CN113111297A - Charging pile power automatic optimization method and device - Google Patents

Charging pile power automatic optimization method and device Download PDF

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Publication number
CN113111297A
CN113111297A CN202110327812.7A CN202110327812A CN113111297A CN 113111297 A CN113111297 A CN 113111297A CN 202110327812 A CN202110327812 A CN 202110327812A CN 113111297 A CN113111297 A CN 113111297A
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China
Prior art keywords
gun
real
unit
time state
state parameter
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CN202110327812.7A
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Chinese (zh)
Inventor
杨洋
李雪强
常青
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BEIJING YOUSHENG INTELLIGENT CONTROL TECHNOLOGY CO.,LTD.
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Suzhou Sriway New Energy Technology Co ltd
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Priority to CN202110327812.7A priority Critical patent/CN113111297A/en
Publication of CN113111297A publication Critical patent/CN113111297A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • 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
    • B60L53/30—Constructional details of charging stations
    • B60L53/31—Charging columns specially adapted for electric vehicles
    • 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

Abstract

The invention discloses a method and a device for automatically optimizing power of a charging pile, and relates to the technical field of new energy charging piles. The method for automatically optimizing the power of the charging pile comprises the following steps: judging whether the deviation is within the tolerance range or not by comparing the real-time state parameter group of each gun with a curve obtained by off-line learning; if so, the weights of the guns are recalculated according to the offline learning curve, and resources are reallocated according to the weights of the guns. The device includes: the device comprises a comparison judgment module and a calculation distribution module. The invention solves the problem that the existing charging pile can not ensure that a vehicle can be quickly supplied with power.

Description

Charging pile power automatic optimization method and device
Technical Field
The invention relates to the technical field of new energy charging piles, in particular to a charging pile power automatic optimization method and device.
Background
With the support of China on the new energy automobile industry, the electric automobile industry is rapidly developed. As an auxiliary device of an electric vehicle, the charging pile industry is also developing vigorously. Along with charging pile is more and more used, the user is also more and more to its functional demand. At present, the problem concerned by users is how to ensure that the vehicle can be quickly powered up.
To the current problem that can't guarantee that the vehicle can mend the electricity fast of filling among the prior art, effectual solution has not been proposed yet at present.
Disclosure of Invention
The purpose of the invention is as follows: the charging pile power automatic optimization method and device are provided to solve the problems in the prior art.
The technical scheme is as follows: a charging pile power automatic optimization method and a charging pile power automatic optimization device are provided, wherein the charging pile power automatic optimization method comprises the following steps: judging whether the deviation is within the tolerance range or not by comparing the real-time state parameter group of each gun with a curve obtained by off-line learning; if so, the weights of the guns are recalculated according to the offline learning curve, and resources are reallocated according to the weights of the guns.
In a further embodiment, determining whether the deviation is within the tolerance range by comparing the set of real-time state parameters of each gun with the curve learned offline comprises: if not, reducing the confidence coefficient of the off-line learning curve, and improving the confidence coefficient of the real-time state parameter group; and recalculating the weight of each gun according to the real-time state parameter group of each gun and the penalized learning curve.
In a further embodiment, determining whether the deviation is within the tolerance range by comparing the respective gun real-time status parameter sets with the curve learned offline comprises: when the charging pile receives a charging request of a first user, judging the use state of a charging gun; if yes, releasing the resources occupied by the gun which is transferred into the idle state to the resource pool.
In a further embodiment, if yes, releasing the resource occupied by the gun which has transitioned to the idle state to the resource pool comprises: and reporting all the real-time state parameter groups of the guns which are transferred into the idle state to a background.
In a further embodiment, reporting all the real-time status parameter sets of the gun which has transitioned to the idle state to the background further comprises: judging whether the resource pool is empty or not; if not, 1 unit of resource is allocated to the gun whose demand does not match and which is the highest priority.
In order to achieve the above object, according to another aspect of the present application, there is provided an automatic power optimization apparatus for a charging pile.
Fill electric pile power automatic optimization device according to this application includes: the comparison and judgment module is used for judging whether the deviation is within the tolerance range by comparing the real-time state parameter group of each gun with the curve obtained by off-line learning; and the calculation and distribution module is used for recalculating the weight of each gun according to the offline learning curve if the gun weight is the offline learning curve, so that the resource is redistributed according to the weight of each gun.
In a further embodiment, the comparing and determining module includes: the confidence coefficient adjusting unit is used for reducing the confidence coefficient of the off-line learning curve and improving the confidence coefficient of the real-time state parameter group if the off-line learning curve is not judged to be in the off-line learning curve; and the calculating unit is used for recalculating the weight of each gun according to the real-time state parameter group of each gun and the penalized learning curve.
In a further embodiment, the comparing and determining module comprises: the charging system comprises a first judging unit, a second judging unit and a charging unit, wherein the first judging unit is used for judging the use state of a charging gun when the charging pile receives a charging request of a first user; and the release unit is used for releasing the resources occupied by the guns which are transferred into the idle state to the resource pool if the guns are in the idle state.
In a further embodiment, the releasing unit comprises after: and the reporting unit is used for reporting all the real-time state parameter groups of the guns which are switched into the idle state to the background.
In a further embodiment, the reporting unit further includes: a second judging unit, configured to judge whether the resource pool is empty; and the priority allocation unit is used for allocating 1 unit of resources to the gun with the unmatched demand and the highest priority if the gun with the unmatched demand does not have the matched demand.
Has the advantages that: in the embodiment of the application, a power automatic optimization mode is adopted, and whether the deviation is in a tolerance range is judged by comparing the real-time state parameter group of each gun with a curve obtained by off-line learning; if so, the weight of each gun is recalculated according to the offline learning curve, so that resources are redistributed according to the weight of each gun, the purpose of automatic power optimization is achieved, the technical effect of ensuring the rapid power supplement of the vehicle is achieved, and the technical problem that the vehicle can not be rapidly supplemented by the existing charging pile is solved.
Drawings
Fig. 1 is a schematic diagram of a charging pile power automatic optimization method according to a first embodiment of the present application;
fig. 2 is a schematic diagram of a charging pile power automatic optimization method according to a second embodiment of the present application;
fig. 3 is a schematic diagram of a charging pile power automatic optimization method according to a third embodiment of the present application;
fig. 4 is a schematic diagram of a charging pile power automatic optimization method according to a fourth embodiment of the present application;
fig. 5 is a schematic diagram of an automatic power optimization method for a charging pile according to a fifth embodiment of the present application;
fig. 6 is a schematic diagram of an automatic power optimization device for a charging pile according to a first embodiment of the present application;
fig. 7 is a schematic diagram of an automatic power optimization apparatus for a charging pile according to a second embodiment of the present application;
fig. 8 is a schematic diagram of an automatic power optimization apparatus for a charging pile according to a third embodiment of the present application;
fig. 9 is a schematic diagram of an automatic power optimization apparatus for a charging pile according to a fourth embodiment of the present application;
fig. 10 is a schematic diagram of an automatic power optimization apparatus for a charging pile according to a fifth embodiment of the present application;
fig. 11 is a schematic flowchart of an automatic power optimization method for a charging pile according to the present application.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
According to an embodiment of the present invention, there is provided an automatic power optimization method for a charging pile, as shown in fig. 1, the method includes steps S100 to S102 as follows:
step 100, judging whether the deviation is within a tolerance range by comparing the real-time state parameter group of each gun with a curve obtained by off-line learning;
data support can be obtained by comparing the real-time state parameter group of each gun with the curve obtained by off-line learning, so that whether the deviation is in a tolerance range can be judged, and an accurate judgment effect is further realized. Preferably, fill electric pile and can carry out the rational distribution of power according to the resource occupation condition, the user is using APP, believe little before the program with vehicle information input, fills electric pile and can learn vehicle BMS's the rule of charging, further promotes later charge efficiency.
102, if yes, recalculating the weight of each gun according to the offline learning curve, and reallocating resources according to the weight of each gun;
the effect of automatic power optimization can be realized by distributing resources through the weight; preferably, through reasonable distribution and machine learning, the switching times of the high-voltage relay are reduced, the switching interval scanning time is optimized, and optimal charging is achieved. According to the embodiment of the present invention, as shown in fig. 2, in step S100, the step of comparing the real-time state parameter set of each gun with the curve obtained by offline learning to determine whether the deviation is within the tolerance range preferably includes:
step 200, if not, reducing the confidence coefficient of the off-line learning curve, and improving the confidence coefficient of the real-time state parameter group;
and step 202, recalculating the weight of each gun according to the real-time state parameter group of each gun and the penalized learning curve.
The corresponding adjusting effect can be realized according to the deviation condition, so that the optimal weight of each gun is obtained through calculation, and the most reasonable resource distribution is carried out according to the weight of each gun.
According to the embodiment of the present invention, as shown in fig. 3, in step S100, the step of determining whether the deviation is within the tolerance range by comparing the real-time state parameter set of each gun with the curve obtained by the offline learning includes:
step 300, when the charging pile receives a charging request of a first user, judging the use state of a charging gun;
step 302, if yes, releasing the resources occupied by the guns which are transferred into the idle state to a resource pool.
The effect of detecting the state of each gun can be realized, so that reasonable resource distribution is ensured, and the effect of resource integration is realized.
According to the embodiment of the present invention, as shown in fig. 4, after step S302, if yes, the step of releasing the resource occupied by the gun that has transitioned to the idle state to the resource pool includes:
and step 400, reporting all real-time state parameter sets of the gun which is switched into the idle state to a background.
The method and the device can realize good state parameter reporting effect, thereby providing a data basis for subsequent data collection.
According to the embodiment of the present invention, as shown in fig. 5, after the step S400, reporting all the sets of real-time status parameters of the gun that has transitioned into the idle state to the background further includes:
step 500, judging whether the resource pool is empty;
step 502, if not, allocate 1 unit of resource to the gun with unmatched demand and highest priority.
The effect of automatic power optimization can be further improved, and therefore the utilization efficiency is further improved. From the above description, it can be seen that the following technical effects are achieved by the present application:
in the embodiment of the application, a power automatic optimization mode is adopted, and whether the deviation is in a tolerance range is judged by comparing the real-time state parameter group of each gun with a curve obtained by off-line learning; if so, the weight of each gun is recalculated according to the offline learning curve, so that resources are redistributed according to the weight of each gun, the purpose of automatic power optimization is achieved, the technical effect of ensuring the rapid power supplement of the vehicle is achieved, and the technical problem that the vehicle can not be rapidly supplemented by the existing charging pile is solved.
According to an embodiment of the present invention, there is provided an apparatus for implementing the above charging pile power automatic optimization, as shown in fig. 6, the apparatus includes: the comparison and judgment module 1 is used for judging whether the deviation is within the tolerance range by comparing the real-time state parameter group of each gun with a curve obtained by off-line learning; and the calculation and distribution module 2 is used for recalculating the weight of each gun according to the offline learning curve if the gun weight is the offline learning curve, so as to redistribute the resources according to the weight of each gun.
Data support can be obtained by comparing the real-time state parameter group of each gun with the curve obtained by off-line learning, so that whether the deviation is in a tolerance range can be judged, and an accurate judgment effect is further realized. Preferably, fill electric pile and can carry out the rational distribution of power according to the resource occupation condition, the user is using APP, believe little before the program with vehicle information input, fills electric pile and can learn vehicle BMS's the rule of charging, further promotes later charge efficiency.
The effect of automatic power optimization can be realized by distributing resources through the weight; preferably, through reasonable distribution and machine learning, the switching times of the high-voltage relay are reduced, the switching interval scanning time is optimized, and optimal charging is achieved.
From the above description, it can be seen that the present invention achieves the following technical effects:
in the embodiment of the application, a power automatic optimization mode is adopted, and whether the deviation is in a tolerance range is judged by comparing the real-time state parameter group of each gun with a curve obtained by off-line learning; if so, the weight of each gun is recalculated according to the offline learning curve, so that resources are redistributed according to the weight of each gun, the purpose of automatic power optimization is achieved, the technical effect of ensuring the rapid power supplement of the vehicle is achieved, and the technical problem that the vehicle can not be rapidly supplemented by the existing charging pile is solved.
According to the embodiment of the present invention, preferably, as shown in fig. 7, the comparing and determining module 1 includes: the confidence coefficient adjusting unit 3 is used for reducing the confidence coefficient of the off-line learning curve and improving the confidence coefficient of the real-time state parameter group if the off-line learning curve is not judged to be in the off-line learning curve; and the calculating unit 4 is used for recalculating the weight of each gun according to the real-time state parameter group of each gun and the penalized learning curve.
The corresponding adjusting effect can be realized according to the deviation condition, so that the optimal weight of each gun is obtained through calculation, and the most reasonable resource distribution is carried out according to the weight of each gun.
According to the embodiment of the present invention, as shown in fig. 8, the comparing and determining module 1 includes: the first judging unit 5 is used for judging the use state of the charging gun when the charging pile receives a charging request of a first user; and the releasing unit 6 is used for releasing the resources occupied by the guns which are transferred into the idle state to the resource pool if the gun is in the idle state.
The effect of detecting the state of each gun can be realized, so that reasonable resource distribution is ensured, and the effect of resource integration is realized.
According to an embodiment of the present invention, it is preferable that, as shown in fig. 9, the releasing unit 6 thereafter includes: and the reporting unit 7 is used for reporting all the real-time state parameter groups of the guns which are switched into the idle state to the background.
The method and the device can realize good state parameter reporting effect, thereby providing a data basis for subsequent data collection.
According to the embodiment of the present invention, as shown in fig. 10, preferably, the reporting unit 7 further includes: a second judging unit 8, configured to judge whether the resource pool is empty; and a priority allocation unit 9, for allocating 1 unit of resource to the gun with unmatched demand and highest priority if not.
The effect of automatic power optimization can be further improved, and therefore the utilization efficiency is further improved.
It should be noted that the terminal device may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the schematic diagrams of the present invention are merely examples of a terminal device, and are not to be construed as limiting the terminal device, and may include more or less components than those shown, or some of the components may be combined, or different components, for example, the terminal device may further include a joy device, network access device, bus, etc.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the embodiments, and various equivalent changes can be made to the technical solution of the present invention within the technical idea of the present invention, and these equivalent changes are within the protection scope of the present invention.

Claims (10)

1. A charging pile power automatic optimization method is characterized by comprising the following steps: judging whether the deviation is within the tolerance range or not by comparing the real-time state parameter group of each gun with a curve obtained by off-line learning; if so, the weights of the guns are recalculated according to the offline learning curve, and resources are reallocated according to the weights of the guns.
2. The method of claim 1, wherein the step of determining whether the deviation is within a tolerance range by comparing the real-time state parameter set of each gun with a curve obtained by offline learning comprises: if not, reducing the confidence coefficient of the off-line learning curve, and improving the confidence coefficient of the real-time state parameter group; and recalculating the weight of each gun according to the real-time state parameter group of each gun and the penalized learning curve.
3. The method of claim 1, wherein the determining whether the deviation is within the tolerance range by comparing the real-time state parameter set of each gun with the curve obtained by offline learning comprises: when the charging pile receives a charging request of a first user, judging the use state of a charging gun; if yes, releasing the resources occupied by the gun which is transferred into the idle state to the resource pool.
4. The method of claim 3, wherein if yes, releasing the resource occupied by the gun which has been transferred to the idle state to the resource pool comprises:
and reporting all the real-time state parameter groups of the guns which are transferred into the idle state to a background.
5. The method of claim 4, wherein reporting all real-time status parameter sets of guns that have transitioned to the idle state to the background further comprises: judging whether the resource pool is empty or not; if not, 1 unit of resource is allocated to the gun whose demand does not match and which is the highest priority.
6. An automatic optimization device of charging pile power is characterized by comprising: the comparison and judgment module is used for judging whether the deviation is within the tolerance range by comparing the real-time state parameter group of each gun with the curve obtained by off-line learning; and the calculation and distribution module is used for recalculating the weight of each gun according to the offline learning curve if the gun weight is the offline learning curve, so that the resource is redistributed according to the weight of each gun.
7. The device of claim 6, wherein the comparing and determining module comprises: the confidence coefficient adjusting unit is used for reducing the confidence coefficient of the off-line learning curve and improving the confidence coefficient of the real-time state parameter group if the off-line learning curve is not judged to be in the off-line learning curve; and the calculating unit is used for recalculating the weight of each gun according to the real-time state parameter group of each gun and the penalized learning curve.
8. The device of claim 6, wherein the comparing and determining module comprises: the charging system comprises a first judging unit, a second judging unit and a charging unit, wherein the first judging unit is used for judging the use state of a charging gun when the charging pile receives a charging request of a first user; and the release unit is used for releasing the resources occupied by the guns which are transferred into the idle state to the resource pool if the guns are in the idle state.
9. The device of claim 6, wherein the release unit comprises: and the reporting unit is used for reporting all the real-time state parameter groups of the guns which are switched into the idle state to the background.
10. The device of claim 6, wherein the reporting unit further comprises:
a second judging unit, configured to judge whether the resource pool is empty; and the priority allocation unit is used for allocating 1 unit of resources to the gun with the unmatched demand and the highest priority if the gun with the unmatched demand does not have the matched demand.
CN202110327812.7A 2021-03-26 2021-03-26 Charging pile power automatic optimization method and device Pending CN113111297A (en)

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Cited By (2)

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CN115438909A (en) * 2022-08-03 2022-12-06 广东天枢新能源科技有限公司 Electric vehicle charging pile power resource distribution method and system based on big data
CN115542235A (en) * 2022-11-07 2022-12-30 北京志翔科技股份有限公司 Method, device and equipment for determining metering error of charging gun and storage medium

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CN110288271A (en) * 2019-07-11 2019-09-27 北京全来电科技有限公司 A kind of platform area grade charging load control strategy and method based on Model Predictive Control
CN110888908A (en) * 2019-11-01 2020-03-17 广州大学 Charging station/pile recommendation system and method capable of achieving deep learning continuously

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WO2018149080A1 (en) * 2017-02-17 2018-08-23 西安特锐德智能充电科技有限公司 Power distribution method and monitoring unit for group charging system
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