CN113682183A - Intelligent electric vehicle charging system based on SCADA system - Google Patents
Intelligent electric vehicle charging system based on SCADA system Download PDFInfo
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- CN113682183A CN113682183A CN202111153646.XA CN202111153646A CN113682183A CN 113682183 A CN113682183 A CN 113682183A CN 202111153646 A CN202111153646 A CN 202111153646A CN 113682183 A CN113682183 A CN 113682183A
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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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- 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
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
- Secondary Cells (AREA)
Abstract
The invention discloses an intelligent electric vehicle charging system based on an SCADA system, which comprises: a central monitoring device; the central monitoring device is connected with an output end monitoring system through a communication module; the output monitoring system is provided with: the device comprises a charger, a temperature sensor, a current sensor, a microseismic sensor and a voltage sensor; the output end monitoring system uploads multisource heterogeneous charging information such as temperature, current, voltage and vibration in the charging process measured by the device to the central monitoring device in real time, static and dynamic automobile battery information is collected through the central monitoring device, the charging state of an automobile battery is monitored in real time, a multisource heterogeneous charging early warning model in the charging process is built, early prediction and early warning of charging operation faults are achieved, the charging operation process is controlled in time through the central monitoring device, and the battery is prevented from being broken down in the charging process.
Description
Technical Field
The utility model belongs to the technical field of electric automobile charges, concretely relates to intelligence electric automobile charging system based on SCADA system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The use of the conventional petrochemical fuel causes great pollution to the atmosphere, and meanwhile, as an unrecoverable energy source, the storage amount of the petrochemical fuel is less and less; therefore, research and development and production of electric vehicles are actively dedicated by many countries and scientific research institutions, and development of electric vehicles becomes a necessary trend for energy conservation, emission reduction and global environment improvement.
However, the disadvantages of the electric vehicle, such as poor cruising ability and inconvenient charging, also become obstacles for limiting the wide-range use thereof.
Therefore, how to solve the charging problem of the electric automobile, the power battery can be maintained, and the user can conveniently operate and use the electric automobile through the humanized man-machine interaction interface and the perfect communication capability, so that the electric automobile power-assisting device becomes the key for popularizing the power-assisted electric automobile.
However, in recent years, as the number of users of electric vehicles increases, the charging and ignition events of electric vehicles also occur frequently; the method also limits the pace of investment and construction of charging piles and charging stations in vast vehicle enterprises and charging companies, but the existing electric vehicle charging process is not provided with a prediction early warning device and a corresponding prediction early warning method, so that dangerous situations are often found after the battery smokes and fires, obviously, economic losses are caused at this moment, and great risks are caused to the surrounding environment and people.
Disclosure of Invention
Aiming at the defects in the prior art, one or more embodiments of the disclosure provide an intelligent electric vehicle charging system based on an SCADA system, static and dynamic multisource heterogeneous charging information is collected and the charging state of a vehicle battery is monitored in real time through a data collection and process monitoring function based on the SCADA system, so that a multisource heterogeneous charging information early warning model of the charging process is established, the early prediction and early warning of charging operation faults are realized, the charging operation process is controlled in time through a central monitoring device, and the battery is prevented from being broken down in the charging process.
According to an aspect of one or more embodiments of the present disclosure, there is provided an intelligent electric vehicle charging system based on a SCADA system, including: a central monitoring device; the central monitoring device is connected with an output end monitoring system through a communication module.
The output monitoring system is provided with: the device comprises a charger, a temperature sensor, a current sensor, a microseismic sensor and a voltage sensor; the output end monitoring system uploads multi-source heterogeneous charging information such as temperature, current, voltage, vibration and the like in the charging process measured by the device to the central monitoring device in real time.
Furthermore, the central monitoring device is also connected with an input end monitoring system through a communication module, and the input end monitoring system is a monitoring system which is arranged on the electric automobile and is used for maintaining and maintaining the automobile battery.
Furthermore, the total battery capacity, the current battery electric quantity, the current battery temperature, the current charging current value and the current charging voltage value information in the charging process, which are measured by the input end monitoring system, are uploaded to the central monitoring device in real time.
Furthermore, the central monitoring device is connected with an external monitoring device through a communication module.
Furthermore, based on the multi-source heterogeneous charging information parameters acquired by the central monitoring device, an association criterion algorithm is adopted to mine an internal logic symbiotic relation between the multi-source heterogeneous charging information parameters and battery faults, a multi-source heterogeneous charging information early warning model in the charging process is constructed, and each early warning information parameter is quantized.
Further, a support probability of the multi-source heterogeneous early warning information parameters on charging faults is obtained by using the multi-source heterogeneous charging information early warning model; and sends early warning signals and control instructions through the central monitoring device.
According to one aspect of one or more embodiments of the disclosure, a method for constructing a multi-source heterogeneous charging process early warning information early warning model is provided, and a multi-source heterogeneous early warning information parameter is acquired by using the intelligent electric vehicle charging system based on the SCADA system.
Furthermore, the multi-source heterogeneous early warning information adopts four algorithms of dispersion standardization, an arc tangent function, a logarithmic function method and a zero-mean method to process the charging fault early warning information parameters, eliminates interference information and retains effective information.
Furthermore, according to the acquired charging fault early warning information, single parameter attribute measurement is carried out, the acquired multi-source charging fault early warning information is combined, a log audit algorithm is adopted to respectively calculate the supporting probability of the single parameter to the charging fault, and the value range of the supporting probability is [ 0, 1 ].
Furthermore, a charging fault multi-source heterogeneous early warning information deep fusion method is established according to the support probability of the single parameter to the occurrence of the charging fault, all early warning information parameters are fused, the support probability of the multi-source heterogeneous early warning information parameters to the occurrence of the charging fault is calculated, and the value range of the support probability is [ 0, 1 ].
Advantageous effects
The intelligent electric vehicle charging system based on the SCADA system collects static and dynamic multisource charging devices and vehicle battery information and monitors the charging state of a vehicle battery in real time through a data collection and process monitoring function based on the SCADA system, so that a multisource heterogeneous charging early warning model of the charging process is established, early prediction and early warning of charging operation faults are realized, a charging operation process is controlled in time through a central monitoring device, and the battery is prevented from being broken down in the charging process.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
Fig. 1 is a diagram of a multi-source heterogeneous charging information charging early warning model of an intelligent electric vehicle charging system based on a SCADA system according to one or more embodiments of the present disclosure.
The specific implementation mode is as follows:
technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in one or more embodiments of the present disclosure, and it is to be understood that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments, which can be derived by one of ordinary skill in the art from one or more embodiments of the disclosure without making any creative effort, shall fall within the scope of protection of the disclosure.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It is also noted that the flowchart or block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the logical function specified in the respective embodiment. In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Without conflict, the embodiments and features of the embodiments in the present disclosure may be combined with each other, and the present disclosure will be further described with reference to the drawings and the embodiments.
Example one
As shown in fig. 1, an intelligent electric vehicle charging system based on SCADA system includes: the monitoring system comprises a central monitoring device, a communication module, an output end monitoring system, an input end monitoring system, an external monitoring device and a fault early warning device.
The central monitoring device, as the core of the charging system, includes: and the computer and the software are connected with the field external equipment, the module and the system and are used for collecting process data and sending a control command to the field connected equipment, the module and the system.
The output end monitoring system comprises: charger, temperature sensor, current sensor, microseism sensor, voltage sensor.
The micro-vibration sensor is arranged on the charger and is attached to the contact surface of the battery through the connection between the charger and the battery charging port, and senses and receives micro-vibration signals of the charger through the contact surface of the micro-vibration sensor and the charger and simultaneously senses and receives the micro-vibration signals of the battery through the contact surface of the micro-vibration sensor and the battery charging port.
The input end monitoring system is based on a monitoring system which is arranged by each vehicle manufacturer in different types of vehicles and is used for maintaining and maintaining the automobile battery.
The input end monitoring system generally comprises the following monitoring information: the total capacity of the battery, the current electric quantity of the battery, the current temperature of the battery, the current charging current value, the current charging voltage value and other information.
The external monitoring device includes: video monitoring devices, infrared temperature detectors and other external monitoring devices.
The fault early warning device adopts an acousto-optic warning device.
The communication module includes: the device comprises an output end communication unit, an input end communication unit, an external monitoring communication unit and a fault early warning communication unit.
Data information of output end monitoring system, input end monitoring system, external monitoring device based on SCADA system gathers through each communication unit, wherein, include: the method comprises the steps of collecting multiple structural data such as structured, semi-structured, unstructured, dynamic values and static values, mining an internal logic symbiotic relation between multi-source heterogeneous charging information parameters and battery faults by adopting an association criterion algorithm, constructing a multi-source heterogeneous charging information early warning model in the charging process, and further quantifying each early warning information parameter.
S1: the method comprises the steps of utilizing a central monitoring device to carry out four algorithms of deviation standardization, an arc tangent function, a logarithmic function method and a zero-mean value method, processing charging fault early warning information parameters through the four algorithms, eliminating interference information and reserving effective information.
S2: and according to the acquired charging fault early warning information, performing single parameter attribute measurement, combining the acquired multi-source charging fault early warning information, and respectively calculating the support probability of the single parameter to the occurrence of the charging fault by adopting a log audit algorithm, wherein the value range of the support probability is (0, 1).
S3: according to the support probability of the single parameter to the occurrence of the charging fault, a charging fault multi-source heterogeneous early warning information deep fusion method is established, all early warning information parameters are fused, the support probability of the multi-source heterogeneous early warning information parameters to the occurrence of the charging fault is calculated, and the value range of the support probability is [ 0, 1 ].
S4: and calculating to obtain the charging fault occurrence probability situation based on real-time data by taking the delta T time period as a sliding window, and predicting the occurrence probability of the charging fault in the next delta T time period by adopting a GM (1,1), ARMA or Ho l T-Wi nters model. Meanwhile, multisource heterogeneous information data in the charging process are collected through each communication unit of the SCADA system, and the occurrence probability of the charging fault in the charging process is continuously corrected, so that the accuracy of the charging system for predicting the occurrence probability of the charging fault is improved.
According to the support probability of the multi-source heterogeneous early warning information parameters obtained by the model algorithm on the occurrence of the charging fault, the central monitoring device sends a primary early warning signal to the fault early warning device and sends early warning information to workers and a vehicle owner through the SCADA system, wherein the support probability is between 0.65 and 0.75; between (0.75-0.85), the central monitoring device sends a secondary early warning signal to the fault early warning device, and sends early warning information to workers and car owners through the SCADA system; and (0.85-1), the central monitoring device sends a three-stage early warning signal to the fault early warning device and controls the charger to immediately stop charging operation.
It should be noted that although several modules or sub-modules of the device are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the modules described above may be embodied in one module in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module described above may be further divided into embodiments by a plurality of modules.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. 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 features disclosed herein.
Claims (10)
1. The utility model provides an intelligence electric automobile charging system based on SCADA system which characterized in that includes: a central monitoring device; the central monitoring device is connected with an output end monitoring system through a communication module;
the output monitoring system is provided with: the device comprises a charger, a temperature sensor, a current sensor, a microseismic sensor and a voltage sensor; the output end monitoring system uploads multi-source heterogeneous charging information such as temperature, current, voltage, vibration and the like in the charging process measured by the device to the central monitoring device in real time.
2. An intelligent electric vehicle charging system based on SCADA system as in claim 1, wherein the central monitoring device is further connected with an input end monitoring system through a communication module, and the input end monitoring system is a monitoring system installed on the electric vehicle and related to the maintenance of the vehicle battery.
3. The intelligent electric vehicle charging system based on the SCADA system as claimed in claim 2, wherein the information of the total battery capacity, the current battery temperature, the current charging current value and the current charging voltage value measured by the input end monitoring system during the charging process is uploaded to the central monitoring device in real time.
4. An intelligent SCADA system based charging system of an electric vehicle according to claim 3, wherein the central monitoring device is connected to an external monitoring device through a communication module.
5. The intelligent electric vehicle charging system based on the SCADA system as recited in claim 4, wherein based on the multi-source heterogeneous charging information parameters collected by the central monitoring device, an association criterion algorithm is adopted to mine an internal logic symbiotic relationship between the multi-source heterogeneous charging information parameters and a battery fault, a multi-source heterogeneous charging information early warning model of the charging process is constructed, and each early warning information parameter is further quantized.
6. The intelligent electric vehicle charging system based on the SCADA system as in claim 5, wherein a supporting probability of a multi-source heterogeneous early warning information parameter to the occurrence of a charging fault is obtained by using a multi-source heterogeneous charging information early warning model; and sends early warning signals and control instructions through the central monitoring device.
7. A construction method of an early warning information early warning model in a multi-source heterogeneous charging process is characterized in that a multi-source heterogeneous early warning information parameter is collected by the intelligent electric vehicle charging system based on the SCADA system according to any one of claims 1 to 6.
8. The method for constructing the early warning information model in the multi-source heterogeneous charging process according to claim 7, wherein the multi-source heterogeneous early warning information adopts four algorithms of dispersion standardization, an arc tangent function, a logarithmic function method and a zero-mean method to process the early warning information parameters of the charging fault, eliminates interference information and retains effective information.
9. The method for constructing the early warning model of the early warning information in the multi-source heterogeneous charging process according to claim 8, wherein single parameter attribute measurement is performed according to the acquired early warning information of the charging fault, and the support probability of the single parameter to the occurrence of the charging fault is respectively calculated by combining the acquired early warning information of the multi-source charging fault by adopting a log audit algorithm, wherein the value range of the support probability is [ 0, 1 ].
10. The method for constructing the early warning information early warning model in the multi-source heterogeneous charging process according to claim 9, wherein a charging fault multi-source heterogeneous early warning information deep fusion method is constructed according to the support probability of a single parameter to the occurrence of a charging fault, all early warning information parameters are fused, and the support probability of the multi-source heterogeneous early warning information parameters to the occurrence of the charging fault is calculated, wherein the value range of the support probability is [ 0, 1 ].
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