CN110322120B - Electric vehicle charging scheduling method and system based on benefit maximization - Google Patents
Electric vehicle charging scheduling method and system based on benefit maximization Download PDFInfo
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Abstract
The invention discloses an electric vehicle charging scheduling method and system based on benefit maximization, wherein the method comprises the following steps of S1: obtaining a recommended charging station position for a user according to the collected road condition information, the current vehicle information, the information of nearby charging stations and a benefit maximization principle; s2: obtaining the charging electric quantity recommended for the user according to the historical driving information of the vehicle and the benefit maximization principle; s3: and charging according to the position of the charging station and the charging electric quantity. According to the electric vehicle charging scheduling method and system based on the benefit maximization, the most suitable charging amount and charging position are recommended for the user on the premise of the overall benefit maximization, and when the charging station cannot meet the user requirement, a mobile charging station can be additionally assigned to relieve the requirement pressure.
Description
Technical Field
The invention relates to the technical field of electric vehicle scheduling, in particular to an electric vehicle charging scheduling method and system based on benefit maximization.
Background
With the continuous development of electric vehicle technology and the popularization of electric vehicles, how to reasonably utilize the resources of the existing charging service stations becomes the focus of attention on the development of electric vehicle technology. In particular, different regions, for example: the utilization rate of the charging service station shows larger difference in urban centers and suburbs; the utilization rates of the charging service stations at different times, leisure time and before morning and evening peak periods are also very different. Moreover, most users are bound to a certain charging station to charge at the peak time, and the vehicle is not stopped until the vehicle is full. The heap charging at the same place at the same time can lead a large number of taxis to stay at the charging service station to provide no service, greatly aggravate the imbalance of supply and demand of the taxi and passengers, and lead the income of taxi drivers to be reduced due to a large amount of charging waiting time. Obviously, the current charging mode is not reasonable.
Therefore, how to provide a more reasonable electric vehicle charging scheduling method is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides an electric vehicle charging scheduling method and system based on benefit maximization, and the scheduling method is more reasonable and can relieve the charging pressure of the electric vehicle.
In order to achieve the purpose, the invention adopts the following technical scheme:
an electric vehicle charging scheduling method based on benefit maximization comprises the following steps:
s1: obtaining a recommended charging station position for a user according to the collected road condition information, the current vehicle information, the information of nearby charging stations and a benefit maximization principle;
s2: obtaining the charging electric quantity recommended for the user according to the historical driving information of the vehicle and the benefit maximization principle;
s3: and charging according to the position of the charging station and the charging electric quantity.
Preferably, the method further comprises step S4: if the number of the vehicles needing to be charged exceeds the preset proportion of the charging potential of the charging station or the waiting charging time is longer than the preset value, adding a mobile charging station to charge the vehicles; and obtaining the number of charging positions of the charging stations and the waiting charging time according to the information of the nearby charging stations.
Preferably, step S1 specifically includes:
s11: collecting current road condition information, current vehicle information and queuing information of nearby charging stations;
s12: and learning the information collected in the step S11 through a deep learning convolutional network based on the benefit maximization principle to obtain the charging station position recommended for the user.
Preferably, step S2 specifically includes:
s21: collecting historical power consumption and working time information of the vehicle;
s22: analyzing the data collected in the step S21 through a deep learning convolution network based on a benefit maximization principle, and recommending charging electric quantity to a user based on the current road condition information and the predicted road condition information; and the predicted road condition information is obtained by inputting the collected road condition information into a deep learning convolution network for prediction.
Preferably, step S4 specifically includes:
s41: collecting the current vehicle information needing to be charged and the vehicle condition;
s42: different weights are distributed to different vehicles, the center point of the cluster is calculated through a clustering method, and the position of the mobile charging station is deployed at the center point of the cluster.
An electric vehicle charging scheduling system based on benefit maximization, comprising:
the charging station position recommending module is used for obtaining a charging station position recommended for a user according to the collected road condition information, the current vehicle information, the information of nearby charging stations and a benefit maximizing principle;
the charging electric quantity recommendation module is used for obtaining the charging electric quantity recommended for the user according to the historical driving information of the vehicle and the benefit maximization principle;
and the charging module is used for charging according to the position of the charging station and the charging electric quantity.
Preferably, the system further comprises a mobile charging station scheduling module, which is used for allocating the mobile charging stations to charge the vehicles when the number of the vehicles needing to be charged exceeds the preset proportion of the charging level of the charging stations or the waiting charging time is greater than the preset value; and obtaining the number of charging positions of the charging stations and the waiting charging time according to the information of the nearby charging stations.
Preferably, the charging station location recommending module specifically includes:
the first collecting unit is used for collecting current road condition information, current vehicle information and queuing information of nearby charging stations;
and the first recommending unit is used for learning the information collected by the first collecting unit through a deep learning convolution network based on the benefit maximization principle to obtain the charging station position recommended for the user.
Preferably, the charging capacity recommending module specifically includes:
the second collection unit is used for collecting historical power consumption and working time information of the vehicle;
the second recommending unit is used for analyzing the data collected by the second collecting unit through a deep learning convolution network based on the benefit maximization principle and recommending the charging electric quantity to the user based on the current road condition information and the predicted road condition information; and the predicted road condition information is obtained by inputting the collected road condition information into a deep learning convolution network for prediction.
Preferably, the mobile charging station scheduling module specifically includes:
the third collecting unit is used for collecting the current vehicle information needing to be charged and the vehicle condition;
and the cluster deployment unit is used for distributing different weights for different vehicles, calculating the central point of the cluster by a clustering method, and deploying the position of the mobile charging station at the central point of the cluster.
According to the technical scheme, compared with the prior art, the electric vehicle charging scheduling method and system based on the maximization of the benefit are provided, the most suitable charging amount and charging position are recommended for the user on the premise of the maximization of the global benefit, and when the charging station cannot meet the user requirement, a mobile charging station can be added to relieve the requirement pressure.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flow chart of an electric vehicle charging scheduling method based on benefit maximization according to the present invention;
FIG. 2 is a schematic view of a scenario of an electric vehicle charging scheduling system based on benefit maximization according to the present invention;
FIG. 3 is a schematic diagram of a framework of an electric vehicle charging scheduling system based on benefit maximization according to the present invention;
fig. 4 is a flowchart for implementing benefit maximization-based electric taxi charging scheduling provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to the accompanying drawing 1, the embodiment of the invention discloses an electric vehicle charging scheduling method based on benefit maximization, which specifically comprises the following steps:
s1: obtaining a recommended charging station position for a user according to the collected road condition information, the current vehicle information, the information of nearby charging stations and a benefit maximization principle;
s2: obtaining the charging electric quantity recommended for the user according to the historical driving information of the vehicle and the benefit maximization principle;
s3: and charging according to the position of the charging station and the charging electric quantity.
It should be noted that, in the specific implementation, the sequential execution relationship between step S1 and step S2 is not limited, and the charging station position may be obtained first and then the charging amount, the charging amount may be obtained first and then the charging station position may be obtained, or the charging station position and the charging amount may be obtained simultaneously.
In addition, the maximum benefit here means that the charging waiting time of the electric vehicle is shortest and the running time is longest.
In practice, an electric driver may concentrate on charging for some period of time (e.g., before a peak commute), and may fully charge the vehicle. Therefore, a large number of taxis stay in the charging service station and cannot enjoy charging service due to the fact that the same-place bundling charging at the same time can cause that the supply and demand of the taxis and passengers are unbalanced, and the income of taxi drivers is reduced due to a large amount of charging waiting time. Therefore, each driver is allowed to be partially charged, partial charging electric quantity is recommended according to historical driving information (such as electric consumption, working time and the like) of the taxi, the charging requirement of the taxi can be met, the running time is ensured, other taxis can be charged, the waiting time is shortened, and the overall benefit is maximized. Moreover, if a large number of fixed charging stations are deployed, the charging station resources cannot be reasonably utilized in leisure time, and resource waste is easily caused. Therefore, the present invention proposes to deploy a mobile charging vehicle to alleviate the conflict between short supply and short demand of the vehicle and the charging station, specifically refer to step S4.
In order to further optimize the above technical solution, on the basis of the above embodiment, the method further includes step S4: if the number of the vehicles needing to be charged exceeds the preset proportion of the charging potential of the charging station or the waiting charging time is longer than the preset value, adding a mobile charging station to charge the vehicles; and obtaining the number of charging positions of the charging stations and the waiting charging time according to the information of the nearby charging stations.
It should be noted here that, in a specific implementation, the preset ratio may be set to 40%, and the preset value may be set to 1 hour, for example: when the number of vehicles needing to be charged exceeds 40% of the charging potential number of the charging station, or the waiting charging time exceeds 1 hour, the mobile charging vehicle is added. In specific implementation, the preset proportion and the preset value can be defined according to specific situations. In particular, the mobile charging station may be a mobile charging cart.
When the charging demand of the vehicle greatly exceeds the supply quantity of the charging station, or when the vehicle demand suddenly increases, the charging pressure is relieved by adding and moving the charging station to solve the urgent need.
In order to further optimize the above technical solution, it is further defined that step S1 specifically includes, on the basis of the above embodiment:
s11: collecting current road condition information, current vehicle information and queuing information of nearby charging stations;
s12: and learning the information collected in the step S11 through a deep learning convolutional network based on the benefit maximization principle to obtain the charging station position recommended for the user.
Because many users will concentrate on a certain charging station to charge, the centralized charging will cause a large amount of queuing phenomena, and there will be a problem of resource utilization imbalance. Therefore, according to the technical scheme provided by the invention, the most appropriate charging station location is provided for the user on the premise of sufficient electric quantity of the user according to the current position of the user, the current information of the vehicle of the user, the current road condition information and the queuing conditions of the surrounding charging stations.
In order to further optimize the above technical solution, it is further defined that step S2 specifically includes, on the basis of the above embodiment:
s21: collecting historical power consumption and working time information of the vehicle;
s22: analyzing the data collected in the step S21 through a deep learning convolution network based on a benefit maximization principle, and recommending charging electric quantity to a user based on the current road condition information and the predicted road condition information; and the predicted road condition information is obtained by inputting the collected road condition information into a deep learning convolution network for prediction.
This can result in a vehicle that is charged for an extended period of time and a longer waiting period for the driver behind, since many users will fill the vehicle to continue driving. The technical scheme provided by the invention recommends a proper partially-charged electric quantity for the user according to the historical electric consumption and the working time of the vehicle so as to meet the requirement of maximizing the global benefit.
In order to further optimize the above technical solution, it is further defined that step S4 specifically includes, on the basis of the above embodiment:
s41: collecting the current vehicle information needing to be charged and the vehicle condition;
s42: different weights are distributed to different vehicles, the center point of the cluster is calculated through a clustering method, and the position of the mobile charging station is deployed at the center point of the cluster.
In the step, the user vehicles are imagined as points one by one, each point is endowed with different weights according to the current vehicle information, and then all the points are clustered, wherein the center of the cluster is the position of the mobile charging station. Since the mobile charging vehicle cannot be located at the center of the road, it is recommended that the mobile vehicle enter the nearest parking lot to provide service to the vehicle that needs charging.
Referring to fig. 3, in addition, an embodiment of the present invention further discloses an electric vehicle charging scheduling system based on benefit maximization, including:
the charging station position recommending module is used for obtaining a charging station position recommended for a user according to the collected road condition information, the current vehicle information, the information of nearby charging stations and a benefit maximizing principle;
the charging electric quantity recommendation module is used for obtaining the charging electric quantity recommended for the user according to the historical driving information of the vehicle and the benefit maximization principle;
and the charging module is used for charging according to the position of the charging station and the charging electric quantity.
In order to further optimize the technical scheme, on the basis of the embodiment, the system further comprises a mobile charging station scheduling module, wherein the mobile charging station scheduling module is used for allocating a mobile charging station to charge the vehicles when the number of the vehicles needing to be charged exceeds a preset proportion of charging potential of the charging station or the waiting charging time is greater than a preset value; and obtaining the number of charging positions of the charging stations and the waiting charging time according to the information of the nearby charging stations.
In order to further optimize the above technical solution, on the basis of the above embodiment, it is further defined that the charging station location recommendation module specifically includes:
the first collecting unit is used for collecting current road condition information, current vehicle information and queuing information of nearby charging stations;
and the first recommending unit is used for learning the information collected by the first collecting unit through a deep learning convolution network based on the benefit maximization principle to obtain the charging station position recommended for the user.
In order to further optimize the technical solution, on the basis of the above embodiment, it is further defined that the charging electric quantity recommendation module specifically includes:
the second collection unit is used for collecting historical power consumption and working time information of the vehicle;
the second recommending unit is used for analyzing the data collected by the second collecting unit through a deep learning convolution network based on the benefit maximization principle and recommending the charging electric quantity to the user based on the current road condition information and the predicted road condition information; and the predicted road condition information is obtained by inputting the collected road condition information into a deep learning convolution network for prediction.
In order to further optimize the above technical solution, on the basis of the above embodiment, it is further defined that the mobile charging station scheduling module specifically includes:
the third collecting unit is used for collecting the current vehicle information needing to be charged and the vehicle condition;
and the cluster deployment unit is used for distributing different weights for different vehicles, calculating the central point of the cluster by a clustering method, and deploying the position of the mobile charging station at the central point of the cluster.
With reference to fig. 2 and fig. 4, fig. 2 is a schematic view illustrating a scene of an electric vehicle charging scheduling system based on benefit maximization, and fig. 4 is a flowchart illustrating a process for implementing electric taxi charging scheduling based on benefit maximization:
1) a user submits a charging application to a cloud server;
2) the cloud server can recommend the most suitable charging station position for the cloud server according to the currently collected information;
3) the cloud server recommends the most appropriate charging amount for the cloud server according to the currently collected information;
4) if the charging station can not meet the requirements of the vehicles needing to be charged at present, the mobile charging station is deployed.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (4)
1. An electric vehicle charging scheduling method based on benefit maximization is characterized by comprising the following steps:
s1: obtaining a recommended charging station position for a user according to the collected road condition information, the current vehicle information, the information of nearby charging stations and a benefit maximization principle;
s2: obtaining the charging electric quantity recommended for the user according to the historical driving information of the vehicle and the benefit maximization principle; step S2 specifically includes:
s21: collecting historical power consumption and working time information of the vehicle;
s22: analyzing the data collected in the step S21 through a deep learning convolution network based on a benefit maximization principle, and recommending charging electric quantity to a user based on the current road condition information and the predicted road condition information; the predicted road condition information is obtained by inputting the collected road condition information into a deep learning convolution network for prediction;
s3: charging according to the position of the charging station and the charging electric quantity;
the benefit maximization principle means that the charging waiting time of the electric vehicle is shortest and the running time is longest;
step S4: if the number of the vehicles needing to be charged exceeds the preset proportion of the charging potential of the charging station or the waiting charging time is longer than the preset value, adding a mobile charging station to charge the vehicles; the number of charging positions of the charging stations and the waiting charging time are obtained according to the information of the nearby charging stations;
step S4 specifically includes:
s41: collecting the current vehicle information needing to be charged and the vehicle condition;
s42: different weights are distributed to different vehicles, the center point of the cluster is calculated through a clustering method, and the position of the mobile charging station is deployed at the center point of the cluster.
2. The electric vehicle charging scheduling method based on the benefit maximization according to claim 1, wherein the step S1 specifically comprises:
s11: collecting current road condition information, current vehicle information and queuing information of nearby charging stations;
s12: and learning the information collected in the step S11 through a deep learning convolutional network based on the benefit maximization principle to obtain the charging station position recommended for the user.
3. An electric vehicle charging scheduling system based on benefit maximization, characterized by comprising:
the charging station position recommending module is used for obtaining a charging station position recommended for a user according to the collected road condition information, the current vehicle information, the information of nearby charging stations and a benefit maximizing principle;
the charging electric quantity recommendation module is used for obtaining the charging electric quantity recommended for the user according to the historical driving information of the vehicle and the benefit maximization principle; the charging electric quantity recommendation module specifically comprises:
the second collection unit is used for collecting historical power consumption and working time information of the vehicle;
the second recommending unit is used for analyzing the data collected by the second collecting unit through a deep learning convolution network based on the benefit maximization principle and recommending the charging electric quantity to the user based on the current road condition information and the predicted road condition information; the predicted road condition information is obtained by inputting the collected road condition information into a deep learning convolution network for prediction;
the charging module is used for charging according to the position of the charging station and the charging electric quantity;
the benefit maximization principle means that the charging waiting time of the electric vehicle is shortest and the running time is longest;
the mobile charging station dispatching module is used for assigning the mobile charging stations to charge the vehicles when the number of the vehicles needing to be charged exceeds a preset proportion of charging potential of the charging stations or waiting for charging time is greater than a preset value; the number of charging positions of the charging stations and the waiting charging time are obtained according to the information of the nearby charging stations;
the mobile charging station dispatching module specifically comprises:
the third collecting unit is used for collecting the current vehicle information needing to be charged and the vehicle condition;
and the cluster deployment unit is used for distributing different weights for different vehicles, calculating the central point of the cluster by a clustering method, and deploying the position of the mobile charging station at the central point of the cluster.
4. The system as claimed in claim 3, wherein the charging station location recommendation module specifically comprises:
the first collecting unit is used for collecting current road condition information, current vehicle information and queuing information of nearby charging stations;
and the first recommending unit is used for learning the information collected by the first collecting unit through a deep learning convolution network based on the benefit maximization principle to obtain the charging station position recommended for the user.
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| CN113240264B (en) * | 2021-05-10 | 2024-01-05 | 成都特来电新能源有限公司 | Intelligent scheduling method and system for electric vehicle |
| CN114919433B (en) * | 2022-05-27 | 2022-12-09 | 深圳先进技术研究院 | Electric vehicle cluster charging and discharging control method, system and related equipment |
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