CN112560588A - Electric vehicle charging station man-vehicle management and control method and system based on image recognition - Google Patents
Electric vehicle charging station man-vehicle management and control method and system based on image recognition Download PDFInfo
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Abstract
The invention discloses an image recognition-based electric vehicle charging station man-vehicle management and control method and system, and belongs to the technical field of electric vehicle management and control. The invention relates to an image recognition-based electric vehicle charging station man-vehicle management and control method, which comprises an operation method and an overhaul method; the operation method comprises the following steps: and allowing the new energy vehicles and the white list vehicles to drive into the charging parking spaces for charging. The vehicle is not charged after driving in or is not driven away after charging, and voice broadcast prompt is carried out; for the vehicles with the mobile phone and the license plate numbers registered on the operation platform, simultaneously sending a short message to inform the driver of driving away; through continuous exploration and test, the charging non-standard behavior is timely reminded through an image recognition technology, a voice prompt device and a mobile phone short message technology, the charging non-standard behavior is effectively prevented, further, the fault of the charging pile is effectively avoided, and the utilization rate of the charging pile is improved.
Description
Technical Field
The invention relates to an image recognition-based electric vehicle charging station man-vehicle management and control method and system, and belongs to the technical field of electric vehicle management and control.
Background
Charging facilities require operator continuous maintenance, and under cost pressure, operators have limited manpower and auxiliary equipment available in terms of operations and maintenance. At present, a monitoring camera is generally installed, an independent access gate is installed in part conditionally, and a few charging stations are provided with parking lock management and control systems for managing and controlling the occupation of a fuel vehicle. And the daily maintenance of the equipment needs to be equipped with maintenance personnel.
At present, most of operators have charging pile faults, such as damage of a charging gun head, and most of the faults are caused by that a charging car owner hangs a gun according to requirements, randomly throws the charging gun on the ground and is rolled by wheels or is exposed to the sun and rain. To this kind of the nonstandard action of charging, the present majority fills electric pile and can not fine prevention, has influenced the utilization ratio that fills electric pile.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide the image recognition-based electric vehicle charging station man-car management and control method and system which can prompt the charging non-standard behavior in time, effectively prevent the charging non-standard behavior and the charging pile fault and improve the charging pile utilization rate.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a man-car management and control method for an electric vehicle charging station based on image recognition comprises an operation method and an overhaul method;
the operation method comprises the following steps:
allowing new energy vehicles and white list vehicles to drive into a charging parking space for charging, and carrying out voice broadcast prompting without charging after driving or without driving away after charging; for the vehicles with the mobile phone and the license plate numbers registered on the operation platform, simultaneously sending a short message to inform the driver of driving away;
carrying out image recognition on the condition that the charging gun is not inserted back into the charging pile after charging is finished, carrying out voice broadcast prompting after the charging gun is inserted back into the charging pile, and simultaneously sending a short message to prompt gun hanging for the vehicle with a mobile phone and a license plate number registered on an operation platform;
under the condition of equipment failure, if the vehicle owner is identified to drive in or prepare to drive in the charging station, the equipment failure is prompted through voice, the vehicle owner is guided to go to other charging stations or other charging stations, and meanwhile, the condition that the vehicle drives in the charging station to influence maintenance is avoided;
the overhaul method comprises the following steps:
dividing a fault maintenance state and a routine maintenance state, under the two conditions, distributing maintenance tasks to a certain maintenance worker through an operation platform, and automatically switching to the maintenance state after the maintenance worker arrives at a specific charging station and the camera recognizes the face characteristics of the maintenance worker;
in an overhaul state, when a door of a communication cabinet or a faulty charging pile is opened, an alarm on a platform cannot be triggered, and when personnel enter a warning area, the alarm cannot be triggered; the charging potential in the health state in the same charging station still keeps the operation state. After the fault is eliminated, the operation and inspection personnel manually recover to the operation state.
Through continuous exploration and test, the charging non-standard behavior is timely reminded through an image recognition technology, a voice prompt device and a mobile phone short message technology, the charging non-standard behavior is effectively prevented, further, the fault of the charging pile is effectively avoided, and the utilization rate of the charging pile is improved.
Furthermore, the vehicle can be controlled by utilizing the independent entrance and exit barrier gate, the license plate recognition camera and the barrier gate are used for distinguishing the license plate of the new energy source from other license plates, and the new energy source vehicle is released only by opening the barrier.
As a preferable technical measure:
the method for determining the new energy vehicle and the white list vehicle to drive into the charging parking space comprises the following steps:
whether a vehicle approaches is determined through the image acquisition of the camera and the input of the parking space lock sensor.
Whether a vehicle approaches is determined by two factors, namely the movement of the camera and the vehicle approach induction of the parking spot lock. Compared with the detection only by means of the movement of the camera, the missing rate is lower. When either one of the two detects that the vehicle approaches, the image acquisition is triggered.
As a preferable technical measure:
the following data information is obtained through camera image acquisition: whether a cabinet door is opened, whether a charging gun is inserted back or not, whether charging is performed, whether a charging vehicle identity is charged, and whether a transportation and inspection person is present or not;
and (4) obtaining results of an operation state and a maintenance state by combining whether a vehicle is stopped at the parking space sensed by the parking space lock and performing algorithm analysis, wherein the operation state further comprises an operation normal state and an operation alarm state.
As a preferable technical measure:
for the operation alarm state, the man-vehicle management and control system can give on-site voice prompt and mobile phone short message prompt according to the generated time, duration and vehicle owner information corresponding to the vehicle identity.
As a preferable technical measure:
the image recognition method comprises image processing and image feature extraction;
the image processing specifically includes: image sampling, image enhancement, image restoration, image coding and compression and image segmentation;
the image feature extraction specifically comprises:
and extracting color features, texture features, shape features and local feature points of the graph.
As a preferable technical measure:
the image identification method based on the color features is used for color images, and classification identification is carried out through the simplicity of a color histogram and insensitivity of the color histogram along with the size and rotation transformation of the images;
the image recognition method based on the texture features is completed by analyzing the features with structural rules in the image or counting the distribution information of the color intensity in the image;
the image recognition method based on the shape features forms a visual feature vector by finding the shape of an object in an image and describing the shape, so as to finish the classification of different images, wherein the shape features comprise perimeter, area, circularity and eccentricity.
As a preferable technical measure:
the classification and identification method is a naive Bayes classification method, and the working process is as follows:
using one n-dimensional feature vector X ═ X for each data sample1，x2，...xnRepresents, describing n metrics for n attribute a1, a2, … An samples, respectively;
there are m classes C1，C2，…Cm(ii) a Given an unknown data sample X, the naive Bayes classification method predicts that X belongs to the class with the highest posterior probability;
that is, naive Bayes classification assigns unknown samples to class CiAnd if and only if
P(Ci/X)＞P(Cj/X)，1≤j≤m，j≠i
Maximizing probability P (C)iX); its probability P (C)i/X) largest class CiReferred to as maximum a posteriori assumption.
As a preferable technical measure:
the naive Bayes classification method has the calculation formula as follows:
since P (X) is constant for all classes, for P (X/C)i)P(Ci) Maximization;
if the prior probabilities of classes are unknown, it is generally assumed that the classes are equi-probable, i.e., P (C) at the same time1)＝P(C2)＝…P(Cm) (ii) a And accordingly only for P (X/C)i) Maximization; otherwise, maximize P (X/C)i)P(Ci) (ii) a Prior probability of class P (C)i)＝si/s
Calculating s thereiniIs of the class CiAnd s is the total number of training samples; given a data set with many attributes, P (X/C) is calculatedi) May not beIs usually large; calculating P (X/C) for reducingi) The overhead of (2) can make a naive assumption that the class conditions are independent;
given the class labels of the samples, the attribute values are mutually condition-independent, namely, no dependency exists among the attributes; in this way it is possible to obtain,
as a preferable technical measure:
probability P (X)1/Ci)，P(X2/Ci)，...P(Xn/Ci) ,.. estimating from the training samples, wherein
(a) If the attribute Ak is a classification attribute, P (X)k/Ci)＝sik/si，
Wherein s isikIs to have a value X on the attribute AkkClass C ofiNumber of samples of (1), and siIs CiThe number of training samples in (1);
(b) if the attribute Ak is a continuous-value attribute, it is generally assumed that the attribute follows a gaussian distribution, and, thus,
wherein, given class CiThe value of the training sample property Ak,
is a gaussian density function of the attribute Ak and is respectively a mean value and a standard deviation;
classifying the unknown sample X, and classifying each class CiCalculating
Sample X is assigned to class CiAnd if and only if
I.e. X is assigned to it
Largest class Ci。
As a preferable technical measure:
an electric vehicle charging station man-vehicle management and control system based on image recognition,
by applying the human-vehicle control method of the electric vehicle charging station based on image recognition,
the parking lot comprises a camera, a parking spot lock, a main controller and a loudspeaker;
the camera is used for collecting images of vehicles and human faces;
the parking spot lock is arranged on the charging position and used for controlling the driving authority of the vehicle;
the loudspeaker is used for prompting a charging vehicle owner;
the main controller is used for recognizing human faces and license plates based on the collected images and videos and controlling parking spot locks and speakers based on a human-vehicle management and control method of the electric vehicle charging station.
Compared with the prior art, the invention has the following beneficial effects:
the invention breaks through the prejudice of the prior art through continuous exploration and test. According to the invention, the charging non-standard behavior is timely reminded through an image recognition technology, a voice prompt device and a mobile phone short message technology, so that the charging non-standard behavior is effectively prevented, the fault of the charging pile is effectively avoided, and the utilization rate of the charging pile is improved.
Drawings
FIG. 1 is a schematic diagram of the present inventor's vehicle management and control method inputting collected data;
FIG. 2 is a schematic diagram illustrating a state evaluation process of the vehicle management and control method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
As shown in fig. 1-2, an electric vehicle charging station people and vehicle management and control method based on image recognition includes an operation method and an overhaul method; the operation method comprises the following steps:
allowing new energy vehicles and white list vehicles to drive into a charging parking space for charging, and carrying out voice broadcast prompting without charging after driving or without driving away after charging; for the vehicles with the mobile phone and the license plate numbers registered on the operation platform, simultaneously sending a short message to inform the driver of driving away;
carrying out image recognition on the condition that the charging gun is not inserted back into the charging pile after charging is finished, carrying out voice broadcast prompting after the charging gun is inserted back into the charging pile, and simultaneously sending a short message to prompt gun hanging for the vehicle with a mobile phone and a license plate number registered on an operation platform;
under the condition of equipment failure, if the vehicle owner is identified to drive in or prepare to drive in the charging station, the equipment failure is prompted through voice, the vehicle owner is guided to go to other charging stations or other charging stations, and meanwhile, the condition that the vehicle drives in the charging station to influence maintenance is avoided;
the overhaul method comprises the following steps:
dividing a fault maintenance state and a routine maintenance state, under the two conditions, distributing maintenance tasks to a certain maintenance worker through an operation platform, and automatically switching to the maintenance state after the maintenance worker arrives at a specific charging station and the camera recognizes the face characteristics of the maintenance worker;
in an overhaul state, when a door of a communication cabinet or a faulty charging pile is opened, an alarm on a platform cannot be triggered, and when personnel enter a warning area, the alarm cannot be triggered; the charging potential in the health state in the same charging station still keeps the operation state. After the fault is eliminated, the operation and inspection personnel manually recover to the operation state.
Through continuous exploration and test, the charging non-standard behavior is timely reminded through an image recognition technology, a voice prompt device and a mobile phone short message technology, the charging non-standard behavior is effectively prevented, further, the fault of the charging pile is effectively avoided, and the utilization rate of the charging pile is improved.
Furthermore, the vehicle can be controlled by utilizing the independent entrance and exit barrier gate, the license plate recognition camera and the barrier gate are used for distinguishing the license plate of the new energy source from other license plates, and the new energy source vehicle is released only by opening the barrier.
The invention relates to a specific embodiment of a method for determining a charging parking space for driving into a charging parking space, which comprises the following steps:
the method for determining the new energy vehicle and the white list vehicle to drive into the charging parking space comprises the following steps:
whether a vehicle approaches is determined through the image acquisition of the camera and the input of the parking space lock sensor.
Whether a vehicle approaches is determined by two factors, namely the movement of the camera and the vehicle approach induction of the parking spot lock. Compared with the detection only by means of the movement of the camera, the missing rate is lower. When either one of the two detects that the vehicle approaches, the image acquisition is triggered.
The invention obtains a specific embodiment of data information through a camera image:
the following data information is obtained through camera image acquisition: whether a cabinet door is opened, whether a charging gun is inserted back or not, whether charging is performed, whether a charging vehicle identity is charged, and whether a transportation and inspection person is present or not;
and (4) obtaining results of an operation state and a maintenance state by combining whether a vehicle is stopped at the parking space sensed by the parking space lock and performing algorithm analysis, wherein the operation state further comprises an operation normal state and an operation alarm state.
For the operation alarm state, the man-vehicle management and control system can give on-site voice prompt and mobile phone short message prompt according to the generated time, duration and vehicle owner information corresponding to the vehicle identity.
The image recognition method of the invention comprises the following specific embodiments:
the image recognition method comprises image processing and image feature extraction;
the image processing specifically includes: image sampling, image enhancement, image restoration, image coding and compression and image segmentation;
the image feature extraction specifically comprises:
and extracting color features, texture features, shape features and local feature points of the graph.
The image identification method based on the color features is used for color images, and classification identification is carried out through the simplicity of a color histogram and insensitivity of the color histogram along with the size and rotation transformation of the images;
the image recognition method based on the texture features is completed by analyzing the features with structural rules in the image or counting the distribution information of the color intensity in the image;
the image recognition method based on the shape features forms a visual feature vector by finding the shape of an object in an image and describing the shape, so as to finish the classification of different images, wherein the shape features comprise perimeter, area, circularity and eccentricity.
The classification and identification method is a naive Bayes classification method, and the working process is as follows:
each data sample is represented by an n-dimensional feature vector X ═ X1，x2，...xnRepresents, describing n metrics for n attribute a1, a2, … An samples, respectively;
there are m classes C1，C2，…Cm(ii) a Given an unknown data sample X, the naive Bayes classification method predicts that X belongs to the class with the highest posterior probability;
that is, naive Bayes classification assigns unknown samples to class CiAnd if and only if
P(Ci/X)＞P(Cj/X)，1≤j≤m，j≠i
Maximizing probability P (C)iX); its probability P (C)i/X) largest class CiCalled maximum afterAnd (5) performing assumption.
One specific embodiment of the present invention:
the naive Bayes classification method has the calculation formula as follows:
since P (X) is constant for all classes, for P (X/C)i)P(Ci) Maximization;
if the prior probabilities of classes are unknown, it is generally assumed that the classes are equi-probable, i.e., P (C) at the same time1)＝P(C2)＝…P(Cm)；
And accordingly only for P (X/C)i) Maximization; otherwise, maximize P (X/C)i)P(Ci) (ii) a Prior probability of class P (C)i)＝siCalculating where siIs of the class CiAnd s is the total number of training samples; given a data set with many attributes, P (X/C) is calculatedi) The overhead of (a) can be very large; calculating P (X/C) for reducingi) The overhead of (2) can make a naive assumption that the class conditions are independent;
given the class labels of the samples, the attribute values are mutually condition-independent, namely, no dependency exists among the attributes; in this way it is possible to obtain,
probability P (X)1/Ci)，P(X2/Ci)，...P(Xn/Ci) ,.. estimating from the training samples, wherein
(a) If the attribute Ak is a classification attribute, P (X)k/Ci)＝sik/si，
Wherein s isikIs to have a value X on the attribute AkkClass C ofiNumber of samples of (1), and siIs CiThe number of training samples in (1);
(b) if the attribute Ak is a continuous-value attribute, it is generally assumed that the attribute follows a gaussian distribution, and, thus,
wherein, given class CiThe value of the training sample property Ak,
is a gaussian density function of the attribute Ak and is respectively a mean value and a standard deviation;
classifying the unknown sample X, and classifying each class CiCalculating
Sample X is assigned to class CiAnd if and only if
I.e. X is assigned to it
Largest class Ci。
The invention applies a specific embodiment of a man-car management and control method of an electric vehicle charging station:
an electric vehicle charging station man-vehicle management and control system based on image recognition,
by applying the human-vehicle control method of the electric vehicle charging station based on image recognition,
the parking lot comprises a camera, a parking spot lock, a main controller and a loudspeaker;
the camera is used for collecting images of vehicles and human faces;
the parking spot lock is arranged on the charging position and used for controlling the driving authority of the vehicle;
the loudspeaker is used for prompting a charging vehicle owner;
the main controller is used for recognizing human faces and license plates based on the collected images and videos and controlling parking spot locks and speakers based on a human-vehicle management and control method of the electric vehicle charging station.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A man-car management and control method for an electric vehicle charging station based on image recognition comprises an operation method and an overhaul method;
it is characterized in that the preparation method is characterized in that,
the operation method comprises the following steps:
allowing new energy vehicles and white list vehicles to drive into a charging parking space for charging, and carrying out voice broadcast prompting without charging after driving or without driving away after charging; for the vehicles with the mobile phone and the license plate numbers registered on the operation platform, simultaneously sending a short message to inform the driver of driving away;
carrying out image recognition on the condition that the charging gun is not inserted back into the charging pile after charging is finished, carrying out voice broadcast prompting after the charging gun is inserted back into the charging pile, and simultaneously sending a short message to prompt gun hanging for the vehicle with a mobile phone and a license plate number registered on an operation platform;
under the condition of equipment failure, if the vehicle owner is identified to drive in or prepare to drive in the charging station, the equipment failure is prompted through voice, and the vehicle owner is guided to go to other charging stations or other charging stations;
the overhaul method comprises the following steps:
dividing a fault maintenance state and a routine maintenance state, under the two conditions, distributing maintenance tasks to a certain maintenance worker through an operation platform, and automatically switching to the maintenance state after the maintenance worker arrives at a specific charging station and the camera recognizes the face characteristics of the maintenance worker;
in an overhaul state, when a door of a communication cabinet or a faulty charging pile is opened, an alarm on a platform cannot be triggered, and when personnel enter a warning area, the alarm cannot be triggered; the charging potential in the health state in the same charging station still keeps the operation state.
2. The image recognition-based human-vehicle management and control method for the electric vehicle charging station is characterized in that,
the method for determining the new energy vehicle and the white list vehicle to drive into the charging parking space comprises the following steps:
whether a vehicle approaches is determined through the image acquisition of the camera and the input of the parking space lock sensor.
3. The image recognition-based human-vehicle management and control method for the electric vehicle charging station according to claim 2,
the following data information is obtained through camera image acquisition: whether a cabinet door is opened, whether a charging gun is inserted back or not, whether charging is performed, whether a charging vehicle identity is charged, and whether a transportation and inspection person is present or not;
and (4) obtaining results of an operation state and a maintenance state by combining whether a vehicle is stopped at the parking space sensed by the parking space lock and performing algorithm analysis, wherein the operation state further comprises an operation normal state and an operation alarm state.
4. The image recognition-based human-vehicle management and control method for the electric vehicle charging station according to claim 3,
for the operation alarm state, the man-vehicle management and control system can give on-site voice prompt and mobile phone short message prompt according to the generated time, duration and vehicle owner information corresponding to the vehicle identity.
5. The image recognition-based human-vehicle management and control method for the electric vehicle charging station according to any one of claims 1 to 4,
the image recognition method comprises image processing and image feature extraction;
the image processing specifically includes: image sampling, image enhancement, image restoration, image coding and compression and image segmentation;
the image feature extraction specifically comprises:
and extracting color features, texture features, shape features and local feature points of the graph.
6. The image recognition-based human-vehicle management and control method for the electric vehicle charging station according to claim 5,
the image identification method based on the color features is used for color images, and classification identification is carried out through the simplicity of a color histogram and insensitivity of the color histogram along with the size and rotation transformation of the images;
the image recognition method based on the texture features is completed by analyzing the features with structural rules in the image or counting the distribution information of the color intensity in the image;
the shape characteristics include perimeter, area, circularity, eccentricity.
7. The image recognition-based human-vehicle management and control method for the electric vehicle charging station according to claim 6,
the classification and identification method is a naive Bayes classification method, and the working process is as follows:
using one n-dimensional feature vector X ═ X for each data sample1，x2，...xnRepresents, describing n metrics for n attribute a1, a2, … An samples, respectively;
there are m classes C1，C2，…Cm(ii) a Given an unknown data sample X, the naive Bayes classification method predicts that X belongs to the class with the highest posterior probability;
that is, naive Bayes classification assigns unknown samples to class CiAnd if and only if
P(Ci/X)＞P(Cj/X)，1≤j≤m，j≠i
Maximizing probability P (C)iX); its probability P (C)i/X) largest class CiReferred to as maximum a posteriori assumption.
8. The image recognition-based human-vehicle management and control method for the electric vehicle charging station according to claim 7,
the naive Bayes classification method has the calculation formula as follows:
for P (X/C)i)P(Ci) Maximization; while P (C)1)＝P(C2)＝…P(Cm)；
And accordingly only for P (X/C)i) Maximization; otherwise, maximize P (X/C)i)P(Ci)；
Prior probability of class P (C)i)＝siS, calculating where siIs of the class CiAnd s is the total number of training samples;
giving class labels of samples, wherein attribute values are mutually condition-independent; in this way it is possible to obtain,
9. the image recognition-based human-vehicle management and control method for the electric vehicle charging station according to claim 8,
probability P (X)1/Ci)，P(X2/Ci)，...P(Xn/Ci) ,.. estimating from the training samples, wherein
(a) If the attribute Ak is a classification attribute, P (X)k/Ci)＝sik/si，
Wherein s isikIs to have a value X on the attribute AkkClass C ofiNumber of samples of (1), and siIs CiThe number of training samples in (1);
(b) if the attribute Ak is a continuous-value attribute, it is generally assumed that the attribute follows a gaussian distribution, and, thus,
wherein, given class CiThe value of the training sample property Ak,
is attribute AkAs a function of the gaussian density of (a), as a mean and standard deviation, respectively;
classifying the unknown sample X, and classifying each class CiCalculating
Sample X is assigned to class CiAnd if and only if
I.e. X is assigned to it
Largest class Ci。
10. An electric vehicle charging station man-vehicle management and control system based on image recognition is characterized in that,
applying the image recognition-based human-vehicle management and control method for the electric vehicle charging station according to any one of claims 1 to 9,
the parking lot comprises a camera, a parking spot lock, a main controller and a loudspeaker;
the camera is used for collecting images of vehicles and human faces;
the parking spot lock is arranged on the charging position and used for controlling the driving authority of the vehicle;
the loudspeaker is used for prompting a charging vehicle owner;
the main controller is used for recognizing human faces and license plates based on the collected images and videos and controlling parking spot locks and speakers based on a human-vehicle management and control method of the electric vehicle charging station.
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