unify the charging pile numbers with the charging pile subscripts, and can find corresponding charging pile numbers according to the charging pile subscripts, and send charging power adjustment instructions to the charging piles through the communication module according to the charging pile numbers, wherein the adjustment values are respectively The positive number is to increase the charging power, and the negative number is to decrease the charging power.
Step S40: storing the adjustment record of the charging power of the capacity-free charging pile each time;
Each adjustment record includes: the number of the charging pile, the time of adjustment, the charging power before and after adjustment, the power of the transformer before and after adjustment, and the adjustment batch number, wherein the adjustment batch number is used for identifying adjustment records under the same adjustment strategy.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention in further detail, and are not to be construed as limiting the scope of the invention, but are merely intended to cover any modifications, equivalents, improvements, etc. based on the teachings of the invention.
Claims (3)
1. A capacity-free charging pile control system, comprising: the system comprises a communication module, a capacity-free charging pile control module, a storage module and a man-machine interaction module;
The communication module is used for establishing communication connection between monitoring equipment and the non-capacity-increasing charging pile control module, and the monitoring equipment comprises a power sensor at the input side of the transformer and the non-capacity-increasing charging pile;
The capacity-free charging pile control module is used for controlling the charging power of the capacity-free charging pile in real time;
The storage module is used for storing the charging power prediction model of the non-capacity-increasing charging pile and the adjustment record of the charging power of the non-capacity-increasing charging pile by the non-capacity-increasing charging pile control module;
The man-machine interaction module is used for checking or exporting the charging power adjustment record of the non-capacity-increasing charging pile stored in the storage module and importing a charging power prediction model of the non-capacity-increasing charging pile;
the method for controlling the charging power of the capacity-free charging pile in real time specifically comprises the following substeps:
the real-time power of the transformer is obtained through the communication module, and the alarm rule of the transformer is set;
When the transformer alarm is detected, acquiring real-time charging power of the non-capacity-increasing charging pile through a communication module, inputting the acquired real-time charging power of the non-capacity-increasing charging pile into a non-capacity-increasing charging pile charging power prediction model, and outputting an adjustment strategy of the charging power of the non-capacity-increasing charging pile;
Intelligently adjusting the charging power of the capacity-free charging pile according to a capacity-free charging pile charging power adjustment strategy;
Storing the adjustment record of the charging power of the capacity-free charging pile each time;
the method comprises the following substeps of:
establishing a capacity-free charging pile and transformer historical power data set C;
analyzing the relation between the transformer power and the capacity-free charging pile power according to the data set C;
Establishing a capacity-free charging pile charging power prediction model according to the relation between the power of the compressor and the capacity-free charging pile power;
The capacity-free charging pile charging power prediction model is as follows: Wherein re (expression, return value) is a function of the return value to the right when the left expression is true, sta i is the power of the ith charging pile, sta i+1 is the power of the (i+1) th charging pile, n is the total number of charging piles, γ is the influence coefficient between the charging pile of sta i and the charging pile of sta i+1, b is a constant term of the model for adjusting the model deviation degree, δ is the deviation adjustment coefficient of the model,/> For the adjustment quantity of the charging power of the ith charging pile, ρ is the rated power of the transformer, and Y is the output capacity-free charging pile charging power adjustment strategy/>
As a variation, according to/>To determine whether to change incrementally or incrementally.
2. The capacity-free charging pile control system according to claim 1, wherein the relation between the transformer power and the capacity-free charging pile power is analyzed according to the data set C, and the method specifically comprises the following substeps:
building a training data set according to the data set C;
model training is carried out by using a training data set, and a preliminary transformer power and capacity-free charging pile power relation model is obtained;
and evaluating the model, and optimizing parameters in the model to obtain a final relation model of the transformer power and the capacity-free charging pile power.
3. A capacity-free charging pile control system according to claim 2, characterized in that the model is evaluated and parameters in the model are optimized, in particular comprising the following sub-steps:
giving an initial parameter value for a parameter in the model;
and iterating the parameter values in the model by using a parameter set generated by model training, evaluating a prediction result by using an evaluation function, and optimizing the model parameters.
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| CN111342481A (en) * | 2020-03-11 | 2020-06-26 | 佛山科学技术学院 | Charging station distribution transformer overload protection method and system based on energy storage |
| CN113103905A (en) * | 2021-04-02 | 2021-07-13 | 山东大卫国际建筑设计有限公司 | Intelligent charging distribution adjusting method, device, equipment and medium for electric automobile |
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|---|---|---|---|---|
| US9509176B2 (en) * | 2012-04-04 | 2016-11-29 | Ihi Inc. | Energy storage modeling and control |
| CN112200373A (en) * | 2020-10-15 | 2021-01-08 | 中国科学院深圳先进技术研究院 | Training method and training device for load prediction model, storage medium and equipment |
| CN112477674A (en) * | 2020-11-16 | 2021-03-12 | 深圳市康士柏实业有限公司 | Remote cluster charging control method, device and system for charging pile |
| CN112874369B (en) * | 2021-01-12 | 2022-08-05 | 上海追日电气有限公司 | Charging and energy supply optimization method and device of charging management system |
| KR102592134B1 (en) * | 2021-05-11 | 2023-10-19 | 최옥만 | Prediction Method of Capacity Limit of Transformers due to Increase in Electric Vehicles |
| FR3122951B1 (en) * | 2021-05-12 | 2024-02-02 | Renault Sas | Method and system for controlling the charging of a fleet of electric motor vehicles |
| CN113580993B (en) * | 2021-07-26 | 2023-06-20 | 华南理工大学 | Ordered charging method for charging pile clusters with highest transformer efficiency as target |
| CN113580994B (en) * | 2021-08-05 | 2023-04-14 | 国网江苏省电力有限公司苏州供电分公司 | Intelligent optimization method and system for electric vehicle integrated charging |
| CN115817254A (en) * | 2021-09-17 | 2023-03-21 | 台达电子企业管理（上海）有限公司 | Charging pile cluster power sharing control method, system and device |
| CN115230515A (en) * | 2022-07-25 | 2022-10-25 | 江苏安科瑞微电网研究院有限公司 | Charging management system based on charging station load and charging control method thereof |
| CN117060474A (en) * | 2023-08-22 | 2023-11-14 | 阳光电源(南京)有限公司 | Scheduling method, system, equipment and storage medium of new energy charging station |
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| CN113103905A (en) * | 2021-04-02 | 2021-07-13 | 山东大卫国际建筑设计有限公司 | Intelligent charging distribution adjusting method, device, equipment and medium for electric automobile |
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