CN-122008941-A - Charging pile remote safety management system based on Internet of things

CN122008941ACN 122008941 ACN122008941 ACN 122008941ACN-122008941-A

Abstract

The invention discloses a charging pile remote safety management system based on the Internet of things, which relates to the technical field of data monitoring and control and comprises an Internet of things data acquisition module, a live analysis module, a charging scheduling module, a load analysis module and a remote control module; according to the invention, the total number of vehicles to be charged is obtained by deploying the sensor array of the internet of things, the charging prediction requirement value of the vehicles can be generated by combining the actual port charging efficiency of the vehicles, the charging dispatching optimization model of the power grid constraint is constructed, the total number of vehicles to be charged, the total number of charging piles and the total capacity of the power grid are comprehensively considered, the load pressure analysis of the power grid can be comprehensively carried out, the optimal charging threshold value of the charging piles is generated, the total active power of the whole charging station cluster is monitored and calculated in real time, the total active power is compared with the safety capacity threshold value provided by the power grid side, the optimal charging threshold value of the charging piles and the load early warning signals of different grades are received, and the remote and accurate control of the charging piles is realized.

Inventors

YANG HUI
LUO DONGDONG
QIU SHIGUANG
CHENG XIN
LIU CHANGMING
LIN KAI
DU CHANGZHENG
LIANG XIAOTING
LIU LIANGKUN
Hu Longpeng
DUAN JINGPING

Assignees

佛山康晋云充技术有限公司

Dates

Publication Date: 20260512
Application Date: 20260127

Claims (8)

1. The charging pile remote safety management system based on the Internet of things is characterized by comprising an Internet of things data acquisition module, a live analysis module, a charging scheduling module, a load analysis module and a remote control module; The system comprises an Internet of things data acquisition module, a charging pile power grid load data acquisition module and a charging pile parameter acquisition module, wherein the Internet of things data acquisition module acquires vehicle characteristic data, the charging pile power grid load data and the charging pile parameter in real time through an Internet of things sensor array deployed on the charging pile, acquires vehicle data based on camera equipment, and recognizes the vehicle charging requirement to obtain the total number of vehicles to be charged; The live analysis module is used for carrying out charge demand analysis based on the total number of vehicles to be charged and the use number of the charging piles in a normal state, carrying out charge analysis by combining the actual port charging efficiency of the vehicles, generating a vehicle charge prediction demand value, and carrying out charge grade division on the charge type according to the load of vehicle charging to obtain high, medium and low grade differentiated charge permission labels; the charging dispatching module is used for constructing a power grid constraint charging dispatching optimization model, carrying out charging power grid load pressure analysis by combining the total number of the charging piles and the total capacity of the power grid based on the total number of vehicles to be charged, calculating theoretical charging efficiency by combining the charging port standard, and generating an optimal charging threshold of the charging piles; The load analysis module monitors and calculates the total active power of the whole charging station cluster in real time, compares the total active power with a safety capacity threshold value provided by a power grid side, and generates load early warning signals of different grades when the monitored real-time total power approaches the safety threshold value; The remote control module is used for receiving the optimal charging threshold value of the charging pile and the load early warning signals of different grades, compiling the optimal charging threshold value and the load early warning signals into executable control commands of the charging pile to be remotely issued, setting a unified control electric quantity value for each charging vehicle, and triggering automatic power-off protection to stop power supply when the charging quantity is monitored to reach a preset threshold value.
2. The internet of things-based charging pile remote safety management system according to claim 1, wherein the vehicle charging demand identification is performed to obtain the total number of vehicles to be charged, and the specific process is as follows: disposing a composite sensor array comprising an electric power parameter sensor, an environment sensor and image pickup equipment in the charging pile body and the peripheral area; Monitoring input voltage, current and power of the charging pile based on the electric power parameter sensor, establishing a historical data storage analysis library, storing historical operation data of the charging pile, and storing data according to peak period, middle peak period and low peak period in a dividing manner, and marking the power consumption and the charging upper limit time of the holiday charging pile; according to the charging pile location range diagram, defining a charging range by taking a charging pile as a center point, carrying out charging waiting route planning and road identification setting, carrying out charging route planning according to different time, generating a real-time charging pile charging road planning diagram, and marking the normal waiting number of vehicles, the early warning waiting number of vehicles and the peak waiting number of vehicles; counting waiting vehicles in a planned route based on the charging road planning diagram to obtain the total number of all vehicles in the waiting area; the vehicle types and license plate images are collected through the camera equipment, the number of vehicles entering a charging area and not being charged is summarized in real time, and the total number of vehicles to be charged is dynamically updated in combination with the occupied state of the charging piles.
3. The internet of things-based charging pile remote safety management system according to claim 1, wherein the charging demand analysis is performed based on the total number of vehicles to be charged and the number of charging piles used in a normal state, and specifically comprises the following steps: S100, acquiring a history charging pile use record, vehicle charging duration and charging quantity, and performing history electricity utilization statistical analysis to obtain average bicycle charging demand electric quantity; s101, calculating a basic charging demand base, wherein the basic demand base is equal to the total number of vehicles to be charged and the average bicycle charging demand electric quantity; calculating a capacity pressure coefficient, wherein the capacity pressure coefficient=the total number of vehicles to be charged/the available number of charging piles in a normal state; acquiring theoretical maximum charging power of a current queuing vehicle, and averaging the theoretical maximum charging power of all vehicles to be charged to obtain the current queuing average charging power; vehicle charge prediction demand value = base demand base x capacity pressure coefficient x efficiency correction factor; S102, obtaining high, medium and low-grade differentiated charging permission labels according to the actual port charging quantity of the vehicle.
4. The charging pile remote safety management system based on the internet of things according to claim 1, wherein a charging scheduling optimization model of power grid constraint is constructed, and the specific process is as follows: S200, constraint conditions of a charging scheduling optimization model of power grid constraint are set, wherein the constraint conditions comprise power grid safety, physical constraint of a charging pile and vehicle demand constraint; S201, input parameter preparation and standardization, namely calculating a real-time load pressure coefficient K, wherein the formula is K= (total number of vehicles to be charged x average charging demand)/(available charging pile number x total capacity of the power grid); k comprehensively reflects the ratio of the vehicle demand to the supply capacity, and maps the theoretical maximum charging power of different vehicle types into a standardized charging efficiency coefficient based on the vehicle charging port standard; S202, constructing an optimization function taking a charging threshold as a decision variable, and constructing through a real-time load pressure coefficient, the total number of vehicles to be charged and the peak load of a safety power grid, wherein N=F× (1-K) ×f, N is the optimal charging threshold of the current vehicle, F is the highest safety electric quantity, and F is the lowest safety electric quantity; The optimal charging threshold of the vehicle is the output dimension of the power grid constrained charging schedule optimization model.
5. The remote safety management system of the charging pile based on the internet of things according to claim 1, wherein the charging scheduling module further comprises a real-time monitoring unit for monitoring the temperature of the charging pile in real time, smoothing the temperature data by adopting a sliding window algorithm, setting a dynamic safety threshold, The normal range is that the surface temperature of the battery pack is less than or equal to 45 ℃ and the interface temperature is less than or equal to 60 ℃; When the temperature exceeds the normal range but does not reach the dangerous value, triggering yellow early warning and starting power back-off; And triggering a red early warning when the temperature of the charging pile is more than or equal to 55 ℃ and the interface temperature is more than or equal to 70 ℃, and immediately stopping charging and starting the cooling system.
6. The charging pile remote safety management system based on the internet of things according to claim 1, wherein the total active power of the whole charging station cluster is monitored and calculated in real time and compared with a safety capacity threshold provided by a power grid side, and the system specifically comprises the following steps: s300, acquiring charging efficiency of a charging pile, accumulating power data of the charging pile, and calculating total active power by combining power factor correction; S301, comparing the real-time total active power with a power grid safety capacity threshold, calculating the load rate, and dividing the early warning level according to the load rate, wherein the early warning level comprises a green safety zone, the load rate is less than 80%, early warning is not needed, and normal charging is kept; The yellow early warning area is 80 percent or less, the load rate is less than 90 percent, the three-stage early warning is triggered, the power back of the charging pile is started, and the user is guided to charge in a peak shifting manner; The orange early warning area is 90 percent or less, the load rate is less than 95 percent, the second-level early warning is triggered, the access of new users is limited, and the charging of the high-priority vehicles is preferentially ensured; And in the red early warning area, the load rate is more than or equal to 95%, the first-stage early warning is triggered, the emergency current limiting strategy is started, the power of all charging piles is forcedly reduced, and the automatic power-off protection is triggered.
7. The charging pile remote safety management system based on the internet of things according to claim 1, wherein the remote control module further comprises an early warning control unit, and the specific contents are as follows: The charging threshold instruction is safely and reliably sent to the appointed charging pile, the state report of the charging pile is received, and whether the instruction is executed correctly is checked; And after receiving the control command to the charging pile, adjusting the output power, feeding back the actual charging quantity, the power grid change and the temperature parameter through the sensor to form closed-loop control, monitoring the total active power change in real time, verifying the effectiveness of the early warning measures, and if the load rate is continuously increased, further upgrading the early warning grade.
8. The charging pile remote safety management system based on the internet of things according to claim 1 is characterized by further comprising a safety execution and emergency module for monitoring a real-time charging process of each charging pile, sending a power supply stopping instruction to the charging pile and completing settlement when the battery capacity of the vehicle reaches an actual upper limit after dynamic adjustment, and simultaneously, carrying out risk judgment based on the residual cruising of the vehicle and the load information of a peripheral charging network, touching an alert vehicle on a detected cruising safety threshold, starting an emergency exempting process, and authorizing the temporary lifting of the upper limit of charging.

Description

Charging pile remote safety management system based on Internet of things Technical Field The invention relates to the technical field of data monitoring and control, in particular to a charging pile remote safety management system based on the Internet of things. Background With the rapid development of the electric automobile industry, the charging pile is taken as an important infrastructure of the electric automobile, the number of charging pile remote safety management systems is continuously increased, powerful technical support is provided for effective management and safe operation of the charging pile, charging experience of a user and efficiency of charging pile operation are greatly improved, and at present, the technology of the Internet of things is widely applied in the field of charging pile management. Through the internet of things, the charging pile can communicate with a background management system in real time, and functions of remote monitoring, fault diagnosis, charging management and the like are realized. For example, parameters such as voltage, current, temperature and the like of the charging pile are acquired in real time by using a sensor technology and are uploaded to a background system for analysis and processing, so that potential safety hazards can be found in time and corresponding measures can be taken. Meanwhile, through the intelligent charging system, reasonable charging is carried out according to the charging quantity and time, so that user settlement is facilitated. However, there is no effective countermeasure against the special situation that the holiday charging vehicles are too many. The existing system mainly focuses on equipment monitoring and basic charging management under normal conditions, and has relatively weak load balancing and charge amount control functions during the charge peak period. The intelligent charging scheduling algorithm and the real-time electric quantity distribution strategy are lacking, and the charging quantity of the charging pile cannot be dynamically adjusted according to actual charging requirements and power grid conditions so as to ensure the safe operation and charging efficiency of the charging pile; in view of the above technical drawbacks, a solution is now proposed. Disclosure of Invention The method and the system aim at obtaining the total number of vehicles to be charged through deploying the sensor array of the internet of things, combining the actual port charging efficiency of the vehicles, generating the vehicle charging prediction demand value, constructing the charging dispatching optimization model of the power grid constraint, comprehensively considering the total number of the vehicles to be charged, the total number of the charging piles and the total capacity of the power grid, carrying out comprehensive load pressure analysis of the charging power grid, generating the optimal charging threshold value of the charging piles, monitoring and calculating the total active power of the whole charging station cluster in real time, comparing the total active power with the safety capacity threshold value provided by the power grid side, receiving the optimal charging threshold value of the charging piles and load early warning signals of different grades, and realizing remote and accurate control of the charging piles. In order to achieve the aim, the charging pile remote safety management system based on the Internet of things comprises an Internet of things data acquisition module, a live analysis module, a charging scheduling module, a load analysis module and a remote control module; The system comprises an Internet of things data acquisition module, a charging pile power grid load data acquisition module and a charging pile parameter acquisition module, wherein the Internet of things data acquisition module acquires vehicle characteristic data, the charging pile power grid load data and the charging pile parameter in real time through an Internet of things sensor array deployed on the charging pile, acquires vehicle data based on camera equipment, and recognizes the vehicle charging requirement to obtain the total number of vehicles to be charged; The live analysis module is used for carrying out charge demand analysis based on the total number of vehicles to be charged and the use number of the charging piles in a normal state, carrying out charge analysis by combining the actual port charging efficiency of the vehicles, generating a vehicle charge prediction demand value, and carrying out charge grade division on the charge type according to the load of vehicle charging to obtain high, medium and low grade differentiated charge permission labels; the charging dispatching module is used for constructing a power grid constraint charging dispatching optimization model, carrying out charging power grid load pressure analysis by combining the total number of the charging piles and the total capacity of the power g