CN115438909A - Electric vehicle charging pile power resource distribution method and system based on big data - Google Patents
Electric vehicle charging pile power resource distribution method and system based on big data Download PDFInfo
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Abstract
The invention discloses a big data-based electric vehicle charging pile power resource distribution method and system, wherein the method comprises the following steps: the charging distribution business of each charging pile in the charging station is obtained, the charging parameters of the vehicles to be charged corresponding to the charging piles are determined according to the charging distribution business of each charging pile, the charging index weight of each charging pile is analyzed according to the charging parameters of the vehicles to be charged corresponding to each charging pile based on big data analysis rules, and the power resources in the corresponding proportion are distributed to each charging pile according to the charging index weight of each charging pile. The charging index weight of the charging pile is analyzed according to the charging distribution business of each charging pile, and then the power resource needing to be distributed is determined, so that the electric energy can be reasonably distributed according to the charging business condition of each charging pile, the problem of electric energy supply of different types of vehicle charging of each charging pile can be solved, and the practicability and the whole charging efficiency are improved.
Description
Technical Field
The invention relates to the technical field of resource allocation, in particular to an electric vehicle charging pile power resource allocation method and system based on big data.
Background
Along with the reinforcing of environmental protection consciousness and the national attention to environmental protection, the new forms of energy electric motor car takes place at the same time of transporting, however, because the limited need of new forms of energy electric motor car battery capacity charges at any time, and the electric motor car can be guaranteed to charge at any time by the setting up of electric motor car stake of charging, then, can all be provided with the public electric pile of charging in the higher region of each electric motor car frequency of use, the electric power resource distribution system of current public electric pile of charging is each stake of charging for the system is average with electric energy distribution, make each fill electric pile and keep the same electric energy supply, but above-mentioned method has following problem: because the different so the electric energy that its needs of each vehicle type of filling electric pile butt joint is also different thereby make some fill electric pile can appear the condition that the electric energy is not enough in the charging process and lead to unable punctual completion vehicle charging work, greatly reduced charge efficiency.
Disclosure of Invention
Aiming at the problems shown above, the invention provides a big data-based electric vehicle charging pile power resource allocation method and system, which are used for solving the problems that in the background art, because the types of vehicles connected with each charging pile are different, the required electric energy is different, so that the charging work of some charging piles cannot be completed on time due to the fact that the electric energy is insufficient in the charging process, and the charging efficiency is greatly reduced.
A big data-based electric vehicle charging pile power resource allocation method comprises the following steps:
acquiring a charging distribution service of each charging pile in a charging station;
determining the charging parameters of the vehicles to be charged corresponding to the charging piles according to the charging distribution business of each charging pile;
analyzing the charging index weight of each charging pile according to the charging parameters of the vehicles to be charged corresponding to each charging pile based on big data analysis rules;
and distributing power resources in a corresponding proportion to each charging pile according to the charging index weight of the charging pile.
Preferably, the acquiring a charging distribution service of each charging pile in the charging station includes:
acquiring a charging reservation request instruction sent by each vehicle to be charged;
determining the vehicle type of each vehicle to be charged according to a reserved charging request instruction sent by each vehicle to be charged;
detecting the current service state of each charging pile;
and reasonably distributing charging piles according to the vehicle type of each vehicle to be charged and the current service state of each charging pile, and determining the charging distribution task of each charging pile according to the distribution result.
Preferably, the determining the charging parameters of the vehicle to be charged corresponding to each charging pile according to the charging distribution service of each charging pile includes:
determining the charging demand of a vehicle to be charged corresponding to each charging pile based on the charging distribution service of each charging pile;
acquiring charging configuration data of each vehicle to be charged corresponding to each charging pile;
determining target charging time, charging power, charging voltage and charging current of the vehicle to be charged according to charging configuration data of each vehicle to be charged corresponding to each charging pile and the charging demand of the vehicle to be charged;
and integrating the target charging time, the charging power, the charging voltage and the charging current of each vehicle to be charged to obtain the charging parameters of the vehicle to be charged.
Preferably, the analyzing of the charging index weight of the charging pile based on the big data analysis rule according to the charging parameters of the vehicle to be charged corresponding to each charging pile includes:
acquiring a plurality of analysis indexes of the vehicle to be charged according to the charging parameters of the vehicle to be charged corresponding to each charging pile based on the big data analysis rule;
setting the same initial weight for each analysis index, and constructing a correlation matrix of a plurality of analysis indexes according to the influence parameters of the vehicle charging amount;
calculating the random response of each analysis index according to preset electric quantity data and the incidence matrix, and determining the influence weight of the analysis index and the vehicle charging quantity according to the random response of each analysis index;
determining a comprehensive distribution charging index of each charging pile according to an analysis index parameter value of each vehicle to be charged corresponding to each charging pile;
and determining the charging index weight of each charging pile according to the quotient of the comprehensive distribution charging index of each charging pile and the charging total index corresponding to all charging distribution services.
Preferably, the allocating power resources in a corresponding proportion to each charging pile according to the charging index weight of each charging pile includes:
determining a first minimum electric quantity increment value of each charging pile according to the charging index weight of the charging pile;
acquiring a response parameter corresponding to the charging dynamic trigger operation of each charging pile, and determining the power loss of the charging pile based on the response parameter;
adjusting the first minimum electric quantity increment value of each charging pile according to the electric power loss to obtain a second minimum electric quantity increment value;
and determining a power distribution proportion corresponding to the second minimum electric quantity increment value of each charging pile, and distributing corresponding power resources to the charging piles according to the power distribution proportion.
Preferably, the detecting the current service state of each charging pile includes:
detecting the current output power of each charging pile;
acquiring a corresponding state identifier according to the current output power, confirming the membership degree of the state identifier, and judging the current state of each charging pile according to the membership degree, wherein the current state comprises the following steps: a working state and an idle state;
when the current state of the charging pile is an idle state, determining that the current service state of the charging pile is no service, and when the current state of the charging pile is a working state, determining that the current service state of the charging pile is in service;
and detecting the charging progress of each first target charging pile with the current service state serving, and determining the service progress of each first target charging pile according to the charging progress.
Preferably, the allocating reasonable charging piles to each vehicle to be charged according to the vehicle type of each vehicle to be charged and the current service state of each charging pile, and determining the charging allocation task of each charging pile according to the allocation result includes:
according to the vehicle type of each vehicle to be charged, analyzing the charging characteristics of the vehicle to be charged to obtain an analysis result;
determining the maximum system electric quantity loss and the charging power requirement of each vehicle to be charged according to the analysis result of each vehicle to be charged;
determining the charging service efficiency requirement according to the maximum system electric quantity loss and the charging power requirement of each vehicle to be charged;
and distributing reasonable charging piles for each vehicle to be charged according to the charging service efficiency requirement of each vehicle to be charged and the current service state of each charging pile.
Preferably, the method further comprises:
acquiring a charging decision variable of each distribution vehicle of each charging pile;
constructing a charging business constraint condition of the charging pile according to the charging decision variable of each distributed vehicle;
evaluating the utilization rate of each charging pile based on the charging business constraint condition of the charging pile;
determining whether the utilization rate of each charging pile is greater than or equal to a preset threshold value, if so, not needing to perform subsequent operation, and if not, acquiring condition parameters corresponding to charging business constraint conditions of a second target charging pile of which the utilization rate is less than the preset threshold value;
replacing the first conditional parameter of each second target charging pile with the second conditional parameters of the rest second target charging piles until the utilization rate evaluated by the modified charging service constraint condition of each charging pile is greater than or equal to the preset threshold value;
and performing cooperative scheduling on all vehicles to be charged according to the modified charging service preset conditions of each charging pile.
Preferably, before acquiring the charging distribution service of each charging pile in the charging station, the method further includes:
collecting physiological data of each vehicle to be charged corresponding to a driver during driving;
determining the driving characteristics of each driver according to the driving physiological data of the driver;
evaluating single longest electric quantity support driving mileage data of each vehicle to be charged based on the driving characteristics of each driver;
collecting a plurality of characteristic parameters of a built-in battery pack unit of each vehicle to be charged;
inputting each characteristic parameter of each vehicle to be charged into a preset initial characteristic model to obtain an initial characteristic value corresponding to each characteristic parameter;
constructing an attenuation function of each characteristic parameter according to the attenuation characteristics of the characteristic value of the characteristic parameter;
determining a decay time series of each characteristic parameter through the decay function of the characteristic parameter;
correlating sequence factors in the decay time sequence of each characteristic parameter with the stage mileage in the single longest electric quantity support driving mileage data of each vehicle to be charged to obtain a correlation result;
determining a battery state index value of each vehicle to be charged in each stage mileage according to the correlation result;
performing power shortage judgment on the built-in battery pack unit of each vehicle to be charged based on the battery state index value of each vehicle to be charged in each stage mileage and the mileage of the vehicle to be charged in the last period, and acquiring a judgment result;
setting the charging service type of each vehicle to be charged according to the judgment result of each vehicle to be charged;
and allocating adaptive charging piles to each vehicle to be charged according to the charging service type of the vehicle to be charged.
An electric automobile charging pile power resource distribution system based on big data, the system comprises:
the acquisition module is used for acquiring the charging distribution business of each charging pile in the charging station;
the determining module is used for determining the charging parameters of the vehicles to be charged corresponding to the charging piles according to the charging distribution business of each charging pile;
the analysis module is used for analyzing the charging index weight of each charging pile according to the charging parameters of the vehicle to be charged corresponding to each charging pile based on the big data analysis rule;
and the distribution module is used for distributing power resources in a corresponding proportion to each charging pile according to the charging index weight of the charging pile.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a work flow chart of a big data-based electric vehicle charging pile power resource allocation method provided by the invention;
FIG. 2 is another work flow chart of the big data-based electric vehicle charging pile power resource allocation method provided by the invention;
FIG. 3 is a flowchart of another work of the method for allocating electric vehicle charging pile power resources based on big data according to the present invention;
fig. 4 is a schematic structural diagram of an electric vehicle charging pile power resource distribution system based on big data provided by the invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
Along with the reinforcing of environmental protection consciousness and the national attention to environmental protection, the new forms of energy electric motor car takes place at the same time of transporting, however, because the limited need of new forms of energy electric motor car battery capacity charges at any time, and the electric motor car can be guaranteed to charge at any time by the setting up of electric motor car stake of charging, then, can all be provided with the public electric pile of charging in the higher region of each electric motor car frequency of use, the electric power resource distribution system of current public electric pile of charging is each stake of charging for the system is average with electric energy distribution, make each fill electric pile and keep the same electric energy supply, but above-mentioned method has following problem: because the different so that the electric energy that its needs of each car type of filling the electric pile butt joint is also different thereby make some fill electric pile can appear the condition that the electric energy is not enough in the charging process and lead to unable punctually accomplishing vehicle charging work, greatly reduced charge efficiency. In order to solve the above problems, the embodiment discloses an electric vehicle charging pile power resource allocation method based on big data.
A big data-based electric vehicle charging pile power resource allocation method is shown in figure 1 and comprises the following steps:
s101, acquiring a charging distribution service of each charging pile in a charging station;
step S102, determining charging parameters of the vehicles to be charged corresponding to the charging piles according to the charging distribution business of each charging pile;
step S103, analyzing the charging index weight of each charging pile according to the charging parameters of the vehicle to be charged corresponding to each charging pile based on big data analysis rules;
and S104, distributing power resources in a corresponding proportion to each charging pile according to the charging index weight of each charging pile.
The working principle of the technical scheme is as follows: the charging distribution business of each charging pile in the charging station is obtained, the charging parameters of the vehicles to be charged corresponding to the charging piles are determined according to the charging distribution business of each charging pile, the charging index weight of each charging pile is analyzed according to the charging parameters of the vehicles to be charged corresponding to each charging pile based on big data analysis rules, and the power resources in the corresponding proportion are distributed to each charging pile according to the charging index weight of each charging pile.
The beneficial effects of the above technical scheme are: the charging index weight of the charging pile is analyzed according to the charging distribution business of each charging pile, and then the power resource needing to be distributed is determined, so that each charging pile can reasonably distribute electric energy according to the charging business condition of the charging pile, the problem of electric energy supply of different types of vehicles for charging of each charging pile can be solved, the practicability and the whole charging efficiency are improved, the problem that in the prior art, because the types of the vehicles for butting the charging piles are different, the electric energy needed by each charging pile is different, and therefore the situation that the electric energy is insufficient in the charging process of some charging piles can cause that the vehicle charging work can not be finished on time is solved, and the charging efficiency is greatly reduced.
In one embodiment, as shown in fig. 2, the acquiring the charging distribution service of each charging pile in the charging station includes:
step S201, acquiring a charging reservation request instruction sent by each vehicle to be charged;
step S202, determining the vehicle type of each vehicle to be charged according to a reserved charging request instruction sent by each vehicle to be charged;
step S203, detecting the current service state of each charging pile;
and S204, distributing reasonable charging piles to each vehicle to be charged according to the vehicle type of each vehicle to be charged and the current service state of each charging pile, and determining a charging distribution task of each charging pile according to a distribution result.
The beneficial effects of the above technical scheme are: the charging pile can be reasonably distributed in an idle mode according to the vehicle type of each vehicle to be charged, so that the charging efficiency is further improved, and further, the charging pile is distributed through vehicles of the same type, so that the charging pile does not need to be switched to a charging mode according to the vehicle type to stably use the same charging mode to charge the vehicle, and the practicability and the charging efficiency are further improved.
In an embodiment, as shown in fig. 3, the determining, according to the charging distribution service of each charging pile, the charging parameter of the vehicle to be charged corresponding to the charging pile includes:
step S301, determining the charging demand of the vehicle to be charged corresponding to each charging pile based on the charging distribution business of each charging pile;
step S302, acquiring charging configuration data of each vehicle to be charged corresponding to each charging pile;
step S303, determining target charging time, charging power, charging voltage and charging current of the vehicle to be charged according to the charging configuration data of each vehicle to be charged corresponding to each charging pile and the charging demand of the vehicle to be charged;
step S304, integrating the target charging time, the charging power, the charging voltage and the charging current of each vehicle to be charged to obtain the charging parameters of the vehicle to be charged.
The beneficial effects of the above technical scheme are: thereby can obtain every and treat the comprehensive charge parameter of vehicle of charging and establish the condition for follow-up the distribution of filling electric pile, further improve the practicality.
In one embodiment, the analyzing the charging index weight of each charging pile according to the charging parameter of the vehicle to be charged corresponding to the charging pile based on the big data analysis rule includes:
acquiring a plurality of analysis indexes of the vehicle to be charged according to the charging parameters of each vehicle to be charged corresponding to each charging pile based on the big data analysis rule;
setting the same initial weight for each analysis index, and constructing a correlation matrix of a plurality of analysis indexes according to the influence parameters of the vehicle charging amount;
calculating the random response of each analysis index according to preset electric quantity data and the incidence matrix, and determining the influence weight of the analysis index and the vehicle charging quantity according to the random response of each analysis index;
determining a comprehensive distribution charging index of each charging pile according to the analysis index parameter value of each vehicle to be charged corresponding to each charging pile;
and determining the charging index weight of each charging pile according to the quotient of the comprehensive distribution charging index of each charging pile and the charging total index corresponding to all charging distribution services.
The beneficial effects of the above technical scheme are: the comprehensive charging index of each charging pile can be comprehensively determined, the charging index weight of each charging pile is determined according to the corresponding index proportion, and the charging index weight distribution rationality and objectivity of each charging pile are improved.
In one embodiment, the allocating power resources in a corresponding proportion to each charging pile according to the charging index weight of the charging pile includes:
determining a first minimum electric quantity increment value of each charging pile according to the charging index weight of the charging pile;
acquiring a response parameter corresponding to the charging dynamic trigger operation of each charging pile, and determining the power loss of the charging pile based on the response parameter;
adjusting the first minimum electric quantity increment value of each charging pile according to the electric power loss to obtain a second minimum electric quantity increment value;
and determining a power distribution proportion corresponding to the second minimum electric quantity increment value of each charging pile, and distributing corresponding power resources to the charging piles according to the power distribution proportion.
The beneficial effects of the above technical scheme are: the electric quantity increment value of each charging pile is determined by calculating the electric power loss of each charging pile, so that the optimal distributed electric power can be reasonably determined according to the charging loss of each charging pile, the influence caused by working parameters of each charging pile is avoided, and the charging efficiency and the charging stability are improved.
In one embodiment, the detecting the current service state of each charging pile includes:
detecting the current output power of each charging pile;
acquiring a corresponding state identifier according to the current output power, confirming the membership degree of the state identifier, and judging the current state of each charging pile according to the membership degree, wherein the current state comprises the following steps: a working state and an idle state;
when the current state of the charging pile is an idle state, determining that the current service state of the charging pile is no service, and when the current state of the charging pile is a working state, determining that the current service state of the charging pile is in service;
and detecting the charging progress of each first target charging pile with the current service state serving, and determining the service progress of each first target charging pile according to the charging progress.
The beneficial effects of the above technical scheme are: the service state and the service progress of each charging pile are respectively determined, so that a worker can quickly and accurately know the running state of each charging pile and further provide a reference basis for follow-up arrangement of charging vehicles, and further, whether each charging pile runs can be determined more intuitively and quickly by determining the service state of each charging pile according to the current output power, and the determination efficiency and the determination accuracy are improved.
In one embodiment, the allocating reasonable charging piles to each vehicle to be charged according to the vehicle type of the vehicle and the current service state of each charging pile, and determining the charging allocation task of each charging pile according to the allocation result includes:
according to the vehicle type of each vehicle to be charged, analyzing the charging characteristics of the vehicle to be charged to obtain an analysis result;
determining the maximum system electric quantity loss and the charging power requirement of each vehicle to be charged according to the analysis result of each vehicle to be charged;
determining the charging service efficiency requirement according to the maximum system electric quantity loss and the charging power requirement of each vehicle to be charged;
and distributing reasonable charging piles for each vehicle to be charged according to the charging service efficiency requirement of each vehicle to be charged and the current service state of each charging pile.
The beneficial effects of the above technical scheme are: the charging mode corresponding to each vehicle to be charged can be accurately determined by acquiring the charging service efficiency requirement of each vehicle to be charged, and then the charging pile which is distributed with the idle high charging mode is rapidly charged, so that the charging pile distribution rationality is improved, and the charging efficiency is also improved.
In one embodiment, the method further comprises:
acquiring a charging decision variable of each distribution vehicle of each charging pile;
constructing a charging business constraint condition of the charging pile according to the charging decision variable of each distributed vehicle;
evaluating the utilization rate of each charging pile based on the charging business constraint condition of the charging pile;
determining whether the utilization rate of each charging pile is greater than or equal to a preset threshold value, if so, not needing to perform subsequent operation, and if not, acquiring condition parameters corresponding to charging business constraint conditions of a second target charging pile of which the utilization rate is less than the preset threshold value;
replacing the first condition parameter of each second target charging pile with the second condition parameters of the rest second target charging piles until the utilization rate evaluated by the modified charging business constraint condition of each charging pile is greater than or equal to the preset threshold value;
and performing cooperative scheduling on all vehicles to be charged according to the modified charging service preset conditions of each charging pile.
The beneficial effects of the above technical scheme are: the charging service preset conditions of each charging pile can be balanced, so that the distributed power resources of each charging pile can be maximally digested and utilized, the electric energy utilization rate is improved, the charging stability of each charging pile is ensured, and the working efficiency is further improved.
In one embodiment, before acquiring the charging distribution service of each charging post in the charging station, the method further comprises:
acquiring physiological data of each vehicle to be charged corresponding to a driver during driving;
determining the driving characteristics of each driver according to the driving physiological data of the driver;
evaluating single longest electric quantity support driving mileage data of each vehicle to be charged based on the driving characteristics of each driver;
collecting a plurality of characteristic parameters of a built-in battery pack unit of each vehicle to be charged;
inputting each characteristic parameter of each vehicle to be charged into a preset initial characteristic model to obtain an initial characteristic value corresponding to each characteristic parameter;
constructing an attenuation function of each characteristic parameter according to the attenuation characteristics of the characteristic value of the characteristic parameter;
determining a decay time series of each characteristic parameter through the decay function of the characteristic parameter;
correlating sequence factors in the decay time sequence of each characteristic parameter with the stage mileage in the single longest electric quantity support driving mileage data of each vehicle to be charged to obtain a correlation result;
determining a battery state index value of each vehicle to be charged in each stage mileage according to the correlation result;
performing power shortage judgment on a built-in battery pack unit of each vehicle to be charged based on a battery state index value of each vehicle to be charged in each stage mileage and mileage of the vehicle to be charged in the last period to obtain a judgment result;
setting the charging service type of each vehicle to be charged according to the judgment result of each vehicle to be charged;
and allocating adaptive charging piles to each vehicle to be charged according to the charging service type of the vehicle to be charged.
The beneficial effects of the above technical scheme are: the power shortage state of each vehicle to be charged can be evaluated in real time according to the driver driving parameters of each vehicle to be charged and the working characteristic parameters of the built-in battery pack unit, so that the charging pile can be rapidly distributed for the demand charging type of each vehicle to be charged visually, the stable charging operation of each subsequent vehicle to be charged is ensured, and the stability and the practicability are improved.
The embodiment also discloses electric automobile fills electric pile power resource distribution system based on big data, as shown in fig. 4, this system includes:
the acquiring module 401 is configured to acquire a charging distribution service of each charging pile in the charging station;
a determining module 402, configured to determine, according to the charging distribution service of each charging pile, a charging parameter of a vehicle to be charged corresponding to the charging pile;
the analysis module 403 is configured to analyze a charging index weight of each charging pile according to a charging parameter of a vehicle to be charged corresponding to the charging pile based on a big data analysis rule;
and the allocating module 404 is configured to allocate power resources in a corresponding proportion to each charging pile according to the charging index weight of the charging pile.
The working principle and the advantageous effects of the above technical solution have been explained in the method claims, and are not described herein again.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. The electric vehicle charging pile power resource distribution method based on big data is characterized by comprising the following steps:
acquiring a charging distribution service of each charging pile in a charging station;
determining the charging parameters of the vehicles to be charged corresponding to the charging piles according to the charging distribution business of each charging pile;
analyzing the charging index weight of each charging pile according to the charging parameters of the vehicles to be charged corresponding to each charging pile based on big data analysis rules;
and distributing power resources in a corresponding proportion to each charging pile according to the charging index weight of the charging pile.
2. The big data-based electric vehicle charging pile power resource distribution method according to claim 1, wherein the acquiring of the charging distribution business of each charging pile in the charging station comprises:
acquiring a charging reservation request instruction sent by each vehicle to be charged;
determining the vehicle type of each vehicle to be charged according to a reserved charging request instruction sent by each vehicle to be charged;
detecting the current service state of each charging pile;
and reasonably distributing charging piles to each vehicle to be charged according to the vehicle type of each vehicle to be charged and the current service state of each charging pile, and determining the charging distribution task of each charging pile according to the distribution result.
3. The method for allocating electric vehicle charging pile power resources based on big data according to claim 1, wherein the determining the charging parameters of the vehicle to be charged corresponding to each charging pile according to the charging allocation service of the charging pile comprises:
determining the charging demand of a vehicle to be charged corresponding to each charging pile based on the charging distribution service of each charging pile;
acquiring charging configuration data of each vehicle to be charged corresponding to each charging pile;
determining target charging time, charging power, charging voltage and charging current of the vehicle to be charged according to charging configuration data of each vehicle to be charged corresponding to each charging pile and the charging demand of the vehicle to be charged;
and integrating the target charging time length, the charging power, the charging voltage and the charging current of each vehicle to be charged to obtain the charging parameters of the vehicle to be charged.
4. The big data-based electric vehicle charging pile power resource allocation method according to claim 1, wherein the step of analyzing the charging index weight of each charging pile according to the charging parameters of the vehicle to be charged corresponding to the charging pile based on the big data analysis rules comprises the following steps:
acquiring a plurality of analysis indexes of the vehicle to be charged according to the charging parameters of each vehicle to be charged corresponding to each charging pile based on the big data analysis rule;
setting the same initial weight for each analysis index, and constructing a correlation matrix of a plurality of analysis indexes according to the influence parameters of the vehicle charging amount;
calculating the random response of each analysis index according to preset electric quantity data and the incidence matrix, and determining the influence weight of the analysis index and the vehicle charging quantity according to the random response of each analysis index;
determining a comprehensive distribution charging index of each charging pile according to the analysis index parameter value of each vehicle to be charged corresponding to each charging pile;
and determining the charging index weight of each charging pile according to the quotient of the comprehensive distribution charging index of each charging pile and the charging total index corresponding to all charging distribution services.
5. The big data-based electric vehicle charging pile power resource allocation method according to claim 1, wherein the allocating of power resources in a corresponding proportion to each charging pile according to the charging index weight of the charging pile comprises:
determining a first minimum electric quantity increment value of each charging pile according to the charging index weight of the charging pile;
acquiring a response parameter corresponding to the charging dynamic trigger operation of each charging pile, and determining the power loss of the charging pile based on the response parameter;
adjusting the first minimum electric quantity increment value of each charging pile according to the electric power loss to obtain a second minimum electric quantity increment value;
and determining an electric power distribution proportion corresponding to the second minimum electric quantity increment value of each charging pile, and distributing corresponding electric power resources to the charging piles according to the electric power distribution proportion.
6. The big data-based electric vehicle charging pile power resource allocation method according to claim 2, wherein the detecting the current service state of each charging pile comprises:
detecting the current output power of each charging pile;
acquiring a corresponding state identifier according to the current output power, confirming the membership degree of the state identifier, and judging the current state of each charging pile according to the membership degree, wherein the current state comprises the following steps: a working state and an idle state;
when the current state of the charging pile is an idle state, determining that the current service state of the charging pile is no service, and when the current state of the charging pile is a working state, determining that the current service state of the charging pile is in service;
and detecting the charging progress of each first target charging pile with the current service state serving, and determining the service progress of each first target charging pile according to the charging progress.
7. The big-data-based electric vehicle charging pile power resource allocation method according to claim 2, wherein the step of allocating reasonable charging piles to each vehicle to be charged according to the vehicle type of the vehicle and the current service state of each charging pile and determining the charging allocation task of each charging pile according to the allocation result comprises the following steps:
according to the vehicle type of each vehicle to be charged, analyzing the charging characteristics of the vehicle to be charged to obtain an analysis result;
determining the maximum system electric quantity loss and the charging power requirement of each vehicle to be charged according to the analysis result of each vehicle to be charged;
determining the charging service efficiency requirement according to the maximum system electric quantity loss and the charging power requirement of each vehicle to be charged;
and distributing reasonable charging piles for each vehicle to be charged according to the charging service efficiency requirement of each vehicle to be charged and the current service state of each charging pile.
8. The big data-based electric vehicle charging pile power resource allocation method according to claim 1, further comprising the following steps:
acquiring a charging decision variable of each distribution vehicle of each charging pile;
constructing a charging business constraint condition of the charging pile according to the charging decision variable of each distributed vehicle;
evaluating the utilization rate of each charging pile based on the charging business constraint condition of the charging pile;
determining whether the utilization rate of each charging pile is greater than or equal to a preset threshold, if so, not needing subsequent operation, and if not, acquiring condition parameters corresponding to charging service constraint conditions of a second target charging pile of which the utilization rate is less than the preset threshold;
replacing the first condition parameter of each second target charging pile with the second condition parameters of the rest second target charging piles until the utilization rate evaluated by the modified charging business constraint condition of each charging pile is greater than or equal to the preset threshold value;
and performing cooperative scheduling on all vehicles to be charged according to the modified charging service preset conditions of each charging pile.
9. The big data-based electric vehicle charging pile power resource distribution method according to claim 1, wherein before acquiring the charging distribution business of each charging pile in the charging station, the method further comprises the following steps:
acquiring physiological data of each vehicle to be charged corresponding to a driver during driving;
determining the driving characteristics of each driver according to the driving physiological data of the driver;
evaluating single longest electric quantity support driving mileage data of each vehicle to be charged based on the driving characteristics of each driver;
collecting a plurality of characteristic parameters of a built-in battery pack unit of each vehicle to be charged;
inputting each characteristic parameter of each vehicle to be charged into a preset initial characteristic model to obtain an initial characteristic value corresponding to each characteristic parameter;
constructing an attenuation function of each characteristic parameter according to the attenuation characteristics of the characteristic value of the characteristic parameter;
determining a decay time series of each characteristic parameter through the decay function of the characteristic parameter;
correlating sequence factors in the decay time sequence of each characteristic parameter with the stage mileage in the single longest electric quantity support driving mileage data of each vehicle to be charged to obtain a correlation result;
determining a battery state index value of each vehicle to be charged in each stage mileage according to the correlation result;
performing power shortage judgment on the built-in battery pack unit of each vehicle to be charged based on the battery state index value of each vehicle to be charged in each stage mileage and the mileage of the vehicle to be charged in the last period, and acquiring a judgment result;
setting the charging service type of each vehicle to be charged according to the judgment result of each vehicle to be charged;
and allocating adaptive charging piles to each vehicle to be charged according to the charging service type of the vehicle to be charged.
10. The utility model provides an electric automobile fills electric pile power resource distribution system based on big data which characterized in that, this system includes:
the acquisition module is used for acquiring the charging distribution business of each charging pile in the charging station;
the determining module is used for determining the charging parameters of the vehicles to be charged corresponding to the charging piles according to the charging distribution business of each charging pile;
the analysis module is used for analyzing the charging index weight of each charging pile according to the charging parameters of the vehicle to be charged corresponding to each charging pile based on the big data analysis rule;
and the distribution module is used for distributing power resources in a corresponding proportion to each charging pile according to the charging index weight of the charging pile.
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