CN117372006A - Charging method and system for electric bicycle charging pile - Google Patents
Charging method and system for electric bicycle charging pile Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/08—Payment architectures
- G06Q20/14—Payment architectures specially adapted for billing systems
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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
- B60L53/30—Constructional details of charging stations
- B60L53/31—Charging columns specially adapted for electric vehicles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07F—COIN-FREED OR LIKE APPARATUS
- G07F15/00—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity
- G07F15/003—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity for electricity
- G07F15/005—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity for electricity dispensed for the electrical charging of vehicles
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- 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
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- 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
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- 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 charging method and a charging system for an electric bicycle charging pile, relates to the field of electric bicycle charging, and aims to solve the problems that the charging of the traditional charging pile is large in error and inaccurate. The method comprises the following steps: s1, measuring actual electric energy output by a charging pile side during charging to obtain a charging pile meter display value and calculating a charging electric energy error; s2, acquiring standard time during charging and calculating charging time displayed on the charging pile side to obtain a charging time error; s3, constructing a prediction model according to historical electricity fee unit price data, and predicting the unit price of the electricity fee in the next month; s4, obtaining an electric charge error value of the charging pile according to the charging electric energy error of the electric bicycle, the clock time error of the electric bicycle and the predicted value of the unit price of the electricity charge in the month; and S5, calculating the total charging charge of the charging pile according to the following formula according to the electric charge error value of the charging pile.
Description
Technical Field
The invention relates to the field of electric bicycle charging, in particular to a charging method and a charging system for an electric bicycle charging pile.
Background
With the continuous development of the electric bicycle industry, the existing electric bicycle charging is divided into two modes: one kind charges for the stake of charging, and another kind charges for at home by oneself. And because the charging pile is strictly required to be safe and fire-fighting, the charging accident and fire hidden danger can be better prevented, and therefore, the charging pile is further popularized.
At present, the charging and charging of the traditional charging pile is often caused by the factors of inaccurate electric energy metering, errors of clocks, regional electricity price errors, time-of-day pricing of electric charges and the like, so that the charging and charging of the charging pile has larger errors and is inaccurate.
Disclosure of Invention
The embodiment of the invention provides a charging method and a charging system for a charging pile of an electric bicycle, which are used for solving the problems that the charging of the traditional charging pile is large in error and inaccurate.
In order to solve the technical problems, the embodiment of the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides a charging method for a charging pile of an electric bicycle, where the method includes:
s1, measuring actual electric energy output by a charging pile side during charging to obtain a charging pile meter display value; the formula for calculating the charging power error is:
；
wherein,to charge electric energyError (S)>For the charging pile to show the value->Actual output electric energy value of the charging pile side;
s2, acquiring standard time during charging and displaying charging time on the charging pile side; the formula for calculating the charging time error is as follows:
；
wherein,for charging time error, +.>For the standard time during charging, +.>Displaying charging time at the charging pile side;
s3, constructing a prediction model according to the historical electricity fee unit price data, and predicting the unit price of the electricity fee in the next month；
S4, calculating a formula of the electric charge error value of the charging pile according to the charging electric energy error, the charging time error and the predicted value of the unit price of the electric charge in the next month, wherein the formula is as follows:
；
wherein,for the electric charge error value of the charging pile +.>Charging electric energy error of electric bicycle, +.>For the clock time error of electric bicycle, < > for>A predicted value for the unit price of the electricity charge of the next month;
s5, calculating a formula of charging pile charging total charging according to the charging pile electric charge error value, wherein the formula is as follows:
；
wherein,charging the charging pile with total charge->For the current price of electricity, ->For the electric energy change of the charging pile, < >>For service charge->For loss cost; />And the electric charge error value of the charging pile is obtained.
As an improvement, the implementation manner of the step S3 is as follows:
s3.1, dividing the unit price data of the electricity charge with the history of the recent 5 calendar in the current area into a first data set and a second data set, and preprocessing the data;
wherein the second data set comprises: a discrete historical electricity rate unit price, the discrete historical electricity rate unit price comprising: historical electricity rate unit price of time-sharing electricity rates and historical electricity rate unit price of seasonal electricity rates; the first data set is other electricity fee unit price data of the near 5 calendar history except the second data set;
s3.2, constructing a first prediction model by using a long-short-term memory network LSTM based on the first data set, and verifying the first prediction model by using the first test data set;
wherein the first predicted data set is part of the first data set;
s3.3, constructing a second prediction model by using a gradient lifting algorithm based on the second data set, and verifying the second prediction model by using a second test data set;
wherein the second prediction data set is part of the second data set;
s3.4, selecting a first prediction model or a second prediction model according to the current date, and predicting the unit price of the electricity charge in the next month。
As an improvement, the implementation manner of the step S3.2 is as follows:
s3.2.1, constructing a first prediction model based on a long-short-term memory network LSTM, and training the first prediction model by using the data of the first 2/3 of the first data set;
s3.2.2, testing the first prediction model by using the data of the last 1/3 of the first data set, and adjusting the corresponding super parameters until the training of the first prediction model is completed.
As an improvement, in the above step S3.4:
setting target conditions of the first prediction model and target conditions of the second prediction model as follows: the error evaluation index is smaller than or equal to the target threshold value;
setting error evaluation indexes as follows: mean square error of the first predictive model, mean square error of the second predictive model;
setting the target threshold as the minimum value of the mean square error before and after the super-parameter adjustment of the prediction model;
the mean square error MSE is calculated as:
wherein n is the number of samples,is the true value of discrete historical electric charge, < >>Is a predictive value of discrete historical electric charges.
In a second aspect, an embodiment of the present invention provides a charging and billing system for a charging pile of an electric bicycle, including:
the charging electric energy error calculation module is used for measuring actual electric energy output by the charging pile side during charging to obtain a charging pile meter display value; the formula for calculating the charging power error is:
；
wherein,for charging power error, ">For the charging pile to show the value->Actual output electric energy value of the charging pile side;
the charging time error calculation module is used for obtaining the standard time during charging and displaying the charging time on the charging pile side; the formula for calculating the charging time error is as follows:
；
wherein,for charging time error, +.>For the standard time during charging, +.>Charging methodThe pile side displays the charging time;
the unit price prediction module for the electricity fee of the next month is used for constructing a prediction model according to the historical unit price data of the electricity fee and predicting the unit price of the electricity fee of the next month；
The error charge calculation module calculates the formula of the electric charge error value of the charging pile according to the predicted value of the charging electric energy error, the charging time error and the unit price of the electric charge in the next month, wherein the formula is as follows:
；
wherein,for the electric charge error value of the charging pile +.>Charging electric energy error of electric bicycle, +.>For the clock time error of electric bicycle, < > for>A predicted value for the unit price of the electricity charge of the next month;
the charging total charge calculation module is used for calculating the formula of charging total charge of the charging pile according to the electric charge error value of the charging pile, wherein the formula is as follows:
；
wherein,charging the charging pile with total charge->For the current price of electricity, ->For the electric energy change of the charging pile, < >>For service charge->For loss cost; />And the electric charge error value of the charging pile is obtained.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a memory, and a program or an instruction stored in the memory and capable of running on the processor, to implement the charging and billing method for the electric bicycle charging pile provided in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the electric bicycle charging pile charging and billing method provided in the first aspect.
The invention has the beneficial effects that:
in the embodiment of the invention, on one hand, in the aspect of calculating the electric charge error value of the charging pile, the invention not only comprehensively considers the charging electric energy error of the electric bicycle to balance the metering errors of the charging pile and the two electric energy meters of the electric bicycle, but also considers the clock time error of the electric bicycle and further predicts the unit price of the electric charge in the next month. Therefore, the calculated charging pile electric charge error value is more accurate. On the other hand, in the aspect of charging pile charging total billing, the invention not only comprehensively considers the variation of the electric energy of the charging pile, but also considers the service charge, and the loss charge and the charging pile electric charge error value obtained in the first aspect are comprehensively considered together, so that more accurate charging pile charging total billing is obtained through calculation. Therefore, the technical problems that the charging and charging errors of the traditional charging pile are large and the charging and charging are not accurate are solved.
Drawings
Fig. 1 is a schematic diagram of a charging and billing method of an electric bicycle charging pile;
fig. 2 is a schematic diagram of a charging and billing device for the electric bicycle charging pile.
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.
The term "and/or" herein is an association relationship describing an associated object, and means that there may be three relationships, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. The symbol "/" herein indicates that the associated object is or is a relationship, e.g., A/B indicates A or B.
The terms first and second and the like in the description and in the claims, are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order of the objects. For example, the first prediction model and the second prediction model, etc., are used to distinguish between different prediction models, rather than to describe a particular order of the prediction models.
In embodiments of the invention, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g." in an embodiment should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the description of the embodiments of the present invention, unless otherwise indicated, the meaning of "a plurality" means two or more, for example, a plurality of elements means two or more, elements, etc.
It should be noted that, in the embodiment of the present invention, the electric bicycle charging piles are installed in a specific charging station, and a plurality of electric bicycle charging piles are connected to at least one server, and all servers are connected to the same host computer and controlled by the host computer. The upper computer can be a common upper computer or a cloud platform-based upper computer control system.
In addition, when electric bicycle charges with being connected with the electric pile, this electric pile that charges can also be used for measuring electric bicycle's electric energy that charges in real time through data line and this electric bicycle connection (data connection) to obtain information such as electric bicycle electric energy's actual value, electric bicycle clock moment.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a charging method for a charging pile of an electric bicycle, which includes steps S1 to S5 as follows:
s1, measuring actual electric energy output by a charging pile side during charging to obtain a charging pile meter display value; the formula for calculating the charging power error is:
；
wherein,for charging power error, ">For the charging pile to show the value->Actual output electric energy value of the charging pile side;
optionally, the electric bicycle and the electric bicycle charging pile are electrically connected and are in data connection;
the electric bicycle charging pile measures charging electric energy of the electric bicycle in real time, and obtains an actual value of the electric bicycle electric energy and clock time of the electric bicycle through data connection.
It should be noted that, because the electric energy metering aspect of the electric bicycle temporarily has no national standard of electric energy metering, the method refers to the electric energy metering standard of the electric automobile charging pile (GB/T28569-2012 electric automobile alternating current charging pile electric energy metering and GB/T29318-2012 electric automobile non-vehicle charger electric energy metering), on the basis, the difference of the electric bicycle and the electric automobile in terms of batteries and charging pile equipment is actually considered, the electric energy meter test project basically refers to the GB/T28569-2012 standard, but in terms of each error project (such as rate period electric energy indication error, daily timing error, total electric energy indication error and measurement error), the method is implemented according to the small-capacity electric bicycle batteries and the common charging pile equipment. Therefore, the electric energy meter used by the invention is the electric energy meter meeting the conditions, and can measure the charging electric energy of the electric bicycle in real time.
The standard value of the charging electric energy refers to the charging electric energy measured by the charging pile side when the electric bicycle is connected with the charging pile and charged. The actual value of the electric energy of the electric bicycle refers to the measured charging electric energy at the electric bicycle side when the electric bicycle is connected with the charging pile and charged. The actual value of the electric energy of the electric bicycle can be displayed through a display system of the electric bicycle after being detected through an electric energy meter inside the electric bicycle, namely the electric energy of the electric bicycle is displayed through a dial of the electric bicycle.
Further, the charging power error of the electric bicycle is a difference between a standard value of the charging power of the bicycle and an actual value of the electric power of the electric bicycle. The method specifically comprises the following steps: the measurement error of the standard value of the charging electric energy and the measurement error of the actual value of the electric energy of the bicycle, the electric energy loss caused by heating in the charging process and the influence of the ambient temperature on the error of the electric energy meter.
S2, acquiring standard time during charging and displaying charging time on the charging pile side; the formula for calculating the charging time error is as follows:
；
wherein,for charging time error, +.>For the standard time during charging, +.>Displaying charging time at the charging pile side;
it should be noted that, when charging, the charging pile obtains the internal clock time, and at the same time, it also sends information of obtaining the standard clock time to the upper computer.
S3, constructing a prediction model according to the historical electricity fee unit price data, and predicting the unit price of the electricity fee in the next month。
Alternatively, the above step S3 may be implemented by the following steps S3.1 to S3.4.
S3.1, dividing the electricity fee unit price data of the recent 5 calendar history of the current area into a first data set and a second data set, and preprocessing the data.
Wherein the second data set comprises: a discrete historical electricity rate unit price, the discrete historical electricity rate unit price comprising: historical electricity rate unit price of time-sharing electricity rates and historical electricity rate unit price of seasonal electricity rates; the first data set is other near 5 calendar history electricity fee monovalent data except the second data set.
The time-sharing electricity price means that the unit price of the electricity charge is time-sharing charged according to the use time. For example, the unit price of electricity charge is 0.6 yuan/degree at 8 to 18 hours per day, and the unit price of electricity charge is 0.5 yuan/degree at other times.
Seasonal electricity prices means that the price of electricity fees is charged seasonally according to seasons. For example, the electricity rate unit price is 0.6 yuan/degree in a region of 7-9 months, and the electricity rate unit price is 0.5 yuan/degree in other seasons.
Specifically, the near 5 year calendar rate price data of the current area includes a first data set (common rate, and non-time-of-use rate, and non-seasonal rate) and a second data set (mainly including a historical rate price of time-of-use rate and a historical rate price of seasonal rate). Illustratively, assuming that the local peak electricity consumption period is 7-9 months old, and seasonal electricity prices and time-of-use electricity prices are employed, the electricity price data during 1-6 months and 10-12 months constitute a first data set, and the electricity price data during 7-9 months constitute a second data set.
S3.2, constructing a first prediction model by using a long-short-term memory network LSTM based on the first data set, and verifying the first prediction model by using the first test data set.
Wherein the first predicted data set is part of the first data set.
Specifically, the implementation manner of the step S3.2 is as follows:
s3.2.1, constructing a first predictive model based on the long-term memory network LSTM, and training the first predictive model using the first 2/3 of the data of the first data set.
S3.2.2, testing the first prediction model by using the data of the last 1/3 of the first data set, and adjusting the corresponding super parameters until the training of the first prediction model is completed.
It should be noted that, in the process of constructing the first prediction model by using the long-short-term memory network LSTM, the data ratio of the training set to the test set is set to be 2:1, that is, the first 2/3 of the data of the first data set is used as the training set to perform training; the latter 1/3 of the data of the first data set is used as a test set for testing.
In addition, the specific manner of constructing the first prediction model based on the long-short-term memory network LSTM may refer to related technologies, and the embodiments of the present application are not repeated.
And S3.3, constructing a second prediction model by using a gradient lifting algorithm based on the second data set, and verifying the second prediction model by using a second test data set.
Wherein the second prediction data set is part of the second data set.
S3.4, selecting a first prediction model or a second prediction model according to the current date, and predicting the unit price of the electricity charge in the next month。
Specifically, in the step S3.4 described above:
setting target conditions of the first prediction model and target conditions of the second prediction model as follows: the error evaluation index is less than or equal to the target threshold.
Setting error evaluation indexes as follows: mean square error of the first predictive model, mean square error of the second predictive model.
And setting a target threshold as the minimum value of the mean square error before and after the super-parameter adjustment of the prediction model.
The mean square error MSE is calculated as:
wherein n is the number of samples,is the true value of discrete historical electric charge, < >>Is a predictive value of discrete historical electric charges.
Optionally, the first prediction model and the second prediction model are used alternatively.
The charging pile may determine the current month according to the standard clock, select the first prediction model or the second prediction model, and predict the unit price of the electricity charge in the next month according to the prediction model.
It can be understood that because the electric charge charging adopts a mode of using before charging, the invention adopts two prediction models to comprehensively consider the influence of various factors such as common electricity price, time-sharing electricity price, seasonal electricity price and the like. The unit price of the electricity charge in the next month can be predicted more accurately, so that the calculation of the error of the charging pile is more accurate, the more accurate control of the cost is facilitated, and the electricity charge is saved.
S4, calculating a formula of the electric charge error value of the charging pile according to the charging electric energy error, the charging time error and the predicted value of the unit price of the electric charge in the next month, wherein the formula is as follows:
；
wherein,for the electric charge error value of the charging pile +.>Charging electric energy error of electric bicycle, +.>For the clock time error of electric bicycle, < > for>A predicted value for the unit price of the electricity charge of the next month;
it can be understood that the error calculation formula not only comprehensively considers the charging electric energy error of the electric bicycle to balance the metering errors of the charging pile and the electric bicycle, but also considers the clock time error of the electric bicycle and further predicts the unit price of the electricity charge in the next month. Therefore, the problem that the charging error of the traditional charging pile is larger is better solved, the charging error of the charging pile is more accurate, and a guarantee is provided for accurately calculating the charging total charge of the charging pile.
S5, calculating a formula of charging pile charging total charging according to the charging pile electric charge error value, wherein the formula is as follows:
；
wherein,charging the charging pile with total charge->For the current price of electricity, ->For the electric energy change of the charging pile, < >>For service charge->For loss cost; />And the electric charge error value of the charging pile is obtained.
It should be noted that the number of the substrates,for the current price of electricity, ->For the predicted value of the unit price of the electricity charge in the next month, the current unit price of the electricity charge is multiplied by the change quantity of the electricity charge of the charging pile when the current charge electricity charge is calculated, and the predicted value of the unit price of the electricity charge in the next month is used when the calculation of the error value of the electricity charge of the charging pile is carried out, so that the error value of the electricity charge of the charging pile which should be paid for the next month is shown. In this way, the charging pile is charged for total charging>The condition that electricity fee should be paid in the next month can be better reflected.
In addition, the service chargeThere are generally two charging modes: one is fixed billing, i.e. service charge +.>Is a constant; the other is obtained by multiplying the electric energy variation of the charging pile by the service charge coefficient (i.e.; the +)>Wherein->For a service charge coefficient).
Specifically, if neither the current month nor the next month is in the charging period of the time-of-use electricity price and the seasonal electricity price, or both are in the charging period of the time-of-use electricity price and the seasonal electricity price, the predicted value of the unit price of the electricity fee in the next month is equal to the current electricity billThe price (i.e.,) The method comprises the steps of carrying out a first treatment on the surface of the If one of the current month and the next month is within the charging period of the time-of-use electricity rate and the seasonal electricity rate, the predicted value of the electricity rate unit price of the next month is not equal to the current electricity rate unit price (may be greater or less).
It can be understood that in the aspect of calculating the electric charge error value of the charging pile, the invention not only comprehensively considers the charging electric energy error of the electric bicycle to balance the metering errors of the charging pile and the two electric energy meters of the electric bicycle, but also considers the clock time error of the electric bicycle and further predicts the unit price of the electric charge in the next month. Therefore, the calculated charging pile electric charge error value is more accurate. In the aspect of charging pile charging total billing, the invention comprehensively considers the variable quantity of the electric energy of the charging pile, considers the service charge, comprehensively considers the loss charge and the charging pile electric charge error value obtained in the first aspect together, and calculates to obtain more accurate charging pile charging total billing. Therefore, the technical problems that the charging and charging errors of the traditional charging pile are large and the charging and charging are not accurate are solved.
Example 2
As shown in fig. 2, the present application provides a charging system for an electric bicycle charging pile, including:
the charging electric energy error calculation module is used for measuring actual electric energy output by the charging pile side during charging to obtain a charging pile meter display value; the formula for calculating the charging power error is:
；
wherein,for charging power error, ">For the charging pile to show the value->Charging pile sideActually outputting the electric energy value;
the charging time error calculation module is used for obtaining the standard time during charging and displaying the charging time on the charging pile side; the formula for calculating the charging time error is as follows:
；
wherein,for charging time error, +.>For the standard time during charging, +.>Displaying charging time at the charging pile side;
the unit price prediction module for the electricity fee of the next month is used for constructing a prediction model according to the historical unit price data of the electricity fee and predicting the unit price of the electricity fee of the next month；
The error charge calculation module calculates the formula of the electric charge error value of the charging pile according to the predicted value of the charging electric energy error, the charging time error and the unit price of the electric charge in the next month, wherein the formula is as follows:
；
wherein,for the electric charge error value of the charging pile +.>Charging electric energy error of electric bicycle, +.>For the clock time error of electric bicycle, < > for>A predicted value for the unit price of the electricity charge of the next month;
the charging total charge calculation module is used for calculating the formula of charging total charge of the charging pile according to the electric charge error value of the charging pile, wherein the formula is as follows:
；
wherein,charging the charging pile with total charge->For the current price of electricity, ->For the electric energy change of the charging pile, < >>For service charge->For loss cost; />And the electric charge error value of the charging pile is obtained.
S1, measuring charging electric energy of an electric bicycle in real time to obtain a standard value of the charging electric energy; and obtaining the actual value of the electric energy of the electric bicycle, and calculating to obtain the charging electric energy error of the electric bicycle.
S2, acquiring clock time of the electric bicycle, simultaneously acquiring standard clock time, and calculating to obtain clock time error of the electric bicycle.
And S3, constructing a prediction model according to the historical electricity fee unit price data, and predicting the unit price of the electricity fee in the next month.
And S4, obtaining the electric charge error value of the charging pile according to the charging electric energy error of the electric bicycle, the clock time error of the electric bicycle and the predicted value of the unit price of the electricity charge in the month.
S5, calculating the total charging charge of the charging pile according to the following formula according to the electric charge error value of the charging pile;
wherein,charging the charging pile with total charge->For the current price of electricity, ->For the electric energy change of the charging pile, < >>For service charge->For loss cost; />And the electric charge error value of the charging pile is obtained.
It can be appreciated that the embodiment of the invention provides an electric bicycle charging pile charging and charging system, which adopts the electric bicycle charging pile charging and charging method in the embodiment 1, in terms of total charging of the charging pile, the invention comprehensively considers the variable quantity of the electric energy of the charging pile, considers the service charge, comprehensively considers the loss charge and the electric pile charge error value obtained in the first aspect, thereby better solving the problem of larger charging and charging error of the traditional charging pile, obtaining more accurate charging and charging error of the charging pile and providing equipment guarantee for accurately calculating the total charging of the charging pile.
Optionally, an embodiment of the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor, where the computer program when executed by the processor implements each process of the method embodiment of the foregoing embodiment 1, and the process can achieve the same technical effect, and for avoiding repetition, a description is omitted herein.
The embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements each process of the method in the foregoing embodiment 1, and the same technical effects can be achieved, and for avoiding repetition, a detailed description is omitted herein. Among them, a computer-readable storage medium such as a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, an optical disk, or the like.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that the above-mentioned preferred embodiment should not be construed as limiting the invention, and the scope of the invention should be defined by the appended claims. It will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the spirit and scope of the invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (7)
1. The charging and billing method for the electric bicycle charging pile is characterized by comprising the following steps:
s1, measuring actual electric energy output by a charging pile side during charging to obtain a charging pile meter display value; the formula for calculating the charging power error is:
；
wherein,for charging power error, ">For the charging pile to show the value->Actual output electric energy value of the charging pile side;
s2, acquiring standard time during charging and displaying charging time on the charging pile side; the formula for calculating the charging time error is as follows:
；
wherein,for charging time error, +.>For the standard time during charging, +.>Displaying charging time at the charging pile side;
s3, constructing a prediction model according to the historical electricity fee unit price data, and predicting the unit price of the electricity fee in the next month；
S4, calculating a formula of the electric charge error value of the charging pile according to the charging electric energy error, the charging time error and the predicted value of the unit price of the electric charge in the next month, wherein the formula is as follows:
；
wherein,for the electric charge error value of the charging pile +.>Charging electric energy error of electric bicycle, +.>For the clock time error of electric bicycle, < > for>A predicted value for the unit price of the electricity charge of the next month;
s5, calculating a formula of charging pile charging total charging according to the charging pile electric charge error value, wherein the formula is as follows:
；
wherein,charging the charging pile with total charge->For the current price of electricity, ->For the electric energy change of the charging pile, < >>For service charge->For loss cost; />And the electric charge error value of the charging pile is obtained.
2. The charging and billing method for electric bicycle charging piles according to claim 1, wherein the step S3 is implemented as follows:
s3.1, dividing the unit price data of the electricity charge with the history of the recent 5 calendar in the current area into a first data set and a second data set, and preprocessing the data;
wherein the second data set comprises: a discrete historical electricity rate unit price, the discrete historical electricity rate unit price comprising: historical electricity rate unit price of time-sharing electricity rates and historical electricity rate unit price of seasonal electricity rates; the first data set is other electricity fee unit price data of the near 5 calendar history except the second data set;
s3.2, constructing a first prediction model by using a long-short-term memory network LSTM based on the first data set, and verifying the first prediction model by using the first test data set;
wherein the first predicted data set is part of the first data set;
s3.3, constructing a second prediction model by using a gradient lifting algorithm based on the second data set, and verifying the second prediction model by using a second test data set;
wherein the second prediction data set is part of the second data set;
s3.4, selecting a first prediction model or a second prediction model according to the current date, and predicting the unit price of the electricity charge in the next month。
3. The charging and billing method for electric bicycle charging piles according to claim 2, wherein the step S3.2 is implemented as follows:
s3.2.1, constructing a first prediction model based on a long-short-term memory network LSTM, and training the first prediction model by using the data of the first 2/3 of the first data set;
s3.2.2, testing the first prediction model by using the data of the last 1/3 of the first data set, and adjusting the corresponding super parameters until the training of the first prediction model is completed.
4. The charging and billing method for electric bicycle charging piles according to claim 2, wherein in the step S3.4:
setting target conditions of the first prediction model and target conditions of the second prediction model as follows: the error evaluation index is smaller than or equal to the target threshold value;
setting error evaluation indexes as follows: mean square error of the first predictive model, mean square error of the second predictive model;
setting the target threshold as the minimum value of the mean square error before and after the super-parameter adjustment of the prediction model;
the mean square error MSE is calculated as:
wherein n is the number of samples,is the true value of discrete historical electric charge, < >>Is a predictive value of discrete historical electric charges.
5. Electric bicycle fills electric pile charging system, its characterized in that includes:
the charging electric energy error calculation module is used for measuring actual electric energy output by the charging pile side during charging to obtain a charging pile meter display value; the formula for calculating the charging power error is:
；
wherein,for charging power error, ">For the charging pile to show the value->Actual output electric energy value of the charging pile side;
the charging time error calculation module is used for obtaining the standard time during charging and displaying the charging time on the charging pile side; the formula for calculating the charging time error is as follows:
；
wherein,for charging time error, +.>For the standard time during charging, +.>Displaying charging time at the charging pile side;
the unit price prediction module for the electricity fee of the next month is used for constructing a prediction model according to the historical unit price data of the electricity fee and predicting the unit price of the electricity fee of the next month；
The error charge calculation module calculates the formula of the electric charge error value of the charging pile according to the predicted value of the charging electric energy error, the charging time error and the unit price of the electric charge in the next month, wherein the formula is as follows:
；
wherein,for the electric charge error value of the charging pile +.>Charging electric energy error of electric bicycle, +.>For the clock time error of electric bicycle, < > for>A predicted value for the unit price of the electricity charge of the next month;
the charging total charge calculation module is used for calculating the formula of charging total charge of the charging pile according to the electric charge error value of the charging pile, wherein the formula is as follows:
；
wherein,charging the charging pile with total charge->For the current price of electricity, ->For the electric energy change of the charging pile, < >>For service charge->For loss cost; />And the electric charge error value of the charging pile is obtained.
6. An electronic device comprising a processor, a memory, and a program or instructions stored on the memory and executable on the processor, which when executed by the processor, implements the electric bicycle charging stake charging billing method as claimed in any one of claims 1 to 4.
7. A computer-readable storage medium, wherein a program or instructions is stored on the readable storage medium, which when executed by a processor, implements the electric bicycle charging pile charging billing method according to any one of claims 1 to 4.
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