Intelligent charging station system and charging method

Abstract

The invention discloses an intelligent charging station system and a charging method, and belongs to the technical field of intelligent charging. The charging station system comprises a sensing layer used for collecting multi-source data in real time; the network layer is used for communicating with the sensing layer to receive the multi-source data, performing fusion analysis on the multi-source data and predicting a charging index; and the network layer is also used for calculating the charging priority of the accessed vehicle according to the charging index and a preset weight factor so as to generate a power distribution scheme based on the charging priority and the charging power influence data through a preset scheduling algorithm and issue the power distribution scheme. According to the charging station system, the energy calling strategy can be optimized, a better charging effect is achieved, the charging efficiency of the accessed vehicle is improved, and meanwhile the resource utilization rate and the operation efficiency of the charging station are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent charging technology, and in particular to an intelligent charging station system and a charging method. Background Art

[0002] With the rapid growth of new energy vehicles, the demand for charging stations is also increasing. Traditional charging stations have many drawbacks, such as low charging efficiency and an inability to intelligently adjust charging based on the battery status of different vehicles and user needs. They also lack effective energy management mechanisms, which prevent them from fully utilizing renewable energy, leading to energy waste and increased costs. Furthermore, operational management issues include difficulty in maintenance, poor user experience, and poor interaction with the power grid.

[0003] The specific issues can be sorted out as follows:

[0004] "Fool-style" charging: simply allocated on a first-come, first-served basis or with a fixed power, and cannot be optimized based on the vehicle battery status, the urgency of user needs, or the real-time status of the power grid.

[0005] Grid shock: A large number of vehicles fast charging at the same time causes a sharp increase in the peak load of the grid, affecting grid stability and increasing the cost of grid expansion.

[0006] Poor user experience: long queues, charging speeds not meeting expectations, and lack of personalized service.

[0007] Low operational efficiency: uneven utilization of charging piles, passive equipment maintenance, and lack of refined operational data support.

[0008] Waste of resources: Failure to effectively utilize vehicle batteries as distributed energy storage resources.

[0009] Data silos: Charging data, vehicle data, grid data, and user data are isolated from each other and cannot be optimized collaboratively. Summary of the Invention

[0010] The present application provides an intelligent charging station system and a charging method for solving at least one of the above technical problems.

[0011] This application adopts the following technical solutions:

[0012] On the one hand, the present application provides an intelligent charging station system, which includes: a perception layer for collecting multi-source data in real time; a network layer for communicating with the perception layer to receive the multi-source data, perform fusion analysis on the multi-source data, and predict charging indicators; the network layer is also used to calculate the charging priority of the connected vehicle based on the charging indicator and a preset weight factor, so as to generate and issue a power allocation plan based on the charging priority and charging power impact data through a preset scheduling algorithm.

[0013] In a possible implementation of the present application, the multi-source data includes at least one of charging pile terminal data, vehicle data, grid status data, environmental data, user interaction data, on-site energy storage data and local renewable energy data; wherein, the charging pile terminal data includes at least one of charging voltage, charging current, charging power, charging temperature, connection status and fault data; the vehicle data includes at least one of vehicle battery status data, vehicle identity data and user preset data; the grid status data includes at least one of grid voltage, grid frequency, grid active / reactive power, grid electricity price signal, grid load instruction and grid demand information; the environmental data includes at least one of temperature data, humidity data and light data; the user interaction data includes at least one of target SOC, expected departure time, service preference data and payment confirmation data; the on-site energy storage data includes at least one of energy storage SOC, charge and discharge status data and health status data; the local renewable energy data includes at least power generation data.

[0014] In one possible implementation of the present application, the perception layer includes a charging pile terminal for collecting the charging pile terminal data, a vehicle interaction interface for obtaining the vehicle data, a grid status monitoring unit for obtaining the grid status data, a temperature and humidity sensor and / or a light sensor for collecting the environmental data, a user interaction terminal or a user interaction APP for receiving the user interaction data, an in-station energy storage system for providing the in-station energy storage data, and a local renewable energy power generation system for providing the local renewable energy data.

[0015] In one possible implementation of the present application, the network layer includes a communication network unit and an edge computing / cloud platform; wherein, the communication network unit is used to provide at least one communication mode of wired Ethernet, 5G and Wi-Fi6 to realize data transmission between the perception layer and the edge computing / cloud platform.

[0016] In one possible implementation of the present application, the edge computing / cloud platform includes: a data acquisition and storage module for receiving and storing multi-source data from the perception layer; a multi-source data fusion analysis module for performing data fusion on the multi-source data and predicting charging indicators based on the fused data, wherein the charging indicators include at least one of the time required for vehicle charging to be completed, the future short-term load of the power grid, the renewable energy power generation power and the number of charging station queues; a dynamic priority scheduling algorithm module for calculating the comprehensive priority score of each connected vehicle in real time based on the charging indicators and preset weight factors, and applying a preset scheduling algorithm based on the comprehensive priority score and charging power impact data to generate a power allocation plan, wherein the power allocation plan includes at least the output power of each charging pile; a control instruction generation and issuance module for generating multiple control instructions based on the generated power allocation plan and issuing them accordingly.

[0017] In one possible implementation of the present application, in the dynamic priority scheduling algorithm module, the comprehensive priority of the connected vehicle is a function of the preset weight factor; wherein, the preset weight factor is a dynamically adjusted weight factor, and the dynamically adjusted weight factor includes at least one of the user demand urgency weight, grid friendliness weight, battery health optimization weight, operating income weight, fairness weight and in-station resource coordination weight.

[0018] In one possible implementation of the present application, in the dynamic priority scheduling algorithm module, the charging power affects at least one of the total available power upper limit of the charging pile, the grid constraints and the energy storage status of the charging station, and the preset scheduling algorithm is a linear programming algorithm and / or a heuristic algorithm.

[0019] In one possible implementation of the present application, the power allocation scheme generated by the dynamic priority scheduling algorithm module includes at least one of increasing / decreasing the charging power of a specific vehicle, pausing / resume the charging status of a specific vehicle, starting / stopping energy storage charging or discharging, and calling V2G services; the control instructions generated by the control instruction generation and issuance module include power allocation instructions and / or switch control instructions, and the issuance objects of the control instructions include at least one of the charging pile controller, the energy storage management system, and the V2G controller.

[0020] In one possible implementation of the present application, the network layer also includes: a user service and interaction module, which is used to push charging status, estimated completion time, cost information, value-added services, and at least one of V2G participation invitations and revenue estimates to user APP / terminals; an operation management module, which is used to provide at least one of equipment status monitoring, fault warning, energy efficiency analysis, revenue reports, and user behavior analysis; a V2G / V2X management module, which is used to coordinate connected vehicles that meet preset conditions to reversely transmit power to the power grid or charging station, manage the charging and discharging process, and calculate and settle revenue.

[0021] On the other hand, the present application also provides a charging method, which uses an intelligent charging station system as described in any of the above implementation methods, and the method includes: real-time collection of multi-source data; fusion analysis of the multi-source data to predict charging indicators; calculating the charging priority of the connected vehicle based on the charging indicators and preset weight factors, so as to generate and issue a power allocation plan based on the charging priority and charging power impact data through a preset scheduling algorithm.

[0022] The present application provides a smart charging station system and charging method, which have the following beneficial effects:

[0023] After real-time collection of multi-source data in the smart charging station through the perception layer, it is uploaded to the network layer, and the network layer is used to perform fusion analysis of multi-processing to predict the charging index. The charging index and the preset weight factor are then used to calculate the charging priority of the vehicles connected to the charging station. Based on this charging priority, and taking into account the charging power impact data, a power allocation plan is generated and issued to accurately control each charging pile, realizing dynamic optimal scheduling of resources in the charging station, thereby improving the charging efficiency of connected vehicles and helping to maintain the stability of the power grid. In addition, the scheduling of renewable energy and inviting users to participate in V2G also help improve the resource utilization and operational efficiency of the charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments described in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0025] Figure 1 An architectural diagram of a smart charging station system provided in this application;

[0026] Figure 2 A flow chart of a charging method provided in this application. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the technical solutions of this application, the technical solutions of this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0028] The method in this application is described in detail below with reference to the accompanying drawings.

[0029] Figure 1 This is an architecture diagram of a smart charging station system provided by this application; Figure 1 As shown, the smart charging station system in this application includes a perception layer and a network layer.

[0030] The perception layer is used to collect multi-source data of charging stations in real time. Specifically, it includes:

[0031] A charging pile terminal, configured to collect charging pile terminal data, the charging pile terminal data including at least one of charging voltage, charging current, charging power, charging temperature, connection status, and fault data;

[0032] A vehicle interaction interface for acquiring vehicle data, including at least one of vehicle battery status data, vehicle identity data, and user-preset data. Vehicle interaction interfaces include OBC, CCS, ChaoJi, etc. Vehicle battery status data includes SOC, SOH, temperature, maximum allowable charging power / current, etc. User-preset data includes target SOC, expected departure time, etc.

[0033] a grid status monitoring unit, configured to obtain grid parameters at the connection point in real time, including at least one of grid voltage, grid frequency, grid active / reactive power, grid electricity price signal, grid load instruction, and grid demand information;

[0034] Environmental monitoring unit, including temperature and humidity sensors and light sensors. Light data acquired by the light sensors is preferably used for photovoltaic prediction;

[0035] A user interaction terminal / APP is configured to receive user interaction data input by a user, including at least one of a target SOC, a desired departure time, service preferences, and payment confirmation, and to provide status feedback, including estimated charging completion time, charging cost, charging queue information, and recommended services;

[0036] The on-site energy storage system is used to provide on-site energy storage data, including monitoring of energy storage SOC, charge and discharge status, and health status;

[0037] Local renewable energy systems, such as photovoltaic systems or wind power systems, are used to monitor power generation data.

[0038] The network layer is composed of communication network units and edge computing / cloud platforms. The communication network units are used to realize data transmission between the perception layer and the edge computing / cloud platform, including communication methods or communication functions such as wired Ethernet, 5G and Wi-Fi6.

[0039] The edge computing / cloud platform layer serves as the intelligent decision-making core of the smart charging station system and consists of the following modules:

[0040] The data acquisition and storage module is used to receive and store multi-source data from the perception layer, that is, to receive monitoring data uploaded by various units or systems of the perception layer through the communication network unit; in one example, the data uploaded by the perception layer to the network layer may also include historical data.

[0041] The multi-source data fusion analysis module is used to fuse multi-source data and predict charging indicators based on the fused data. Charging indicators include at least one of the time required for vehicle charging to complete, future short-term grid load, renewable energy generation power, and the number of charging station queues. Specifically, by integrating vehicle battery data, user demand data, real-time grid status and forecast data, electricity price information, station resource status including charging piles, energy storage, renewable energy generation, environmental data, and historical charging behavior data, it predicts key indicators such as the time required for vehicle charging to complete, future short-term grid load trends, renewable energy generation forecasts, and charging station queues.

[0042] The dynamic priority scheduling algorithm module is configured to calculate a comprehensive priority score for each connected vehicle in real time based on charging metrics and preset weighting factors. Based on the comprehensive priority score and charging power impact data, a preset scheduling algorithm, preferably a linear programming algorithm and / or a heuristic algorithm, is applied to generate a power allocation plan. The power allocation plan includes at least the output power of each charging station. In one example, the charging power impact data includes at least one of the total available power limit of the charging station, grid constraints, and the energy storage status of the charging station. In another example, the charging priority of each connected vehicle is dynamically calculated based on the results of a fusion analysis. The priority is a function of multi-dimensional weights, which are preferably dynamically adjustable. These weights include: User demand urgency weight (W_urgency): A "time margin" calculated based on the expected departure time, current state of charge (SOC), and target state of charge (SOC). The smaller the margin, the higher the priority. Grid friendliness weight (W_grid): This weight encourages charging during off-peak electricity prices and periods of high renewable energy output, or discourages charging or enables V2G during peak electricity prices and periods of heavy grid load or demand. The weights are dynamically adjusted based on real-time grid status and electricity price signals. Battery Health Optimization Weight (W_battery): Considers battery SOH and temperature to optimize the charging profile. For example, when SOH is low or the temperature is abnormal, a gentler charging strategy is adopted to extend battery life. Operational Revenue Weight (W_revenue): Considers different service packages, such as priority charging and guaranteed rate, V2G service revenue potential, and basic service fees. Fairness Weight (W_fairness): Prevents low-priority vehicles from waiting for long periods of time by introducing a waiting time factor. In-station Resource Collaboration Weight (W_resource): Optimizes energy storage charging and discharging strategies and maximizes local renewable energy consumption.

[0043] In one possible implementation, the dynamic priority scheduling algorithm module can also be used to generate the optimal charging power in real time and accurately control the output power of each charging pile, which can be lower than the maximum capacity of the pile. Preferably, it includes: increasing / decreasing the charging power of a specific vehicle; pausing / resume the charging of a specific vehicle under the policy allowed by the user; starting / stopping energy storage charging or discharging; and calling V2G services when supported by the vehicle and user agreement.

[0044] The control instruction generation and issuance module is used to generate multiple control instructions based on the generated power allocation plan and issue them accordingly. Preferably, power allocation instructions and switch control instructions are generated based on the power allocation plan and issued to the charging pile controller, energy storage management system, and V2G controller accordingly.

[0045] In addition, the smart charging station system in this application may also include:

[0046] The user service and interaction module is used to push charging status, estimated completion time, cost information, and value-added service recommendations such as lounge access, catering, and car washes to the user's app / terminal; it can also provide V2G participation invitations and revenue estimates.

[0047] The operation management module is used to provide at least one of equipment status monitoring, fault warning, energy efficiency analysis, profit reporting and user behavior analysis to increase the operation efficiency of the smart charging station.

[0048] The V2G / V2X management module is the value-added core of the smart charging station system. It is used to coordinate the reverse power transmission of connected vehicles that meet the preset conditions to the power grid or charging station, manage the charging and discharging process, and calculate and settle the profits.

[0049] It should be noted that the modules within the above-mentioned smart terminal system can be added and deleted as needed, and can also be replaced with other modules that meet the needs. This application only provides an exemplary functional description and is not used to limit the module division results within the smart charging station system.

[0050] This application uses the aforementioned system to achieve an optimal power utilization strategy, improve the charging efficiency of connected vehicles, and also help improve the operational efficiency and operating revenue of smart charging stations. Specifically, by deeply integrating multi-dimensional real-time data and applying a dynamic priority scheduling algorithm, it achieves optimal charging process, flexible regulation of grid load, improved user experience, and enhanced operational efficiency, while also providing a foundation for applications such as V2G / V2X.

[0051] Based on the same inventive concept, the present application also provides a charging method, which uses a smart charging station system as described in any of the above embodiments, and its process is as follows: Figure 2 shown.

[0052] Figure 2 A flow chart of a charging method provided in this application, such as Figure 2 As shown, the charging method provided in this application includes at least the following steps:

[0053] Step 201: Collect multi-source data in real time.

[0054] Step 202: Perform fusion analysis on multi-source data to predict charging indicators.

[0055] Step 203: Calculate the charging priority of the connected vehicle according to the charging index and the preset weight factor, and generate and issue a power allocation plan based on the charging priority and charging power impact data through a preset scheduling algorithm.

[0056] In one possible implementation of the present application, the aforementioned charging method may also be provided as a charging management method, which at least includes:

[0057] Vehicle access and information acquisition: When the vehicle is connected to the charging station, the system obtains the vehicle battery status and VIN through the vehicle interaction interface, and obtains user needs through the user APP / terminal, including target SOC, expected departure time, service selection, etc.

[0058] Real-time data collection from multiple sources: Synchronous collection of grid status, electricity prices, station resource status, and environmental data. Station resource status includes charging piles, energy storage, and renewable energy.

[0059] Data fusion analysis and prediction: Integrate all data to predict key charging indicators, including charging time, grid load, renewable energy output, etc.

[0060] Dynamic priority calculation: Based on the current fusion analysis results, the predicted charging indicators and preset / dynamically adjusted weight factors are optimized to calculate the comprehensive priority score of each connected vehicle in real time.

[0061] Optimal power allocation decision: Based on priority scores, the total available power limit of charging piles, grid constraints, energy storage status, etc., optimization algorithms such as linear programming and heuristic algorithms are applied to solve the optimal power allocation plan at the current moment.

[0062] Control command issuance and execution: The power command is issued to the corresponding charging pile controller for execution, while controlling energy storage charging and discharging and V2G operations.

[0063] Real-time monitoring and dynamic adjustment: Continuously monitor all status changes, such as new vehicle access, user modification requirements, sudden changes in grid status, equipment failures, etc., and repeat the above steps or processes periodically or event-triggered to make dynamic adjustments.

[0064] User interaction and services: real-time update of user interface information, push of related services and notifications.

[0065] Data recording and analysis: Recording of entire process data for algorithm optimization, operational analysis, and fault diagnosis.

[0066] Through the above-mentioned charging method or charging management method, this application can optimize the energy utilization efficiency of the charging station, thereby achieving a better power utilization strategy, improving the charging efficiency of connected vehicles, and at the same time improving the resource utilization and operational efficiency of the charging station.

[0067] The various embodiments in this application are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0068] The device and method provided in this application correspond one to one, so the device also has similar beneficial technical effects as its corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device will not be repeated here.

[0069] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0070] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0071] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included in the protection scope of the present application.

Claims

1. A smart charging station system, characterized in that: The system comprises: Perception layer, used to collect multi-source data in real time; The network layer is configured to communicate with the perception layer to receive the multi-source data, perform fusion analysis on the multi-source data, and predict charging indicators; The network layer is further used to calculate the charging priority of the connected vehicle according to the charging indicator and the preset weight factor, so as to generate and issue a power allocation plan based on the charging priority and charging power impact data through a preset scheduling algorithm.

2. The intelligent charging station system according to claim 1, characterized in that: The multi-source data includes at least one of charging pile terminal data, vehicle data, grid status data, environmental data, user interaction data, on-site energy storage data, and local renewable energy data; Among them, the charging pile terminal data includes at least one of charging voltage, charging current, charging power, charging temperature, connection status and fault data; the vehicle data includes at least one of vehicle battery status data, vehicle identity data and user preset data; the grid status data includes at least one of grid voltage, grid frequency, grid active / reactive power, grid electricity price signal, grid load instruction and grid demand information; the environmental data includes at least one of temperature data, humidity data and light data; the user interaction data includes at least one of target SOC, expected departure time, service preference data and payment confirmation data; the in-station energy storage data includes at least one of energy storage SOC, charging and discharging status data and health status data; the local renewable energy data includes at least power generation data.

3. The intelligent charging station system according to claim 2, characterized in that: The perception layer includes a charging pile terminal for collecting the charging pile terminal data, a vehicle interaction interface for obtaining the vehicle data, a grid status monitoring unit for obtaining the grid status data, a temperature and humidity sensor and / or a light sensor for collecting the environmental data, a user interaction terminal or a user interaction APP for receiving the user interaction data, an in-station energy storage system for providing the in-station energy storage data, and a local renewable energy power generation system for providing the local renewable energy data.

4. The intelligent charging station system according to claim 1, characterized in that: The network layer includes a communication network unit and an edge computing / cloud platform; Among them, the communication network unit is used to provide at least one communication mode of wired Ethernet, 5G and Wi-Fi6 to realize data transmission between the perception layer and the edge computing / cloud platform.

5. The intelligent charging station system according to claim 4, characterized in that: The edge computing / cloud platform includes: A data acquisition and storage module, configured to receive and store multi-source data from the perception layer; a multi-source data fusion analysis module, configured to fuse the multi-source data and predict charging indicators based on the fused data, the charging indicators including at least one of the time required for vehicle charging to complete, future short-term load of the power grid, renewable energy power generation, and the number of charging station queues; a dynamic priority scheduling algorithm module, configured to calculate, in real time, a comprehensive priority score for each connected vehicle based on the charging indicator and a preset weight factor, and apply a preset scheduling algorithm based on the comprehensive priority score and charging power impact data to generate a power allocation plan, the power allocation plan including at least the output power of each charging pile; The control instruction generation and issuance module is used to generate multiple control instructions according to the generated power allocation plan and issue them accordingly.

6. The intelligent charging station system according to claim 5, characterized in that: In the dynamic priority scheduling algorithm module, the comprehensive priority of the access vehicle is a function of the preset weight factor; Among them, the preset weight factor is a dynamically adjusted weight factor, and the dynamically adjusted weight factor includes at least one of the user demand urgency weight, grid friendliness weight, battery health optimization weight, operating income weight, fairness weight and in-station resource coordination weight.

7. The intelligent charging station system according to claim 5, characterized in that: In the dynamic priority scheduling algorithm module, the charging power affects at least one of the total available power upper limit of the charging pile, grid constraints and charging station energy storage status, and the preset scheduling algorithm is a linear programming algorithm and / or a heuristic algorithm.

8. The intelligent charging station system according to claim 5, characterized in that: The power allocation scheme generated by the dynamic priority scheduling algorithm module includes at least one of increasing / decreasing the charging power of a specific vehicle, pausing / resuming the charging state of a specific vehicle, starting / stopping energy storage charging or discharging, and invoking a V2G service; The control instructions generated by the control instruction generation and issuance module include power allocation instructions and / or switch control instructions, and the issuance targets of the control instructions include at least one of the charging pile controller, the energy storage management system and the V2G controller.

9. The intelligent charging station system according to claim 1, characterized in that: The network layer also includes: A user service and interaction module, configured to push at least one of charging status, estimated completion time, cost information, value-added services, and V2G participation invitation and revenue estimate to the user's app / terminal; An operations management module, configured to provide at least one of equipment status monitoring, fault warning, energy efficiency analysis, revenue reporting, and user behavior analysis; The V2G / V2X management module is used to coordinate connected vehicles that meet preset conditions to send power back to the power grid or charging station, manage the charging and discharging process, and calculate and settle the profits.

10. A charging method, using the intelligent charging station system according to any one of claims 1 to 9, characterized in that: The method comprises: Real-time collection of multi-source data; Performing fusion analysis on the multi-source data to predict charging indicators; The charging priority of the connected vehicle is calculated according to the charging index and the preset weight factor, so as to generate and issue a power allocation plan based on the charging priority and charging power impact data through a preset scheduling algorithm.

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