CN112787324A - 5G big data-based short-term battery replacement load prediction method for quick-change electric automobile - Google Patents
5G big data-based short-term battery replacement load prediction method for quick-change electric automobileDownload PDFInfo
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- CN112787324A CN112787324ACN202011627245.9ACN202011627245ACN112787324ACN 112787324 ACN112787324 ACN 112787324ACN 202011627245 ACN202011627245 ACN 202011627245ACN 112787324 ACN112787324 ACN 112787324A
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- vehicle
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- battery
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
- 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
- B60L53/63—Monitoring or controlling charging stations in response to network capacity
- 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
- B60L53/80—Exchanging energy storage elements, e.g. removable batteries
- 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
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
- 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)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Secondary Cells (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
Description
Technical Field
The invention relates to a statistical prediction method for a power change probability of an electric vehicle, in particular to a short-term power change load prediction method for a quick-change electric vehicle based on 5G big data.
Background
At present, charging loads of electric passenger vehicles or battery replacement loads of electric commercial vehicles are researched more, but more complex analysis methods are adopted, more hypothesis factors exist, and analysis results are far away from practical application.
The quick-change type private electric vehicle adopts a battery replacement mode, and battery replacement can be carried out only when the State of charge (SOC) of the battery meets certain condition limit, so that the method is greatly different from an analysis method of a random charging mode of a charging electric vehicle. In addition, the driving time of the quick-change type private electric vehicle is not fixed, and a prediction method for adopting the electric commercial vehicle battery replacement load cannot be referred.
Disclosure of Invention
In order to solve the problems, the invention provides a method for predicting the short-term power change load of a quick-change electric vehicle based on 5G big data, which is used for rapidly predicting the total amount of power change which is possibly generated in the current time through data statistics of the existing Internet of vehicles and calculation according to probability.
In order to achieve the purpose, the method for predicting the short-term battery replacement load of the quick-change electric vehicle based on the 5G big data comprises the following steps:
the first step is as follows: according to the 5G vehicle networking data, carrying out classified statistics on the battery replacement application probabilities Gn (t, SOC) of users at different moments in different battery SOC states of the vehicle day by day(n，t)) And iteratively updated, where t is a time in a day of the classification date, soc(n，t)Is SOC, Gn (t, SOC) of the nth vehicle at time t(n，t)) The battery replacement application probability of the nth vehicle at the time t when the SOC is a certain determined value is obtained;
the second step is that: according to the 5G vehicle networking data, counting the number Ndo (t) of corresponding vehicles of which the SOC state of the vehicle battery at the current moment meets the battery replacement limit value;
the third step: according to the obtained vehicle quantity Ndo (t) meeting the power swapping limit value and the historical power swapping application probability of the user, calculating to obtain the actual quantity Nd (t) of the vehicles needing power swapping at present, wherein the formula is as follows:
the fourth step: according to the obtained vehicle quantity Ndo (t) meeting the power swapping limit value, the historical power swapping application probability Gn (t, soc) is combined(n，t)) The current SOC value SOC of each vehicle(n，t)The battery charging multiplying power Cr and the battery nominal energy E are calculated to obtain the current battery replacement at the current momentThe station needs to charge the battery; the formula is as follows:
according to the method for predicting the short-term battery replacement load of the quick-change electric vehicle based on the 5G big data, the charge state of the networked quick-change vehicle is rapidly obtained through a 5G network, the battery replacement behavior of the networked quick-change vehicle is monitored, and the general battery replacement probability under the relative time and the charge state is obtained through statistics; and then applying the power change probability to online real-time calculation of the short-term load of the quick-change type private electric vehicle. The vehicle SOC data information is obtained through the Internet of vehicles at present on the basis of the Internet of vehicles technology and big data technology, and prediction of real-time power conversion load at the current moment is calculated relatively easily.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects according to the present invention will be given with reference to the preferred embodiments.
Example 1.
The method for predicting the short-term battery replacement load of the quick-change electric vehicle based on the 5G big data, which is described in the embodiment, comprises the following steps:
the first step is as follows: according to the 5G vehicle networking data, carrying out classified statistics on the battery replacement application probabilities Gn (t, SOC) of users at different moments in different battery SOC states of the vehicle day by day(n，t)) And iteratively updated, where t is a time in a day of the classification date, soc(n，t)Is SOC, Gn (t, SOC) of the nth vehicle at time t(n，t)) The battery replacement application probability of the nth vehicle at the time t when the SOC is a certain determined value is obtained;
the second step is that: according to the 5G vehicle networking data, counting the number Ndo (t) of corresponding vehicles of which the SOC state of the vehicle battery at the current moment meets the battery replacement limit value;
the third step: according to the obtained vehicle quantity Ndo (t) meeting the power swapping limit value and the historical power swapping application probability of the user, calculating to obtain the actual quantity Nd (t) of the vehicles needing power swapping at present, wherein the formula is as follows:
the fourth step: according to the obtained vehicle quantity Ndo (t) meeting the power swapping limit value, the historical power swapping application probability Gn (t, soc) is combined(n，t)) The current SOC value SOC of each vehicle(n，t)Calculating to obtain the required power of the battery needing to be charged by the battery replacing station at the current moment; the formula is as follows:
although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. A5G big data-based short-term battery replacement load prediction method for a quick-change electric vehicle is characterized by comprising the following steps:
the first step is as follows: according to the 5G vehicle networking data, carrying out classified statistics on the battery replacement application probabilities Gn (t, SOC) of users at different moments in different battery SOC states of the vehicle day by day(n，t)) And iteratively updated, wherein t is a certain time in a certain day in the classification date, SOC(n，t)Is SOC, Gn (t, SOC) of the nth vehicle at time t(n，t)) A summary of power change application for the nth vehicle at time t when SOC is a certain valueRate;
the second step is that: according to the 5G vehicle networking data, counting the number Ndo (t) of corresponding vehicles of which the SOC state of the vehicle battery at the current moment meets the battery replacement limit value;
the third step: according to the obtained vehicle quantity Ndo (t) meeting the power swapping limit value and the historical power swapping application probability of the user, calculating to obtain the actual quantity Nd (t) of the vehicles needing power swapping at present, wherein the formula is as follows:
the fourth step: according to the obtained vehicle quantity Ndo (t) meeting the power swapping limit value, the historical power swapping application probability Gn (t, soc) is combined(n，t)) The current SOC value SOC of each vehicle(n，t)Calculating to obtain the required power of the battery needing to be charged by the battery replacing station at the current moment; the formula is as follows:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011627245.9ACN112787324A (en) | 2020-12-30 | 2020-12-30 | 5G big data-based short-term battery replacement load prediction method for quick-change electric automobile |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011627245.9ACN112787324A (en) | 2020-12-30 | 2020-12-30 | 5G big data-based short-term battery replacement load prediction method for quick-change electric automobile |
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| CN112787324Atrue CN112787324A (en) | 2021-05-11 |
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| CN202011627245.9APendingCN112787324A (en) | 2020-12-30 | 2020-12-30 | 5G big data-based short-term battery replacement load prediction method for quick-change electric automobile |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107038493A (en)* | 2016-09-18 | 2017-08-11 | 蔚来汽车有限公司 | A method for quickly predicting electric vehicle swapping load |
| US20180339597A1 (en)* | 2017-05-23 | 2018-11-29 | Martin Kruszelnicki | Charging connector |
- 2020
- 2020-12-30CNCN202011627245.9Apatent/CN112787324A/enactivePending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107038493A (en)* | 2016-09-18 | 2017-08-11 | 蔚来汽车有限公司 | A method for quickly predicting electric vehicle swapping load |
| US20180339597A1 (en)* | 2017-05-23 | 2018-11-29 | Martin Kruszelnicki | Charging connector |
Non-Patent Citations (1)
| Title |
|---|
| 刘东明: "《5G革命》", 31 December 2019* |
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