Intelligent orderly load management and control device for charging station

Abstract

The invention belongs to the technical field of electric vehicle charging facilities, and particularly relates to a charging station intelligent ordered load management and control device which comprises a load acquisition module, an intelligent ordered controller, a communication module and a charging pile interface adaptation module. The overload risk is avoided in advance, passive tripping protection is replaced, safety accidents are avoided, and the service life of the box transformer substation and the charging pile is prolonged by 15-20%; a dynamic power distribution algorithm adapts to load changes, the average utilization rate of the charging pile is improved by 30-40%, power supply resource waste is avoided, user experience is optimized, a priority strategy guarantees important user requirements, charging continuity is ensured through active adjustment, the user complaint rate is reduced by 60% or above, operation cost is reduced, full-process automatic management and control is achieved, and 80% of manual monitoring cost is reduced. Equipment maintenance cost is reduced by 25-30%, compatibility is high, the equipment can be directly connected with existing station equipment, large-scale transformation is not needed, deployment cost is low, and generalization performance is high.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging facility technology, specifically to an intelligent and orderly load management device for charging stations. Background Technology

[0002] With the rapid growth in the number of new energy vehicles, the large-scale application of charging stations, as high-load power consumption nodes, has led to increasingly prominent grid compatibility issues. Current technologies often employ single-dimensional control strategies for charging station load control, such as simply limiting power output based on grid load limits or merely meeting users' charging time demands. This results in a difficult-to-reconcile conflict between grid security, user experience, and operational revenue.

[0003] Existing technology 1: Charging pile management and control scheme based on fixed power allocation - technical solution: by presetting the maximum output power limit of each charging pile (such as limiting a 120kW charging pile to 80kW operation), it is ensured that the total power of all charging piles running at the same time does not exceed the transformer capacity. However, the above patent has problems such as high cost, high error rate, environmental unfriendliness, slow response speed, and inability to achieve certain functions.

[0004] Existing Technology 2: Passive Control Scheme Based on Transformer Trip Feedback - Technical Solution: An overload protection switch is installed on the outgoing side of the transformer substation. When the total load exceeds the rated capacity, the protection switch automatically trips and cuts off the power supply. After the load decreases, the switch is manually closed to restore power. However, the above-mentioned patent has static power parameter configuration, which cannot be dynamically adjusted according to the actual load, resulting in low utilization of charging piles. For example, when some charging piles are not in use, the remaining capacity cannot be fully utilized by other charging piles, resulting in a waste of power supply resources; and when multiple charging piles start up simultaneously, the power limit may still be exceeded, leading to overload.

[0005] Existing technology 3: Control scheme based on manual intervention in the background - Technical solution: The load data of the transformer substation is displayed in real time through the background management system. When the load approaches the threshold, the staff sends shutdown or power reduction instructions to some charging piles through 4G / 5G communication. However, the above patent has the problems of slow response speed, with a delay of 10-30 seconds, which cannot cope with the scenario of rapid load increase; high labor cost, requiring dedicated personnel to be on duty 24 hours a day; easy to cause overload risk due to human negligence, and low control reliability. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent and orderly load management device for charging stations. This device solves problems such as high cost, high error rate, environmental unfriendliness, slow response time, inability to perform certain functions, and inability to dynamically adjust based on actual load, leading to low utilization rates of charging piles. For example, when some charging piles are not in use, the remaining capacity cannot be fully utilized by other charging piles, resulting in wasted power resources; when multiple charging piles start simultaneously, the power limit may still be exceeded, leading to overload and an inability to cope with scenarios of rapid load increases; high labor costs require dedicated personnel for 24-hour monitoring; and the risk of overload due to human error results in low management reliability.

[0007] A smart and orderly load management device for charging stations comprises two parts: hardware modules and software algorithms. The structure and connection method of each module are as follows: Load acquisition module The system employs a high-precision three-phase smart meter, model DTZY1352-Z, with an error class of 0.2S. It integrates voltage, current, power, and energy consumption parameter acquisition functions and is equipped with an electromagnetic interference protection module. This module is connected in series on the low-voltage outgoing side of the transformer substation and bidirectionally connected to the intelligent orderly controller via an RS485 communication interface. It supports an adjustable sampling frequency of 1-10Hz, with a default sampling frequency of 5Hz, and is used for real-time uploading of transformer substation load data.

[0008] Intelligent Order Controller As the core module of the device, it adopts an industrial-grade ARM Cortex-A9 processor with a main frequency of 1GHz, equipped with 2GB DDR3 memory and 16GB Flash storage. It integrates a 4-20mA analog input interface, 8-channel DI / 8-channel DO digital input / output interfaces, 2 RS485 channels, 1 Ethernet channel, 1 4G / 5G module, and 1 Wi-Fi module. It has a built-in power management module with AC220V±10% input and DC12V / 5A and DC5V / 3A outputs, adapting to complex power supply environments. This module is installed in the substation control room or independent control cabinet, receiving data from the load acquisition module, running load prediction and power allocation algorithms, storing historical data and equipment parameters, and interacting with the back-end management system.

[0009] Communication module It adopts a multi-communication mode integration design, including an RS485 communication unit, an Ethernet unit, a full-network compatible 4G / 5G unit, and a Wi-Fi unit supporting 802.11b / g / n. It integrates a protocol conversion module, supporting Modbus-RTU, TCP / IP, and MQTT protocols. One end is fixedly connected to the intelligent orderly controller via Ethernet, and the other end is connected to the charging pile interface adapter module via wired or wireless means, realizing bidirectional transmission of data and commands.

[0010] Charging pile interface adapter module A standardized interface adapter unit is designed for AC / DC charging piles of different brands and models, supporting the parsing and conversion of common charging pile protocols such as CAN bus and RS485. One end of this module connects to the communication module, and the other end connects one-to-one with the control interface of each charging pile to ensure accurate issuance of adjustment commands and feedback of the real-time charging status of the charging pile.

[0011] Software Algorithm The software algorithm of this device consists of three parts: data acquisition and preprocessing, load prediction, and power allocation. Load data acquisition and preprocessing algorithm: The Kalman filter algorithm is used to remove abnormal data caused by electromagnetic interference, calculate the real-time load rate of the transformer, and store the load data curve of the past 24 hours. Load prediction algorithm: Based on the LSTM long short-term memory network model, combined with historical load data, scheduled charging information, time period characteristics, and weather data, it predicts the load peak in the next 5-15 minutes. The model is trained with the initial 3 months of historical data and subsequently updated and optimized in real time, with a prediction error of ≤±5%. Power distribution and regulation algorithm: Set 80% of the rated capacity of the transformer as the warning threshold and 90% as the safety threshold, and implement a three-level control strategy based on the real-time load rate and predicted peak value.

[0012] Compared with the prior art, the present invention provides an intelligent and orderly load management device for charging stations, which has the following beneficial effects: Significantly enhanced safety: Through real-time monitoring and load prediction dual protection, overload risks are avoided in advance, replacing passive trip protection, preventing safety accidents, and extending the service life of transformer substations and charging piles by 15-20%.

[0013] Maximizing resource utilization: The dynamic power allocation algorithm adapts to load changes, increasing the average utilization rate of charging piles by 30-40% and avoiding waste of power supply resources.

[0014] User experience optimization: Prioritization strategies ensure the needs of important users, proactive adjustments ensure charging continuity, and user complaint rates have decreased by more than 60%.

[0015] Reduced operating costs: Full-process automated management reduces manual monitoring costs by 80% and equipment maintenance costs by 25-30%.

[0016] High compatibility: It supports multiple communication methods and charging pile protocols, and can be directly connected to existing site equipment without large-scale modification, resulting in low deployment cost and strong promotion.

[0017] High level of intelligence: The LSTM model enables forward prediction of load peaks, and the back-end management system supports data visualization and remote parameter configuration, facilitating refined management. Attached Figure Description

[0018] Figure 1 This is a block diagram of the overall structure of the present invention; Figure 2 This is a flowchart of the process of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0020] The following example, using a charging station with 10 120kW DC charging piles and 1 1000kVA transformer, illustrates the implementation process of this invention in detail: Example 1: Device Hardware Deployment and Debugging Schematic and circuit diagram design: Draw the overall electrical schematic diagram of the device, and clarify the wiring methods of the load acquisition meter, current transformer, intelligent orderly controller and communication module; design the PCB circuit board based on the electrical schematic diagram, integrate the controller core chip, interface circuit and power management circuit, and design the strong and weak current separation wiring diagram of the control cabinet.

[0021] Data output: Compile a hardware bill of materials, specifying the model, specifications, and supplier of each component, such as selecting DTZY1352-Z for smart meters and Huawei ME909S-821 for 4G modules; output production process documents such as welding specifications and assembly procedures.

[0022] Programming: Based on the Linux system, the core control program was written in C language; the LSTM load prediction model code was written in Python and integrated into the controller program; the backend management system was developed using the Java+Vue framework to realize data visualization, parameter configuration, and alarm notification functions.

[0023] Sample production: Purchase components according to the bill of materials, perform PCB board soldering and module assembly, burn the controller program, and complete the initial integration of software and hardware.

[0024] Software and hardware debugging Hardware debugging: Test the power supply stability of the module. When the input is AC220V±10%, the output voltage fluctuation is ≤±0.1V; test the connectivity of the communication module. The RS485 communication distance is ≤1000m, and the 4G / 5G communication delay is ≤50ms; test the accuracy of the load acquisition module. The power measurement error is ≤±0.5%.

[0025] Software debugging: Simulate scenarios such as no load, 50% load, 80% load, and 95% load to test the accuracy of the load prediction algorithm. The error between the predicted peak and the actual peak should be ≤ ±3%. Test the command execution efficiency. The response time from the command to the charging pile should be ≤ 200ms. Verify the effectiveness of the priority strategy to ensure that high-priority charging piles operate at full power.

[0026] Full-scale commissioning: The device was deployed to an actual charging station and connected to 10 120kW DC charging piles and a 1000kVA transformer substation for 72 hours of continuous trial operation, simulating scenarios such as simultaneous charging by multiple users, sudden high-power startup, and scheduled charging. The trial operation results showed that the transformer substation load rate was consistently controlled below 90%, the average utilization rate of the charging piles increased by 35%, and the number of charging interruptions was 0.

[0027] Example 2: Control Effects under Different Load Scenarios Low load scenario: 3 charging piles are in operation, with a total load of 300kW and a load rate of 30% < 80%. All operating charging piles operate at full power of 120kW, and the remaining 7 charging piles can be started at full power charging at any time.

[0028] Medium load scenario: 7 charging piles are in operation, with a total load of 750kW and a load rate of 75% between 80% and 90%. The device dynamically allocates the remaining 150kW capacity and distributes power according to the proportion of the remaining power of the charging piles. The total load is stable within 900kW, and there is no power reduction or shutdown when there are no charging piles.

[0029] High load scenario: 10 charging piles start simultaneously, with a predicted total load of 1100kW and a load rate of 110%≥90%. The device starts with priority adjustment to ensure that 2 reserved user charging piles and 1 emergency charging pile operate at full power, while the remaining 7 temporary user charging piles are each limited to 77kW, and the total load is controlled at 899kW to avoid overload.

[0030] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart and orderly load management device for charging stations, characterized in that: It includes a load acquisition module, an intelligent orderly controller, a communication module, and a charging pile interface adapter module; The load acquisition module uses a high-precision three-phase smart meter, which is connected in series on the low-voltage outgoing side of the transformer substation. It is bidirectionally connected to the intelligent orderly controller through an RS485 communication interface to collect the total load power, three-phase current, and voltage data of the transformer substation, and supports an adjustable sampling frequency of 1-10Hz. The intelligent orderly controller adopts an industrial-grade ARM Cortex-A9 processor, equipped with 2GB DDR3 memory and 16GB Flash storage, and integrates analog input interface, digital input / output interface and multiple communication interfaces. It has a built-in power management module to receive data from the load acquisition module, run load prediction algorithm and power allocation algorithm, and generate power adjustment instructions. The communication module integrates multiple communication methods such as RS485, Ethernet, 4G / 5G, and Wi-Fi, and integrates a protocol conversion module to support Modbus-RTU, TCP / IP, and MQTT protocols. One end is connected to the intelligent orderly controller, and the other end is connected to the charging pile interface adapter module. The charging pile interface adapter module is designed with standardized interface adapter units for AC / DC charging piles of different brands and models. It supports protocol parsing and conversion such as CAN bus and RS485, and connects one-to-one with the charging pile cluster to send power adjustment commands and provide feedback on the real-time status of the charging piles.

2. The intelligent and orderly load management device for charging stations according to claim 1, characterized in that: The load prediction algorithm built into the intelligent orderly controller adopts the LSTM long short-term memory network model, combined with historical load data, charging pile reservation charging information, time period characteristics, and weather data, to predict the peak load of the transformer substation in the next 5-15 minutes, with the prediction error controlled within ±5%.

3. The intelligent and orderly load management device for charging stations according to claim 1, characterized in that: The intelligent orderly controller's built-in power allocation algorithm sets 80% of the transformer's rated capacity as a warning threshold and 90% as a safety threshold, implementing a three-level control strategy: When the real-time load rate is less than 80%, all charging piles operate at their maximum rated power. When 80% ≤ Real-time load rate < 90%, dynamic power allocation is activated to allocate the remaining capacity according to the real-time charging demand of the charging pile. When the real-time load rate is ≥90% or the predicted peak load is ≥90%, priority adjustment is initiated to ensure that high-priority charging piles operate at their rated power, while reducing the power or suspending low-priority charging piles.

4. The intelligent and orderly load management device for charging stations according to claim 1, characterized in that: The high-priority charging piles include dedicated charging piles for emergency vehicles and charging piles occupied by reserved users, while the low-priority charging piles are for temporary users.

5. The charging station load control method according to any one of claims 1-4, characterized in that, Includes the following steps: 。 6.S1. The device is powered on and initialized. The load acquisition module acquires the transformer load data according to the set sampling frequency. S2. The intelligent orderly controller performs Kalman filtering preprocessing on the collected data and calculates the real-time load rate; S3. The intelligent orderly controller predicts the load peak in the next 5-15 minutes using an LSTM model; S4. The intelligent orderly controller determines the threshold range to which the load status belongs based on the real-time load rate and the predicted peak value; S5. The intelligent orderly controller executes the corresponding power allocation strategy and generates adjustment commands; S6. The communication module sends adjustment commands to each charging pile through the charging pile interface adapter module; S7. The charging pile executes commands and feeds back the real-time status to the intelligent orderly controller; S8. Repeat steps S1-S7 to achieve closed-loop control.

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