CN117416239B - Monitoring method and system for alternating-current charging pile of electric automobile and electronic equipment - Google Patents
Monitoring method and system for alternating-current charging pile of electric automobile and electronic equipment Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
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Abstract
The invention discloses a monitoring method, a system and electronic equipment of an electric automobile alternating-current charging pile, belonging to the technical field of charging pile monitoring, wherein the method comprises the following steps: selecting an electric automobile alternating-current charging pile in a normal state as a sample, and acquiring physical state detection data, electric performance detection data and network safety detection data of each alternating-current charging pile; constructing a composite superposition neural network model, and training and optimizing the composite superposition neural network model through physical state detection data, electrical performance detection data and network safety detection data; and acquiring physical state detection data, electric performance detection data and network safety detection data of the electric vehicle alternating-current charging pile to be tested, inputting the physical state detection data, the electric performance detection data and the network safety detection data into the composite superposition neural network model after training and optimization, and outputting a monitoring result of the electric vehicle alternating-current charging pile to be tested. The invention can improve the reliability and the safety of the electric automobile alternating-current charging pile.
Description
Technical Field
The invention relates to the technical field of charging pile monitoring, in particular to a monitoring method, a monitoring system and electronic equipment for an alternating current charging pile of an electric automobile.
Background
With the increase of safety accidents of electric vehicles, the safety problem of the electric vehicles is more and more concerned, especially the safety problem generated in the charging process of the electric vehicles, the alternating-current charging pile of the electric vehicles is used as an important component of the charging network of the electric vehicles, and the running state of the alternating-current charging pile directly relates to the charging efficiency and safety of the electric vehicles. Therefore, it is important to study the monitoring of the ac charging pile.
However, the existing monitoring technology still has certain defects, such as insufficient overall monitoring parameters, and the reliability and accuracy of the monitoring equipment to be improved.
Therefore, how to provide a method, a system and an electronic device for monitoring an ac charging pile of an electric vehicle is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the present invention provides a method, a system and an electronic device for monitoring an ac charging pile of an electric vehicle, which are used for solving the technical problems in the prior art.
In order to achieve the above object, the present invention provides the following technical solutions:
a monitoring method of an electric vehicle alternating-current charging pile comprises the following steps:
selecting an electric automobile alternating-current charging pile in a normal state as a sample, and acquiring physical state detection data, electric performance detection data and network safety detection data of each alternating-current charging pile;
constructing a composite superposition neural network model, and training and optimizing the composite superposition neural network model through physical state detection data, electrical performance detection data and network safety detection data;
and acquiring physical state detection data, electric performance detection data and network safety detection data of the electric vehicle alternating-current charging pile to be tested, inputting the physical state detection data, the electric performance detection data and the network safety detection data into the composite superposition neural network model after training and optimization, and outputting a monitoring result of the electric vehicle alternating-current charging pile to be tested.
Preferably, the physical state detection data includes: the method comprises the steps of generating first sample information by cable temperature, charging pile shell temperature and fan running state and generating physical state detection data of the alternating-current charging pile;
the electrical performance test data includes: voltage, current, power, and generating second sample information from the electrical performance test data;
the network security detection data includes: lightning protection, surge protection, data encryption, and generating third sample information from the network security detection data.
Preferably, the constructed composite stacked neural network model structure comprises:
and the three BP neural networks are respectively a first discrimination layer, a second discrimination layer and a third discrimination layer of the composite superposition neural network.
Preferably, the first discrimination layer, the second discrimination layer and the third discrimination layer respectively include: an input layer, a plurality of hidden layers, and an output layer;
the input layer, the hidden layer and the output layer are not mutually connected, each layer is not connected by a interlayer, and the layers are fully connected.
Further, the number of the hidden layers is n, wherein:
Wherein p is the number of hidden layer neuron nodes, m is the number of input layer neuron nodes, n is the number of output layer neuron nodes, and a is a constant between [1-10 ].
Further, the output monitoring result of the electric vehicle alternating-current charging pile to be tested is a vector resultWherein:
In the formula, x= {1,2,3}, which is the number of neurons in the output layer, respectively represents a physical state output result, an electrical performance output result and a network security output result.
Preferably, the constructing the composite stacked neural network model, and training and optimizing the composite stacked neural network model through the physical state detection data, the electrical performance detection data and the network security detection data includes: setting a learning rate, calculating the iteration times of the weight coefficient, training the composite superposition neural network model, and optimizing the convolutional neural network through a gradient descent correlation algorithm to obtain optimal prediction accuracy.
Further, the iterative calculation formula of the weight coefficient is as follows:
Wnode,o(i+1)＝Wnode,o(i)+γ1δnode(i)Wnode,j(i)；
Wherein, gamma 1 is a convergence coefficient, delta node (i) is a partial derivative of each layer of neurons, and W node,j (i) is an output value of a hidden layer node at the moment i;
Wherein:
wherein delta node is the partial derivative of the output layer neuron node, Is/>Derivative of/(I)Is/>Is a derivative of (a).
A monitoring system for an electric vehicle ac charging stake, comprising:
The acquisition module is used for: selecting an electric automobile alternating-current charging pile in a normal state as a sample, and acquiring physical state detection data, electric performance detection data and network safety detection data of each alternating-current charging pile;
The construction module comprises: constructing a composite superposition neural network model, and training and optimizing the composite superposition neural network model through physical state detection data, electrical performance detection data and network safety detection data;
And a monitoring module: and acquiring physical state detection data, electric performance detection data and network safety detection data of the electric vehicle alternating-current charging pile to be tested, inputting the physical state detection data, the electric performance detection data and the network safety detection data into the composite superposition neural network model after training and optimization, and outputting a monitoring result of the electric vehicle alternating-current charging pile to be tested.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of a method for monitoring an electric vehicle alternating current charging pile when executing the computer program.
Compared with the prior art, the invention discloses a monitoring method, a system and electronic equipment for the alternating-current charging pile of the electric automobile, wherein the alternating-current charging pile of the electric automobile in a normal state is taken as a sample, physical state detection data, electrical performance detection data and network safety detection data of the alternating-current charging pile are obtained, and three different sample data are respectively generated. Meanwhile, a composite superposition neural network model superposed by the BP neural network is established, the generated three different sample data are respectively input into the input layers of the corresponding BP neural network model, and whether the state of the electric vehicle alternating-current charging pile is normal or not is comprehensively judged. The method has the specific beneficial effects that:
1) Providing more comprehensive monitoring data to monitor more key parameters;
2) The reliability and the precision of monitoring are improved through the constructed superposition neural network model, and the accuracy and the reliability of monitoring data are improved;
3) Whether the state of the alternating-current charging pile of the electric automobile is normal or not is automatically and comprehensively judged based on the composite superimposed neural network, so that automatic intelligent monitoring and management of the charging pile are realized;
4) Network safety monitoring data in the sample data is beneficial to strengthening the network safety monitoring of the charging pile so as to ensure the safety and stability of the charging network.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the method of the present invention;
fig. 2 is a schematic diagram of a system structure according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention discloses a monitoring method of an electric vehicle alternating-current charging pile, which comprises the following steps:
selecting an electric automobile alternating-current charging pile in a normal state as a sample, and acquiring physical state detection data, electric performance detection data and network safety detection data of each alternating-current charging pile;
constructing a composite superposition neural network model, and training and optimizing the composite superposition neural network model through physical state detection data, electrical performance detection data and network safety detection data;
and acquiring physical state detection data, electric performance detection data and network safety detection data of the electric vehicle alternating-current charging pile to be tested, inputting the physical state detection data, the electric performance detection data and the network safety detection data into the composite superposition neural network model after training and optimization, and outputting a monitoring result of the electric vehicle alternating-current charging pile to be tested.
In one embodiment, the physical state detection data of the ac charging stake includes: the method comprises the steps of generating first sample information by cable temperature, charging pile shell temperature and fan running state and detecting physical state of an alternating-current charging pile;
The electrical performance detection data of the ac charging pile includes: voltage, current and power, and generating second sample information from the electrical performance detection data;
the network safety detection data of the alternating current charging pile comprises: lightning protection, surge protection, data encryption, and generating third sample information from the network security detection data.
In a specific embodiment, the constructed composite stacked neural network model structure comprises:
the three BP neural networks are respectively a first discrimination layer, a second discrimination layer and a third discrimination layer of the composite superimposed neural network.
In one embodiment, the first discrimination layer, the second discrimination layer and the third discrimination layer respectively include: an input layer, a plurality of hidden layers, and an output layer;
The input layer, the hidden layer and the output layer are not mutually connected, each layer is not connected by a interlayer, and the layers are fully connected.
The hidden layers are n, wherein:
Wherein p is the number of hidden layer neuron nodes, m is the number of input layer neuron nodes, n is the number of output layer neuron nodes, and a is a constant between [1-10 ].
In a specific embodiment, a monitoring result of the electric vehicle ac charging pile to be tested is output as a vector resultWherein:
In the formula, x= {1,2,3}, which is the number of neurons in the output layer, respectively represents a physical state output result, an electrical performance output result and a network security output result.
Specifically, three different sample information are respectively input into the input layers of three BP neural networks, and three different results are output, for example, whenWhen 1, it indicates that the physical state is normal, and when/>When 0, the physical state is abnormal; when/>When 1, it indicates that the electrical performance is normal, and when/>When 0, the electrical performance abnormality is indicated; when/>When 1, network security is indicated, when/>When 0, the network abnormality is indicated;
More specifically, the three different results are logically and calculated, and only when all conditions are true (1), the whole expression is true (1), namely, the state of the electric vehicle alternating-current charging pile to be tested is normal, if any one of the conditions is not true, the whole expression is not true (0), and the state of the electric vehicle alternating-current charging pile to be tested is abnormal, at this time, early warning is needed to prompt related staff to overhaul on site.
In a specific embodiment, constructing a composite overlay neural network model, and training and optimizing the composite overlay neural network model through physical state detection data, electrical performance detection data and network security detection data, including: setting a learning rate, calculating the iteration times of the weight coefficient, training the composite superimposed neural network model, and optimizing the convolutional neural network through a gradient descent correlation algorithm to obtain the optimal prediction accuracy.
In a specific embodiment, the iterative calculation formula of the weight coefficient is:
Wnode,o(i+1)＝Wnode,o(i)+γ1δnode(i)Wnode,j(i)；
Wherein, gamma 1 is a convergence coefficient, delta node (i) is a partial derivative of each layer of neurons, and W node,j (i) is an output value of a hidden layer node at the moment i;
Wherein:
wherein delta node is the partial derivative of the output layer neuron node, Is/>Derivative of/(I)Is/>Is a derivative of (a).
On the other hand, as shown in fig. 2, an embodiment of the present invention provides a monitoring system for an ac charging pile of an electric vehicle, including:
The acquisition module is used for: selecting an electric automobile alternating-current charging pile in a normal state as a sample, and acquiring physical state detection data, electric performance detection data and network safety detection data of each alternating-current charging pile;
The construction module comprises: constructing a composite superposition neural network model, and training and optimizing the composite superposition neural network model through physical state detection data, electrical performance detection data and network safety detection data;
And a monitoring module: and acquiring physical state detection data, electric performance detection data and network safety detection data of the electric vehicle alternating-current charging pile to be tested, inputting the physical state detection data, the electric performance detection data and the network safety detection data into the composite superposition neural network model after training and optimization, and outputting a monitoring result of the electric vehicle alternating-current charging pile to be tested.
In one aspect, the embodiment of the invention further provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the monitoring method of the electric vehicle alternating-current charging pile.
For the system device disclosed in the embodiment, since the system device corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
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 (7)
1. The monitoring method of the alternating-current charging pile of the electric automobile is characterized by comprising the following steps of:
Selecting an electric automobile alternating-current charging pile in a normal state as a sample, and acquiring physical state detection data, electric performance detection data and network safety detection data of each alternating-current charging pile, wherein the physical state detection data comprise: the method comprises the steps of generating first sample information by cable temperature, charging pile shell temperature and fan running state and generating physical state detection data of the alternating-current charging pile;
the electrical performance test data includes: voltage, current, power, and generating second sample information from the electrical performance test data;
The network security detection data includes: lightning protection, surge protection, data encryption, and generating third sample information from the network security detection data;
Constructing a composite superposition neural network model, and training and optimizing the composite superposition neural network model through physical state detection data, electrical performance detection data and network safety detection data, comprising: setting a learning rate and calculating the iteration times of the weight coefficient, training the composite superimposed neural network model, and optimizing the convolutional neural network through a gradient descent correlation algorithm to obtain optimal prediction accuracy, wherein the iteration calculation formula of the weight coefficient is as follows:
Wnode,o(i+1)＝Wnode,o(i)+γ1δnode(i)Wnode,j(i)；
Wherein, gamma 1 is a convergence coefficient, delta node (i) is a partial derivative of each layer of neurons, and W node,j (i) is an output value of a hidden layer node at the moment i;
Wherein:
wherein delta node is the partial derivative of the output layer neuron node, Is/>Derivative of/(I)Is/>Is a derivative of (2);
and acquiring physical state detection data, electric performance detection data and network safety detection data of the electric vehicle alternating-current charging pile to be tested, inputting the physical state detection data, the electric performance detection data and the network safety detection data into the composite superposition neural network model after training and optimization, and outputting a monitoring result of the electric vehicle alternating-current charging pile to be tested.
2. The method for monitoring the alternating-current charging pile of the electric automobile according to claim 1, wherein the constructed composite superimposed neural network model structure comprises:
and the three BP neural networks are respectively a first discrimination layer, a second discrimination layer and a third discrimination layer of the composite superposition neural network.
3. The method for monitoring an ac charging pile for an electric vehicle according to claim 2, wherein the first, second and third discrimination layers respectively include: an input layer, a plurality of hidden layers, and an output layer;
the input layer, the hidden layer and the output layer are not mutually connected, each layer is not connected by a interlayer, and the layers are fully connected.
4. The method for monitoring an electric vehicle ac charging pile according to claim 3, wherein the number of hidden layers is n, wherein:
Wherein p is the number of hidden layer neuron nodes, m is the number of input layer neuron nodes, n is the number of output layer neuron nodes, and a is a constant between [1-10 ].
5. The method for monitoring the electric vehicle alternating-current charging pile according to claim 1, wherein the output monitoring result of the electric vehicle alternating-current charging pile to be tested is a vector resultWherein:
In the formula, x= {1,2,3}, which is the number of neurons in the output layer, respectively represents a physical state output result, an electrical performance output result and a network security output result.
6. A monitoring system for an electric vehicle ac charging pile using the monitoring method for an electric vehicle ac charging pile according to any one of claims 1 to 5, characterized by comprising:
The acquisition module is used for: selecting an electric automobile alternating-current charging pile in a normal state as a sample, and acquiring physical state detection data, electric performance detection data and network safety detection data of each alternating-current charging pile;
The construction module comprises: constructing a composite superposition neural network model, and training and optimizing the composite superposition neural network model through physical state detection data, electrical performance detection data and network safety detection data;
And a monitoring module: and acquiring physical state detection data, electric performance detection data and network safety detection data of the electric vehicle alternating-current charging pile to be tested, inputting the physical state detection data, the electric performance detection data and the network safety detection data into the composite superposition neural network model after training and optimization, and outputting a monitoring result of the electric vehicle alternating-current charging pile to be tested.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method for monitoring an electric car ac charging stake as claimed in any one of claims 1 to 5 when executing the computer program.
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| CN113283491A (en) * | 2021-05-19 | 2021-08-20 | 青岛科技大学 | Fault diagnosis method of electric vehicle alternating current charging pile based on optimized deep confidence network |
| CN113902183A (en) * | 2021-09-28 | 2022-01-07 | 浙江大学 | BERT-based non-invasive distribution room charging pile state monitoring and electricity price adjusting method |
| CN114407713A (en) * | 2021-12-30 | 2022-04-29 | 广东劲天科技有限公司 | Charging pile management system, method and device based on big data and storage medium |
| CN114648171A (en) * | 2022-04-08 | 2022-06-21 | 国网湖南省电力有限公司 | Electric vehicle charging pile load prediction method, system and storage medium |
| CN115102775A (en) * | 2022-07-04 | 2022-09-23 | 蔚来汽车科技(安徽)有限公司 | Charging pile risk detection method, device and system |
| CN115782634A (en) * | 2022-12-12 | 2023-03-14 | 广东电网有限责任公司佛山供电局 | Energy supply device of electric automobile |
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