Electric vehicle charging system

Abstract

The invention provides an electric vehicle charging system. The electric vehicle charging system comprises a parking space level storage and charging unit; the parking space level storage and charging unit is used for being in communication connection with a community direct-current micro-grid, and the community direct-current micro-grid is in communication connection with a cloud platform; the parking space level storage and charging unit comprises a plurality of parking space level energy storage units and a plurality of parking space level charging units, and the parking space level energy storage units and the parking space level charging units are connected through a community direct current micro-grid and are both used for charging electric automobiles; the community direct current micro-grid is used for uniformly scheduling charging and discharging of the parking space level storage and charging units, and the cloud platform is used for uniformly monitoring and managing and formulating a charging and discharging scheduling strategy. According to the invention, the defect that a current charging system cannot adapt to a charging scene with limited space and insufficient power capacity is overcome.

Description

Technical Field

[0001] This invention relates to the field of charging technology, and in particular to an electric vehicle charging system. Background Technology

[0002] With the continuous growth of electric vehicle ownership, the charging demand in residential areas and other regions is becoming increasingly prominent, and the problem of charging difficulties is becoming more and more prominent. At present, many residential areas are facing insufficient power capacity and extremely limited space for equipment installation. They are unable to install charging piles with sufficient power to meet the charging needs of a large number of electric vehicles, and they also lack space that meets safety regulations, making it difficult to implement centralized energy storage solutions. This results in inconvenience for electric vehicle users to charge, and the contradiction between the supply and demand of charging services is becoming increasingly serious.

[0003] To address these issues, related technologies have attempted to optimize charging services by constructing integrated energy storage and charging systems or DC microgrid architectures. However, existing solutions still have many shortcomings. Regarding energy storage and charging systems, traditional centralized energy storage solutions require significant installation space, making them unsuitable for space-constrained residential communities. Distributed energy storage and charging units, lacking effective coordinated scheduling, require large-capacity energy storage batteries to guarantee charging needs, resulting in high system costs and hindering large-scale deployment. In terms of DC microgrid operation and control, when multiple power sources supply multiple loads, traditional power allocation methods either employ an average allocation strategy, which is prone to overload and system failure due to differences in power source performance and status; or they rely on a central control unit to respond to load power requests and allocate power. This approach suffers from delayed power response, poor anti-interference capabilities, unsuitability for large-scale systems, and insufficient dynamic adaptability, failing to meet the real-time power supply needs of dynamically changing loads during electric vehicle charging.

[0004] Meanwhile, most existing charging systems focus only on meeting basic charging functions, failing to fully utilize energy storage resources and the idle capacity of electric vehicle batteries. They also lack effective linkage mechanisms with the electricity market, making it impossible to improve system efficiency through peak-valley price arbitrage or responding to grid peak-shaving and frequency regulation needs. Furthermore, they struggle to alleviate the pressure caused by grid supply-demand imbalances. In addition, some systems suffer from low conversion efficiency, complex structures, inconvenient installation and maintenance, and poor scalability, further limiting their application and promotion in scenarios such as residential communities.

[0005] Therefore, there is an urgent need for an electric vehicle charging system that can adapt to scenarios with limited space and insufficient power capacity, while taking into account charging convenience, low system cost and high returns, and stable operation and timely response, in order to solve the many drawbacks of existing technologies. Summary of the Invention

[0006] The main objective of this invention is to provide an electric vehicle charging system that overcomes the shortcomings of current charging systems that cannot adapt to charging scenarios with limited space and insufficient power capacity.

[0007] To achieve the above objectives, the present invention provides an electric vehicle charging system, including a parking space-level energy storage and charging unit; the parking space-level energy storage and charging unit is used for communication connection with a community DC microgrid, and the community DC microgrid is used for communication connection with a cloud platform; The parking space-level energy storage and charging unit includes multiple parking space-level energy storage units and multiple parking space-level charging units. The parking space-level energy storage units and parking space-level charging units are connected through a community DC microgrid and are all used to charge electric vehicles. The community DC microgrid is used for unified scheduling of charging and discharging of parking space-level energy storage and charging units, and the cloud platform is used for unified monitoring and management and the formulation of charging and discharging scheduling strategies.

[0008] Furthermore, the connection relationship between the parking space-level energy storage unit and the parking space-level charging unit is one-to-one, many-to-one, one-to-many, or many-to-many.

[0009] Furthermore, the parking space-level energy storage unit includes a battery pack, a first DC / DC conversion circuit, a first control circuit, and a first communication circuit; the parking space-level charging unit includes a second DC / DC conversion circuit, a charging circuit, a second control circuit, and a second communication circuit.

[0010] Furthermore, both the parking space-level energy storage unit and the parking space-level charging unit adopt a pure DC solution.

[0011] Furthermore, the parking space-level storage and charging unit is installed in each parking space of the community parking lot.

[0012] Furthermore, the community DC microgrid includes a centralized inverter, a microgrid control subsystem, a DC power transmission and distribution line subsystem, and a communication subsystem; The centralized inverter is used to realize bidirectional conversion between AC power from the power grid or photovoltaic / wind power generation and DC power within the microgrid; The microgrid control subsystem communicates with the centralized inverter, parking space-level energy storage unit, and parking space-level charging unit through the communication subsystem, and connects to the cloud platform through the network to receive cloud platform instructions and perform charging and discharging management.

[0013] Furthermore, the microgrid control subsystem collects the power data of each parking space-level energy storage unit through the communication subsystem. When the power of the parking space-level energy storage unit in the target parking space is insufficient, it schedules the power of the parking space-level energy storage units in other parking spaces to charge the electric vehicle in the target parking space.

[0014] Furthermore, the community DC microgrid includes an active power control module, which is communicatively connected to each parking space-level energy storage unit and parking space-level charging unit; The active power control module collects the status and performance data of each parking space-level energy storage unit, and issues commands to each parking space-level energy storage unit regarding its working status, maximum operating power output, and specified output voltage. Each parking space-level energy storage unit adjusts its output according to the commands to respond to the load's power demand.

[0015] Furthermore, the status and performance data of the parking space-level energy storage unit include whether the parking space-level energy storage unit is available, the maximum power supply, the continuous power supply time, the optimal operating power, whether it is an energy storage power source, the battery capacity, and the SOH parameter.

[0016] Furthermore, the parking space-level energy storage unit can be charged using the power grid, photovoltaic, or wind power generation when the grid load is low and the electricity price is low; when the grid capacity is insufficient or the electricity price is high, it can supply power to electric vehicles that need charging.

[0017] Furthermore, the community's DC microgrid is communicatively connected to an external power data acquisition device, and the cloud platform receives peak-shaving and frequency regulation demand instructions and price data from the power market, realizing peak-valley price arbitrage and grid peak-shaving and frequency regulation response through a benefit optimization model.

[0018] The cloud platform layer collects electric vehicle charging data, community load data, and electricity market price data. It uses AI technology for data analysis and prediction, formulates charging and discharging scheduling strategies, and aggregates power resources, energy storage resources, and electric vehicle battery resources within the system to participate in electricity market transactions.

[0019] The electric vehicle charging system provided by this invention includes a parking space-level energy storage and charging unit. The parking space-level energy storage and charging unit is used for communication connection with a community DC microgrid, and the community DC microgrid is communicated with a cloud platform. The parking space-level energy storage and charging unit includes multiple parking space-level energy storage units and multiple parking space-level charging units. The parking space-level energy storage units and parking space-level charging units are connected through the community DC microgrid and are all used for charging electric vehicles. The community DC microgrid is used for unified scheduling of the charging and discharging of the parking space-level energy storage and charging units, and the cloud platform is used for unified monitoring and management and the formulation of charging and discharging scheduling strategies. In this invention, the parking space-level energy storage and charging unit includes multiple parking space-level energy storage units and multiple parking space-level charging units. The parking space-level energy storage units and parking space-level charging units are connected through the community DC microgrid and are all used for charging electric vehicles. In charging scenarios with limited space and insufficient power capacity, it can ensure the normal and effective charging of electric vehicles, overcoming the shortcomings of current charging systems that cannot adapt to charging scenarios with limited space and insufficient power capacity. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of an electric vehicle charging system application in one embodiment of the present invention; Figure 2This is a schematic block diagram of the structure of a parking space-level storage and charging unit in one embodiment of the present invention.

[0021] The implementation, functional features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] Reference Figure 1 as well as Figure 2 One embodiment of the present invention provides an electric vehicle charging system, including a parking space-level energy storage and charging unit; the parking space-level energy storage and charging unit is used for communication connection with a community DC microgrid, and the community DC microgrid is used for communication connection with a cloud platform; The parking space-level energy storage and charging unit includes multiple parking space-level energy storage units and multiple parking space-level charging units. The parking space-level energy storage units and parking space-level charging units are connected through a community DC microgrid and are all used to charge electric vehicles. The community DC microgrid is used for unified scheduling of charging and discharging of parking space-level energy storage and charging units, and the cloud platform is used for unified monitoring and management and the formulation of charging and discharging scheduling strategies.

[0024] In this embodiment, the electric vehicle charging system adopts a hierarchical collaborative architecture design to achieve spatial adaptability, power supply and demand balance and overall efficiency optimization. Stable connections are established between each level through standardized communication protocols to ensure the accuracy of data transmission and command execution. Among them, the parking space-level energy storage and charging unit, as the terminal execution carrier of the system, is directly deployed in each parking space of the residential community parking lot without occupying additional ground space, and can adapt to scenarios with limited installation space. The unit consists of multiple parking space-level energy storage units and multiple parking space-level charging units. The two are connected flexibly through the community DC microgrid, including one-to-one, many-to-one, one-to-many, or many-to-many configuration modes. It can be dynamically adjusted according to actual charging demand and energy storage resource distribution to achieve efficient resource utilization. The community DC microgrid, as the regional dispatch core, undertakes the unified coordination and management functions of the charging and discharging behavior of the parking space-level energy storage and charging units. By integrating the energy storage resources and charging demand in the region, it constructs a local power distribution network. The cloud platform, as the global control center, realizes data interaction and command issuance with the community DC microgrid through network communication. It is responsible for real-time monitoring of the status of all system equipment, summarizing and analyzing various operating data, and formulating and optimizing charging and discharging scheduling strategies, forming a closed-loop operation system of terminal execution-regional dispatch-global control.

[0025] When the grid load is low, power capacity is sufficient, and electricity prices are low, parking space-level energy storage units connect to external power sources (including AC power from the grid, photovoltaic power generation systems, or wind power generation systems) through the community's DC microgrid to initiate charging mode. During this process, the community's DC microgrid converts the external AC power into DC power suitable for the microgrid through a centralized inverter, and then transmits it to each parking space-level energy storage unit via the DC power transmission and distribution line subsystem. The DC / DC conversion circuit inside the energy storage unit converts the input power into voltage and current parameters suitable for the battery pack, completing energy storage under the precise control of the control circuit. This achieves efficient storage of low-cost energy, providing energy support for charging needs during subsequent peak periods, while avoiding the waste of energy during grid off-peak hours.

[0026] When the grid experiences peak load, insufficient power capacity, or high electricity prices, and electric vehicles request charging, the system activates the energy storage power supply mode. First, the microgrid control subsystem of the community's DC microgrid collects real-time data on the power status of each parking space-level energy storage unit, the connection status of each charging unit, and the charging power demand of electric vehicles through the communication subsystem. Based on preset scheduling rules, it dynamically allocates energy storage resources within the area. If the energy storage unit corresponding to the target charging parking space has sufficient power, it directly supplies power to the corresponding charging unit through the DC microgrid. After processing by the charging unit's internal conversion and charging circuits, it provides standard charging services to electric vehicles. If the energy storage unit corresponding to the target charging parking space has insufficient power, the microgrid control subsystem triggers a cross-parking space power scheduling mechanism, dispatching the remaining power from energy storage units in other idle parking spaces to the target charging unit through the DC transmission and distribution line subsystem. This ensures that the charging needs of electric vehicles are met promptly, without relying on grid expansion, effectively solving the charging problem in scenarios with insufficient power capacity.

[0027] The cloud platform continuously collects operational data from the entire system, including charging behavior data such as charging time, charging power, and charging frequency of electric vehicles; load data such as public facilities and residential electricity consumption within residential communities; and market data such as real-time electricity prices, peak-shaving and frequency regulation demand instructions, and prices in the electricity market. Based on AI technology, the platform performs in-depth analysis and prediction of this data, including predictions of the spatiotemporal distribution of electric vehicle charging demand, peak and valley values ​​of community electricity load, fluctuation trends in electricity market prices, and the response potential of grid peak-shaving and frequency regulation demands. While ensuring that all electric vehicle charging needs are prioritized, the cloud platform formulates optimal charging and discharging scheduling strategies, clarifying the charging and discharging timing, power allocation ratios, and participation methods in electricity market transactions for each community's DC microgrid, each parking space-level energy storage and charging unit, and the connected electric vehicle batteries. Subsequently, the cloud platform issues scheduling strategy instructions to the corresponding community DC microgrid, which are then broken down into specific execution instructions by the microgrid control subsystem and issued to each parking space-level energy storage unit and charging unit, achieving precise control over the charging and discharging behavior of the entire system. Meanwhile, the cloud platform aggregates idle energy storage resources and electric vehicle battery resources within the system through the power trading subsystem, and participates in power market businesses such as virtual power plant trading, peak-valley price arbitrage, and grid peak-shaving and frequency regulation response. While alleviating the pressure of power grid supply and demand imbalance, it creates additional economic benefits for the system and realizes diversified benefits of linking charging services with the power market.

[0028] In one embodiment, the connection relationship between the parking space-level energy storage unit and the parking space-level charging unit is one-to-one, many-to-one, one-to-many, or many-to-many.

[0029] In this embodiment, a one-to-one connection is established, meaning that one parking space-level energy storage unit uniquely corresponds to one parking space-level charging unit, and the two form a dedicated power supply link through the community's DC microgrid. In this mode, the energy from the energy storage unit is prioritized for supplying the corresponding charging unit. If its own power is sufficient, it can directly charge electric vehicles connected to that charging unit; if its power is insufficient, it can supplement the power supply by scheduling energy from other energy storage units through the microgrid. This is suitable for scenarios where the charging demand of a single parking space is relatively stable and the energy storage resources and charging points are fixedly matched, such as the charging configuration of a dedicated fixed parking space, which can ensure the independent power supply reliability of a single parking space.

[0030] A many-to-one connection means that multiple parking space-level energy storage units share a single parking space-level charging unit, with these units connected in parallel to the same charging unit via a DC microgrid. This mode is primarily suitable for charging units with high power demands (such as high-power fast charging) or frequent charging. By aggregating the energy from multiple energy storage units, it provides sufficient power support for a single charging unit, avoiding the decrease in charging efficiency caused by insufficient capacity or power of a single energy storage unit. For example, three 8kWh / 4kW energy storage units working together can continuously supply power to a 10kW charging unit, meeting the power requirements for fast charging of electric vehicles.

[0031] One-to-many connection means that one parking space-level energy storage unit simultaneously corresponds to multiple parking space-level charging units. A single energy storage unit supplies power to multiple charging units in a time-sharing or power-sharing manner through the power distribution mechanism of the DC microgrid. This mode is suitable for scenarios where the charging needs of multiple parking spaces are dispersed and peak loads do not overlap, such as when some parking spaces in a residential community are only charged sporadically at night. Through the rational allocation of power from the energy storage units, the basic charging needs of multiple parking spaces can be met while avoiding idle energy storage resources, improving the utilization rate of individual energy storage units, and reducing the overall system configuration cost.

[0032] Many-to-many connections involve multiple parking space-level energy storage units forming a mesh network with multiple parking space-level charging units. The electrical energy from all storage units is aggregated into a shared energy pool via the community's DC microgrid. All charging units can draw power from this pool as needed. This mode offers the most flexible configuration. The microgrid control subsystem can dynamically adjust the power distribution path and ratio based on the power status of each storage unit and the real-time power demand of each charging unit. For example, when storage units A and B have sufficient power, while charging units C and D simultaneously require charging, the microgrid can schedule A to supply power to C, B to D, or A and B to jointly supply power to C, which has a higher power demand. This maximizes adaptability to the dynamic changes in energy storage resources and charging demand, making it particularly suitable for residential parking lots with fluctuating charging demand and uneven parking space usage.

[0033] All four connection relationships rely on the unified scheduling and power transmission capabilities of the community's DC microgrid. They do not require changes to the physical layout of the hardware and can be switched by simply adjusting the control logic, thus taking into account the system's flexibility, scalability, and resource utilization efficiency.

[0034] In one embodiment, the parking space-level energy storage unit includes a battery pack, a first DC / DC conversion circuit, a first control circuit, and a first communication circuit; the parking space-level charging unit includes a second DC / DC conversion circuit, a charging circuit, a second control circuit, and a second communication circuit.

[0035] In this embodiment, the circuit and functional module configuration of the parking space-level energy storage unit and the parking space-level charging unit are based on the core principle of achieving efficient energy storage, precise conversion, and reliable control through specialized module division of labor, adapting to the collaborative operation requirements of DC microgrids, as detailed below: As the core carrier for storing and releasing electrical energy, the parking space-level energy storage unit achieves efficient energy management through the collaborative work of its various modules, as detailed below: Battery pack: As a physical medium for storing electrical energy, it is used to receive electrical energy from the grid, photovoltaic or wind power generation during off-peak hours and release electrical energy when charging demand arises. Its capacity and power parameters (such as 8kWh / 4kW) need to be adapted to the charging load of the community and the dispatching needs of the microgrid to provide a stable energy source for electric vehicle charging.

[0036] The first DC / DC conversion circuit is the core power conversion component, responsible for bidirectional voltage and current regulation. During the charging phase, it converts the DC power transmitted from the community's DC microgrid into voltage and current levels suitable for the battery pack, ensuring the safety and efficiency of the charging process. During the discharging phase, it converts the electrical energy output from the battery pack into electrical energy that conforms to the DC microgrid standard, so that it can be transmitted to the charging unit or participate in the electricity market transaction through the microgrid.

[0037] The first control circuit is the core management component for unit operation, with built-in preset control strategies and protection mechanisms. On one hand, it receives instructions (such as charging and discharging power, start and stop signals) from the cloud platform and microgrid control subsystem transmitted by the first communication circuit, and precisely regulates the charging and discharging state of the battery pack and the operating parameters of the first DC / DC conversion circuit. On the other hand, it monitors key data such as battery pack voltage, current, temperature, SOC (state of charge), and SOH (state of health) in real time. When abnormal conditions such as overload, overtemperature, overcharge / over-discharge occur, it immediately triggers shutdown or adjustment commands to ensure the safe operation of the unit.

[0038] The first communication circuit is responsible for the communication interface of data transmission and command interaction. Through standardized communication protocols (compatible with 4G / 5G / WIFI / Ethernet or LoRa communication), it enables bidirectional data interaction with the community's DC microgrid layer communication subsystem. It uploads data such as battery pack status and operating parameters to the microgrid control subsystem, while simultaneously receiving charging and discharging scheduling commands from the microgrid to ensure coordinated synchronization between the unit and the microgrid.

[0039] As the terminal component for both power output and charging execution, the parking space-level charging unit focuses on converting the electrical energy transmitted by the microgrid into charging parameters suitable for electric vehicles, ensuring the stability and compliance of the charging process, as detailed below: The second DC / DC conversion circuit is an energy adaptation and conversion component. Based on the electric vehicle's battery type, voltage level, and charging requirements, it converts the DC power (e.g., 700VDC / 500VDC) transmitted from the community's DC microgrid into a voltage and current range acceptable to the electric vehicle's battery. It also features power regulation to adapt to the charging power requirements of different vehicle models (e.g., supporting 10kW rated power output). In one embodiment, the second DC / DC conversion circuit can both convert energy from the DC microgrid to charge the electric vehicle and feed energy from the electric vehicle's battery back to the DC microgrid, a key characteristic of V2G. Through bidirectional energy conversion by the second DC / DC conversion circuit, with the electric vehicle owner's consent, excess power from the electric vehicle can be used to charge other electric vehicles via the DC microgrid. It also allows the electric vehicle's battery to participate in virtual power plant trading, including peak-valley arbitrage and demand-side response, through the cloud platform and the microgrid's scheduling, thereby helping electric vehicle owners obtain additional income.

[0040] Charging circuit: This is the functional circuit that directly performs the charging operation, including a charging interface (compatible with standard electric vehicle charging interfaces) and circuit protection devices (such as fuses and relays). Under the regulation of the second control circuit, the electrical energy processed by the second DC / DC conversion circuit is safely delivered to the electric vehicle battery. At the same time, the current, voltage, and connection status of the charging circuit are monitored in real time to ensure the continuity and safety of the charging process.

[0041] The second control circuit is the core of the charging unit's operation and management, forming a collaborative control logic with the first control circuit. It receives charging commands (such as charging power and charging duration) from the microgrid control subsystem via the second communication circuit, and, combined with charging demand signals from the electric vehicle, adjusts the output parameters of the second DC / DC conversion circuit and the on / off state of the charging circuit. Simultaneously, it monitors abnormal conditions during the charging process (such as loose interfaces, overcurrent, and overvoltage), promptly triggering interrupt or protection mechanisms to prevent equipment damage or safety risks.

[0042] The second communication circuit undertakes multi-directional communication functions. On the one hand, it connects to the communication subsystem of the community's DC microgrid to upload the operating status of the charging unit and charging data (such as charging power and amount of charge), and receive scheduling instructions issued by the microgrid. On the other hand, it can communicate with connected electric vehicles to obtain information such as vehicle battery status and charging needs, so as to achieve precise adaptation and collaborative control of the charging process.

[0043] Both types of units are configured to achieve coordinated operation with the community DC microgrid and cloud platform through precise control of their respective control circuits and real-time interaction with communication circuits. At the same time, the pure DC architecture module design (without additional AC / DC conversion links) significantly improves power conversion efficiency, reduces system losses and costs, and provides hardware support for the realization of V2G mode, enabling electric vehicle batteries to participate in electricity market transactions through charging units and microgrids.

[0044] In one embodiment, the battery pack has a capacity of 8kWh and a rated power of 4kW; the parking space-level charging unit has a rated power of 10kW. It is understood that in this embodiment, the specific values ​​of the battery pack capacity, rated power, and rated power of the charging unit are only illustrative examples and are not specifically limited. Their specific values ​​can be set according to specific actual needs.

[0045] Both the parking space-level energy storage unit and the parking space-level charging unit adopt a pure DC solution.

[0046] In one embodiment, the parking space-level storage and charging unit is installed in each parking space of the community parking lot.

[0047] In this embodiment, the design of installing the parking space-level energy storage and charging unit in each parking space of the community parking lot is based on core technical considerations of scenario adaptability, space utilization, and charging convenience. Its installation logic and technical advantages are as follows: This installation method adopts an integrated parking space deployment solution, directly integrating parking space-level energy storage units and parking space-level charging units or placing them nearby in designated areas of each parking space (such as the side of the parking space, under the ground, or next to the charging pile). It does not require additional occupation of public passages, green areas, or other ground space in the community parking lot, perfectly addressing the pain point of extremely limited installation space in residential communities. From an installation perspective, the unit structure design balances lightweight and modularity, allowing for flexible adjustment of the installation position according to the size of the parking space. It also eliminates the need for large-scale modifications to the existing parking lot (such as eliminating the need to excavate large cable trenches or build additional equipment rooms). Installation and fixation are completed only through standardized fasteners and connected to the DC power transmission and distribution lines and communication lines of the community's DC microgrid. The installation process is simple, the construction period is short, and batch deployment of parking spaces throughout the community can be completed quickly.

[0048] From a functional perspective, each parking space corresponds to an independent energy storage and charging unit (or a terminal interface sharing energy storage and charging resources). Electric vehicles can directly connect to the charging unit after entering the parking space, eliminating the need to search for charging equipment across parking spaces, greatly improving user convenience. Simultaneously, the distributed installation method ensures precise matching of energy storage resources with charging demand. Each parking space's energy storage and charging unit can independently respond to its own charging needs, or achieve cross-parking space resource scheduling through a DC microgrid. This ensures the charging reliability of individual parking spaces while achieving balanced utilization of energy storage resources throughout the entire community. Furthermore, this installation method has excellent scalability. If new parking spaces are added to the community or charging demand is upgraded, the same specifications of energy storage and charging units can be directly installed in the new parking spaces and connected to the existing DC microgrid without requiring significant modifications to the original system, reducing the cost and difficulty of system expansion.

[0049] The above installation design fundamentally solves the problems of large space occupation of centralized energy storage solutions and the limitations of power capacity and space on charging pile installation. At the same time, it provides a hardware deployment foundation for the implementation of the three-layer architecture of parking space-level energy storage and charging unit-community DC microgrid-cloud platform, enabling the realization of the technical concept of distributed energy storage and charging integration.

[0050] In one embodiment, the community DC microgrid includes a centralized inverter, a microgrid control subsystem, a DC transmission and distribution line subsystem, and a communication subsystem; The centralized inverter is used to realize bidirectional conversion between AC power from the power grid or photovoltaic / wind power generation and DC power within the microgrid; The microgrid control subsystem communicates with the centralized inverter, parking space-level energy storage unit, and parking space-level charging unit through the communication subsystem, and connects to the cloud platform through the network to receive cloud platform instructions and perform charging and discharging management.

[0051] In this embodiment, the centralized inverter is a key device for realizing bidirectional power conversion between the system and external power sources and the power grid, undertaking the responsibility of AC-DC and DC-AC bidirectional conversion. On the one hand, when connected to external power sources such as grid AC, photovoltaic power generation, or wind power, it converts the external AC power into DC power (such as 700VDC / 500VDC) that conforms to the community's DC microgrid standard, charging parking space-level energy storage units or directly supplying parking space-level charging units, ensuring the power input of the DC microgrid. On the other hand, when participating in electricity market transactions (such as V2G mode, peak shaving and frequency regulation response), it can convert the DC power within the microgrid (power from parking space-level energy storage units or electric vehicle battery feedback) into AC power and transmit it to the external power grid, realizing reverse power output and value realization. Compared with distributed inverters, the centralized design is easier to manage uniformly, improves power conversion efficiency, and reduces system operation and maintenance costs.

[0052] The microgrid control subsystem is the brain of the community's DC microgrid, possessing core capabilities such as data acquisition, command parsing, strategy execution, and security management. It establishes real-time communication links with the centralized inverter, all parking space-level energy storage units, and all parking space-level charging units through the communication subsystem. On one hand, it comprehensively collects operational data from each device, including the conversion efficiency and operating status of the centralized inverter, the SOC, SOH, voltage and current parameters of the parking space-level energy storage units, and the connection status and charging power requirements of the parking space-level charging units. On the other hand, it establishes bidirectional interaction with the cloud platform through networks such as 4G / 5G / WIFI / Ethernet, receiving global scheduling strategies (such as charging and discharging power thresholds, electricity price response commands, and peak shaving and frequency regulation requirements) issued by the cloud platform. Based on the collected device status data and cloud platform instructions, the microgrid control subsystem formulates a precise charging and discharging management plan for the area and issues specific execution instructions to each device: such as controlling the start-up, shutdown and switching modes of the centralized inverter, regulating the charging and discharging timing and power of the parking space-level energy storage unit, coordinating the power distribution of the charging unit, and monitoring the system operation status in real time. When overload, voltage abnormality, equipment failure or other situations occur, the protection mechanism is immediately triggered to ensure the safe and stable operation of the regional microgrid.

[0053] The DC power transmission and distribution line subsystem consists of standardized DC cables, terminals, and protective devices (such as DC circuit breakers and surge protection modules), forming a DC power transmission network covering the entire parking lot of the community. Its core function is to accurately deliver the DC power output from the centralized inverter to each parking space-level energy storage unit and charging unit, while also providing a transmission channel for cross-parking space power dispatch. When the power of an energy storage unit in a parking space is insufficient, the subsystem efficiently transmits power from other parking space energy storage units to the target charging unit, achieving shared allocation of energy storage resources. The line design adheres to low-loss and high-safety standards, adapts to the voltage level and power requirements within the microgrid, ensures the stability and reliability of power transmission, and possesses good scalability, allowing for flexible expansion of transmission links as parking spaces are added or equipment is upgraded.

[0054] The communication subsystem adopts a standardized communication protocol, integrating wired and wireless communication resources to provide a stable channel for information exchange between devices within the microgrid and between the microgrid and the cloud platform. On one hand, it enables local communication between the microgrid control subsystem and centralized inverters, parking space-level energy storage units, and parking space-level charging units, ensuring real-time uploading of device status data and immediate issuance of control commands. On the other hand, it serves as the communication interface between the microgrid and the cloud platform, enabling bidirectional transmission of regional operational data (such as charging load, energy storage capacity, and energy conversion) and global commands from the cloud platform via network links. This subsystem features strong anti-interference capabilities, low transmission latency, and good compatibility, adapting to the communication needs of different devices and ensuring the coordinated and synchronous operation of the entire microgrid system.

[0055] Centralized inverters complete the conversion of electrical energy form, providing stable power input or output for the microgrid; the DC transmission and distribution line subsystem establishes power transmission channels, ensuring efficient power flow between various devices; the communication subsystem realizes full-link data interaction, ensuring accurate and timely information transmission; the microgrid control subsystem, as the core hub, integrates device status data and cloud platform instructions, coordinates and schedules the operating status of various devices, and ultimately achieves optimized allocation of energy storage resources within the region, timely response to charging needs, and efficient linkage with the cloud platform, providing core support for the overall low-cost and high-efficiency operation of the system.

[0056] In one embodiment, the microgrid control subsystem collects power data of each parking space-level energy storage unit through the communication subsystem. When the power of the parking space-level energy storage unit in the target parking space is insufficient, it schedules the power of the parking space-level energy storage units in other parking spaces to charge the electric vehicle in the target parking space.

[0057] In this embodiment, the microgrid control subsystem relies on the communication subsystem to construct a full-area energy storage status monitoring network. Through standardized communication protocols (compatible with LoRa, Ethernet, etc.), it collects real-time, high-frequency power data from all parking space-level energy storage units. The core data collected includes the SOC (State of Charge) of each energy storage unit, its output power, and its operating status (idle / charging / discharging), ensuring accurate understanding of the distribution of energy storage resources within the area. This data, transmitted from the communication subsystem to the microgrid control subsystem, is updated in real-time to the regional energy storage resource ledger, providing data support for scheduling decisions.

[0058] When an electric vehicle in the target parking space connects to the charging unit and initiates a charging request, the microgrid control subsystem first retrieves the real-time power data of the corresponding energy storage unit and compares it with a preset charging demand threshold (determined based on the electric vehicle's declared charging power and expected charging amount). If the energy storage unit's SOC is higher than the threshold and its output power meets the charging demand, a direct command is issued to control the energy storage unit to deliver power to the charging unit in the target parking space through the DC power transmission and distribution line subsystem, initiating the charging process. If the comparison reveals that the energy storage unit's power is insufficient (SOC is lower than the threshold) or its output power cannot match the charging demand, the system triggers a cross-parking space scheduling mechanism.

[0059] During the cross-parking space scheduling phase, the microgrid control subsystem, based on the regional energy storage resource ledger, selects other parking space-level energy storage units (single or multiple) that are idle and whose State of Charge (SOC) meets the scheduling requirements. It then calculates the total amount of electricity and power allocation ratio to be scheduled based on charging demand. Subsequently, the microgrid control subsystem issues discharge commands to the selected energy storage units via the communication subsystem, while simultaneously controlling the DC power transmission and distribution line subsystem to switch the power transmission path, converging the electricity from these energy storage units to the charging unit in the target parking space. Throughout this process, the microgrid control subsystem monitors the voltage and current parameters of the power transmission in real time, as well as the receiving status of the target charging unit, dynamically adjusting the output power of each scheduled energy storage unit to ensure stable power transmission, continuous charging, and that the entire scheduling process does not affect the normal charging needs of other parking spaces.

[0060] The core advantage of the above scheduling logic is that it can meet the charging needs without configuring a large-capacity energy storage unit for each parking space, and significantly reduce the overall system cost through resource sharing. At the same time, relying on real-time data collection and dynamic scheduling, it avoids charging interruptions caused by insufficient power of a single energy storage unit, ensuring the reliability of the charging service and perfectly adapting to the characteristics of the dispersed and fluctuating charging needs of residential communities.

[0061] In one embodiment, the community DC microgrid includes an active power control module, which is communicatively connected to each parking space-level energy storage unit and parking space-level charging unit. The active power control module collects the status and performance data of each parking space-level energy storage unit, and issues commands to each parking space-level energy storage unit regarding its working status, maximum operating power output, and specified output voltage. Each parking space-level energy storage unit adjusts its output according to the commands to respond to the load's power demand.

[0062] In this embodiment, the active power control module is the core component of the community DC microgrid that solves the problems of traditional power distribution delay and poor anti-interference. Through the control logic of active data acquisition, instruction preset, and autonomous response, it realizes the immediate satisfaction of load power demand and stable system operation, as detailed below: As an independent power management core, the active power control module establishes a two-way communication link with all parking space-level energy storage units (power supply units) and parking space-level charging units (load units) through the communication subsystem. On the one hand, it has comprehensive data acquisition capabilities, which can obtain the status and performance core data of each parking space-level energy storage unit in real time, including whether the equipment is available, maximum power supply, continuous power supply time, optimal operating power, whether it is an energy storage power supply, battery capacity, and SOH (state of health) and other key parameters, to ensure accurate control of the power supply side resource capabilities. On the other hand, it directly connects to the parking space-level charging unit to sense changes in the load side's power demand in real time (such as the connection status of the charging unit, power demand fluctuations, etc.).

[0063] Based on the collected power supply data and load demand, the active power control module issues precise control commands to each parking space-level energy storage unit according to preset optimization strategies (such as avoiding power overload, improving power supply efficiency, and adapting to load demand). The commands clearly include three core parameters: first, the working status command (start / stop / standby), which clarifies whether the energy storage unit participates in power supply; second, the maximum operating power output command, which limits the upper limit of the energy storage unit's output power to prevent it from exceeding its own performance limits and causing failure; and third, the specified output voltage command, which ensures that the output voltage of the energy storage unit is consistent with the DC microgrid bus voltage and the charging unit's adaptation voltage, thus ensuring the compatibility and safety of power transmission.

[0064] After receiving instructions, each parking space-level energy storage unit autonomously adjusts its output through its own first control circuit and first DC / DC conversion circuit: the control circuit analyzes the instruction parameters and adjusts the working mode of the energy storage unit, while the DC / DC conversion circuit precisely adjusts the voltage and current levels of the power output according to the specified output voltage and maximum operating power. When the parking space-level charging unit (load) has a power demand, it does not need to apply to the control module. The energy storage unit, which has received the preset instructions, can directly respond instantly based on its self-adjusted output state and deliver suitable power to the charging unit through the DC power transmission and distribution line subsystem, achieving zero-delay fulfillment of load demand.

[0065] The core advantages of the above control logic are: by actively pre-setting instructions to replace the traditional mode of load application-central control allocation, the response latency is greatly reduced; frequent communication interactions are reduced, improving the system's anti-interference capability and scalability; at the same time, by clearly defining the upper limit of power output and voltage standards, the overload operation of individual energy storage units is avoided, ensuring the overall stability of the system and perfectly adapting to the scenario requirements of dynamic changes in charging load in residential communities.

[0066] In one embodiment, the status and performance data of the parking space-level energy storage unit includes whether the parking space-level energy storage unit is available, the maximum power supply, the continuous power supply time, the optimal operating power, whether it is an energy storage power source, the battery capacity, and the SOH parameter.

[0067] In one embodiment, the parking space-level energy storage unit is charged using the power grid, photovoltaic, or wind power generation when the grid load is low and the electricity price is low; when the grid capacity is insufficient or the electricity price is high, it supplies power to electric vehicles that need charging.

[0068] In this embodiment, when the power grid is in a low-load period (such as the early morning hours at night), the grid has sufficient power capacity, low supply pressure, and the electricity market price is in a low range. The parking space-level energy storage unit will then initiate charging mode. In this mode, the microgrid control subsystem receives electricity price data and grid load status instructions from the cloud platform through the communication subsystem. After determining that the charging conditions are met, it issues a charging instruction to the parking space-level energy storage unit. The parking space-level energy storage unit connects to an external power source (which can be grid AC, photovoltaic power, or wind power) through the DC transmission and distribution line subsystem of the community's DC microgrid. After AC-DC conversion by a centralized inverter (no conversion is needed if DC power such as photovoltaic / wind power is connected), its own first DC / DC conversion circuit adjusts the electrical energy to voltage and current parameters suitable for the battery pack. Under the safe management of the first control circuit, it efficiently stores low-cost electrical energy in the battery pack, reserving energy for subsequent peak-hour power demand, while avoiding energy waste during grid off-peak periods and improving energy utilization.

[0069] When the power grid enters peak load periods (such as the concentrated charging period after daytime commutes), and the grid's power capacity is strained, supply pressure is high, or electricity market prices are high, and electric vehicles connect to parking space-level charging units and initiate charging demands, the parking space-level energy storage unit will activate its discharge power supply mode. At this time, the microgrid control subsystem, combining grid capacity data, electricity price data, and charging demand signals, issues a discharge command to the parking space-level energy storage unit. The energy storage unit's battery pack releases the stored electrical energy, which is adjusted by the first DC / DC conversion circuit to meet the voltage and power levels required by the community's DC microgrid standards. This energy is then transmitted to the charging unit at the target parking space via the DC transmission and distribution line subsystem. After processing by the charging unit's second DC / DC conversion circuit and charging circuit, it provides a stable charging service for electric vehicles. This process does not rely on additional power from the grid, avoiding the charging difficulties caused by insufficient grid capacity. Furthermore, by using low-cost energy stored during off-peak hours, it reduces the charging cost of electric vehicles and alleviates the power supply pressure on the grid during peak hours, achieving a win-win situation for both system efficiency and grid security.

[0070] The entire charging and discharging switching process is automatically triggered by the microgrid control subsystem based on the grid status, electricity price data and real-time charging demand issued by the cloud platform, without the need for manual intervention. Furthermore, through the hardware module collaboration of the parking space-level energy storage unit and the unified scheduling of the microgrid, the stability of the charging and discharging conversion and the efficiency of power transmission are ensured, adapting to the time distribution characteristics of charging demand in residential communities and the dynamic changes in grid operation.

[0071] In one embodiment, the community's DC microgrid is communicatively connected to an external power data acquisition device, and the cloud platform receives peak-shaving and frequency regulation demand instructions and price data from the power market, and realizes peak-valley price arbitrage and power grid peak-shaving and frequency regulation response through a benefit optimization model.

[0072] The cloud platform collects electric vehicle charging data, community load data, and electricity market price data. It uses AI technology for data analysis and prediction, formulates charging and discharging scheduling strategies, and aggregates power resources, energy storage resources, and electric vehicle battery resources within the system to participate in electricity market transactions.

[0073] In this embodiment, the community's DC microgrid connects to an external power data acquisition device via communication to obtain core grid-side data in real time, including grid load factor, power capacity margin, and power supply stability. The cloud platform simultaneously receives power market data, covering real-time electricity prices, peak-valley price ranges, peak-shaving and frequency regulation demand commands (such as peak shaving / valley filling requirements when grid supply and demand are imbalanced), and corresponding response prices (such as peak-shaving service subsidy standards). Simultaneously, the cloud platform comprehensively collects terminal-side data through the community's DC microgrid's communication subsystem: electric vehicle charging data (charging time, power, frequency, battery status, etc.), community load data (real-time load and fluctuation trends of residential and public facility electricity consumption), and system-wide energy storage resource data (SOC, SOH, and chargeable / dischargeable power of all parking space-level energy storage units). All data is aggregated to the cloud platform via stable network links such as 4G / 5G / WIFI / Ethernet, forming a comprehensive data foundation to support subsequent analysis and decision-making.

[0074] The cloud platform employs AI technology to perform in-depth processing of collected multi-source data: it profiles and predicts demand for electric vehicle charging behavior, accurately determining peak charging times and load distribution for different time periods and parking spaces; it predicts trends in the overall electricity load of the community, identifying peak and off-peak periods to avoid localized power supply pressure caused by the overlap of charging load and residential electricity load; it predicts electricity market price fluctuations and the intensity of peak-shaving and frequency regulation demand, seizing opportunities for arbitrage between peak and off-peak prices and grid synergy; and it combines grid operation status data to predict grid capacity redundancy, providing a basis for charging and discharging strategies to avoid grid overload risks.

[0075] Based on AI predictions, the cloud platform uses a built-in benefit optimization model subsystem to formulate globally optimal charging and discharging scheduling strategies: prioritizing electric vehicle charging needs to ensure the timeliness and reliability of charging services; considering electricity price fluctuations, scheduling parking space-level energy storage units to charge more during off-peak hours to store low-cost energy; scheduling energy storage units to supply power to electric vehicles during peak hours to reduce the consumption of high-priced electricity from the grid and achieve peak-valley arbitrage; responding to grid peak-shaving and frequency regulation needs, when the grid load is too high (peak shaving required), reducing energy storage units charging from the grid and increasing energy storage units supplying power to electric vehicles (or reverse transmission to the grid) to reduce grid pressure; when the grid load is too low (valley filling required), increasing the charging amount of energy storage units to absorb surplus grid energy while obtaining peak-shaving and frequency regulation service revenue.

[0076] The cloud platform leverages its resource aggregation capabilities to integrate dispersed power resources (photovoltaic / wind power connections), energy storage resources (all parking space-level energy storage units), and electric vehicle battery resources (vehicle batteries connected via V2G) into a "virtual power plant" for participation in the electricity market. Based on dispatch strategies, it uniformly submits dispatchable capacity and response capabilities (such as peak-shaving and frequency regulation potential) to the electricity market. When it receives peak-shaving and frequency regulation demand instructions from the electricity market, it issues precise execution instructions to each parking space-level energy storage and charging unit and connected vehicle through the community's DC microgrid, coordinating and adjusting the charging and discharging status to respond to grid demands and obtain transaction revenue. Throughout the transaction process, the cloud platform tracks market dynamics and execution effects in real time, dynamically optimizes dispatch strategies, ensures maximum system revenue, and strictly controls charging and discharging behavior to ensure that it does not exceed the grid's capacity limits, thus guaranteeing grid safety and stability. It breaks through the single function of traditional charging systems that "only meet the charging needs". Through data integration and AI empowerment, it realizes the multiple values ​​of charging services, grid collaboration and economic benefits: it not only solves the problem of charging difficulties in residential communities, but also alleviates grid pressure through peak shaving and valley filling, and can also create additional revenue for system operators through electricity market transactions, forming a win-win situation for users, operators and grid.

[0077] In one embodiment, the parking space-level energy storage unit integrates a self-sensing flexible heat dissipation and energy recovery module, which is used to: monitor the temperature gradient of each area of ​​the battery pack in real time through distributed fiber optic temperature sensors; when the local temperature exceeds the preset temperature, automatically activate the coordinated heat dissipation of the flexible heat pipe array and the micro magnetic levitation fan; at the same time, the waste heat generated during the heat dissipation process is converted into low-voltage electrical energy through a semiconductor cooling chip, which is boosted by the first DC / DC conversion circuit and fed back to the battery pack for energy replenishment. ≥2% of the electrical energy corresponding to the waste heat dissipation can be recovered in a single charging cycle, and the heat dissipation module adopts a flexible bonding design to adapt to the volume deformation of the battery pack during charging and discharging, avoiding heat dissipation failure caused by mechanical stress.

[0078] In one embodiment, the parking space-level charging unit is equipped with a dual-mode adaptive switching module for contactless charging and physical charging; The dual-mode adaptive switching module has a built-in millimeter-wave radar and magnetic field coupling detection unit. When the electric vehicle drives into the parking space, it automatically identifies whether the vehicle supports wireless charging and the location of the charging interface. If it supports wireless charging and the coupling distance is ≤15cm, it switches to non-contact charging mode and achieves 10kW power wireless transmission (transmission efficiency ≥92%) through resonant magnetic field coupling. If the vehicle does not support wireless charging or the coupling conditions are not met, the robotic arm will be automatically triggered to precisely dock with the vehicle's charging interface and switch to physical charging mode. The dual-mode switching process requires no user operation, and the module has a built-in foreign object detection unit. When there is a metal foreign object in the wireless charging area, the magnetic field output is immediately cut off and the module switches to physical charging mode first to eliminate safety hazards.

[0079] In one embodiment, the cloud platform is further used for: Based on the full life cycle operation data of the parking space-level energy storage unit, a corresponding battery performance degradation curve is constructed; The relative positions of IoT sensor nodes of each communication node, energy storage unit, and charging unit in the community DC microgrid are obtained, and the relative positions are simulated as a spatial topology graph. A preset power data base array is obtained, and the data dimension of the power data base array is mutated based on the node connection relationship and distance weight of the spatial topology graph to obtain a first power data array. Based on the characteristic inflection point and trend parameters of the battery performance degradation curve, the weight optimization and feature enhancement of local data are performed on the first power data array to obtain the second power data array. Based on the dimensional information and data type characteristics of the second power data array and the data to be encrypted, a dynamic encryption key is generated to perform layered encryption processing on the data to be encrypted. The data to be encrypted includes dispatch command data between the cloud platform and the community DC microgrid, core operating parameters of each parking space-level storage and charging unit, interactive data of power market transactions, and charging privacy data of electric vehicle users.

[0080] In this embodiment, the cloud platform first collects full-lifecycle operational data for all parking space-level energy storage units within its jurisdiction. This data covers a comprehensive range of operational indicators, including the battery pack's remaining charge (SOC) trajectory from commissioning, state of health (SOH) degradation data, cumulative charge-discharge cycles, continuous operating time under different power conditions, battery operating temperature fluctuation data, fault frequency and fault type correlation data, and energy conversion efficiency degradation trends. The cloud platform cleans, deduplicates, removes outliers, and standardizes the collected multi-source data. Through big data analysis and battery performance modeling algorithms, it uncovers the inherent patterns of battery performance changes with operating time and conditions, constructing a unique battery performance degradation curve for each parking space-level energy storage unit. This curve accurately characterizes core features such as the battery pack's capacity degradation trend, maximum output power degradation trend, correlation trend between state of health and charge-discharge frequency, and performance degradation differences at different temperatures, intuitively reflecting the performance change patterns of the energy storage unit throughout its entire lifecycle.

[0081] The cloud platform acquires real-time location information of IoT sensor nodes for all core devices within the microgrid's coverage area through the communication subsystem of the community's DC microgrid. This includes the physical locations of core communication nodes such as the microgrid control subsystem and active power control modules, the deployment locations of parking space-level energy storage units and charging units, and the communication connection points between sensor nodes. Simultaneously, it collects spatial correlation data such as the actual physical distance between sensor nodes, communication link routes, and power transmission line layouts. Based on spatial geometric modeling and topology graph generation technology, the cloud platform transforms the aforementioned discrete node location information into a visualized and digital spatial topology graph. This graph accurately reconstructs the relative spatial relationships of each sensor node, the communication connections between nodes, and the correlations of power transmission links, clearly presenting the physical layout and equipment networking characteristics of the community's DC microgrid. This enables the data-driven and model-based expression of the spatial distribution and connection logic of equipment within the microgrid, providing a topological basis for subsequent spatial dimension adaptation of power data arrays.

[0082] The cloud platform retrieves a pre-calibrated power data base array. This array is a standardized power data matrix preset by power load forecasting and system capacity calculation algorithms, which combines multiple factors such as the upper limit of the community grid access capacity, the total installed power of the parking space-level storage and charging units, the spatiotemporal distribution data of the community's historical charging load, the peak and valley electricity price range of the electricity market, and the microgrid operation safety threshold. It includes core power data such as charging and discharging power benchmark values, power transmission capacity benchmark values, equipment operating power thresholds, and load allocation benchmark ratios, and serves as the basic data carrier for microgrid power dispatch. The cloud platform performs quantitative analysis on the spatial topology graph, extracting quantitative indicators such as the connectivity of each node (the number of connections between a node and other devices), the communication connection weight between nodes, and the loss weight corresponding to the power transmission distance. Using these quantitative indicators as the basis for data dimension variation, the power data base array is spatially adapted through data processing methods such as array dimension stretching, compression, node mapping reorganization, and numerical weight correction. This ensures that the mutated first power data array deeply matches the actual spatial topology characteristics of the community DC microgrid. Each data dimension and value within the array corresponds to a specific node and connection relationship in the topology graph, eliminating the disconnect between the base array and the actual physical layout of the microgrid, and making the power data more consistent with the actual operation scenario of the microgrid.

[0083] The cloud platform extracts features from the battery performance degradation curves of each parking space-level energy storage unit constructed in the early stage, accurately identifying various characteristic inflection points in the curves, including critical inflection points of battery capacity degradation, sudden inflection points of maximum output power, inflection points of state of health (SOH) threshold, and inflection points of accelerated efficiency degradation. It also extracts trend parameters such as curve slope changes, trend directions, and performance degradation rates at different operating stages. Using these characteristic inflection points and trend parameters as the core optimization basis, the cloud platform performs targeted local data optimization processing on the first power data array: for data dimensions related to energy storage units whose battery performance is in the high-efficiency operating range, with no or low degradation, weights are increased and values ​​are enhanced to strengthen their priority in power dispatch; for data related to energy storage units whose battery performance is close to the degradation inflection point and in the medium degradation range, gradient correction and threshold limits are applied to ensure data rationality and equipment operational safety; for data related to energy storage units whose battery performance exceeds the degradation inflection point and is in the high degradation range, weights are reduced and the scope is narrowed to avoid the negative impact of inefficient equipment on overall power dispatch. Through the above weight optimization and feature enhancement operations, a second power data array is obtained. This array not only fits the spatial topology characteristics of the microgrid, but also deeply adapts to the actual battery performance of the energy storage unit, achieving a precise match between power data and the actual operating capabilities of the equipment.

[0084] The cloud platform first performs a full-dimensional analysis of the data to be encrypted within the system, clarifying its dimensional information (such as the row and column dimensions of the data matrix, the number of data items, and the channel dimensions for data transmission) and data type characteristics (such as numerical scheduling instruction data, character parameter data, time-series transaction data, and privacy-sensitive user charging data). Based on the importance and confidentiality requirements of the data, it classifies it into security levels. Using a second power data array optimized for both spatial topology and battery performance as the core key generation basis, the cloud platform combines the dimensional information and type characteristics of the data to be encrypted with a key generation algorithm that integrates symmetric and asymmetric encryption. This algorithm extracts core information such as feature values, dimensional parameters, and topological association codes from the array to generate a dynamic encryption key highly adapted to the data. This key is dynamically updated synchronously with the power data array, rather than being a fixed key, thus enhancing encryption security and resistance to cracking. Based on the generated dynamic encryption keys, the cloud platform implements layered encryption processing for data to be encrypted at different security levels: For core confidential data such as dispatch instructions between the cloud platform and the community's DC microgrid, and interactive data from electricity market transactions, high-level encryption algorithms and dedicated subkeys are used to ensure absolute security of data transmission and storage; for important data such as core operating parameters of each parking space-level charging and storage unit, medium-level encryption is used for protection; and for the charging privacy data of electric vehicle users, dedicated encryption is applied using privacy protection algorithms, ensuring data security while complying with data privacy protection standards. Through layered encryption processing, precise and differentiated encryption protection is achieved for different types and security levels of data within the system, comprehensively safeguarding system data security and user privacy.

[0085] The data to be encrypted in this solution consists of core sensitive data from the operation of the electric vehicle charging system, specifically including three categories: First, charging and discharging scheduling command data between the cloud platform and the community DC microgrid. This type of data is the core command for microgrid operation, directly determining the system's charging and discharging strategy, and must be strictly encrypted to prevent tampering or theft. Second, core operating parameters of each parking space-level storage and charging unit, including battery pack SOC, SOH, maximum output power, fault status, etc., which are the basis for equipment operation monitoring and scheduling. Encryption can avoid operational risks caused by unauthorized access to equipment data. Third, interactive data from electricity market transactions and charging privacy data of electric vehicle users. The former includes commercially sensitive data such as electricity market declaration capacity, transaction price, and peak-shaving and frequency regulation response commands, while the latter includes personal privacy data such as user charging duration, charging amount, charging frequency, and parking space usage information. Encryption can protect the security of market transactions and the personal information rights of users.

[0086] In one embodiment, the cloud platform is further used for: Based on the full life cycle operation status data of parking space-level energy storage units, construct the corresponding power output characteristic curves of energy storage units; The physical deployment locations of each parking space-level energy storage unit and charging unit and the relative locations of IoT sensor nodes within the community's DC microgrid are obtained, and the relative locations are simulated as a microgrid resource topology graph. A preset charging and discharging power base array is obtained. Based on the node connection relationship and power transmission loss weight of the microgrid resource topology, the charging and discharging power base array is reconstructed in terms of data dimension and numerical variation to obtain the first charging and discharging power array. Based on the power threshold inflection point and high-efficiency output range parameters of the power output characteristic curve, the local power values ​​of the first charge-discharge power array are optimized and the range is limited to obtain the second charge-discharge power array. Based on the second charging and discharging power array and the power and energy dimensions of the real-time charging demand of electric vehicles, precise charging and discharging scheduling instructions for each parking space-level energy storage unit are generated and sent to the microgrid control subsystem of the community DC microgrid for execution.

[0087] In this embodiment, the cloud platform collects complete operational status data for the entire lifecycle of each parking space-level energy storage unit within its jurisdiction. This data covers core operational indicators such as dynamic changes in battery pack remaining charge (SOC) after the energy storage unit is put into operation, state of health (SOH) parameters, maximum output power and continuous output duration under different operating conditions, charge / discharge cycle count and power decay correlation data, coupling data between battery operating temperature and power output, and real-time energy conversion efficiency data. The cloud platform performs cleaning, deduplication, outlier removal, and standardization preprocessing on the collected multi-source heterogeneous data. Relying on big data analysis and battery performance modeling algorithms, it explores the inherent correlation between the energy storage unit's power output capability and various operational indicators, constructing a unique power output characteristic curve for each parking space-level energy storage unit. This curve accurately characterizes the energy storage unit's core features such as maximum output power threshold, high-efficiency power output range, and power output stability under different SOC ranges, different SOH states, and different operating temperatures. It intuitively reflects the change in the actual power output capability of the energy storage unit with its operating state, providing core data support and basis for the precise scheduling of subsequent charge and discharge power.

[0088] The cloud platform, through the communication subsystem of the community's DC microgrid, acquires real-time information on the actual physical deployment locations of all parking space-level energy storage units and charging units within the microgrid's coverage area. Simultaneously, it collects the installation locations, communication connection points, and relative spatial relationships between IoT sensor nodes mounted on each device. It also acquires spatial correlation data such as the layout and routing of power transmission lines between devices and the communication link connections between sensor nodes. Based on spatial geometric modeling and topology graph digitization technology, the cloud platform integrates and models the aforementioned discrete device locations and node correlation information, transforming them into a visualized and digital microgrid resource topology graph. This graph accurately recreates the relative spatial distribution of energy storage units and charging units within the community's DC microgrid, clearly presenting the connection relationships of each IoT sensor node and the logic of power transmission link connections between devices. It achieves a digital and model-based expression of the spatial distribution of energy storage and charging resources and the networking characteristics of devices within the microgrid, providing a topological basis for subsequent spatial dimension adaptation of charging and discharging power arrays and physical scenario matching for power scheduling.

[0089] The cloud platform retrieves a pre-defined charging and discharging power baseline array, calibrated using power load forecasting and system capacity calculation algorithms. This array is a standardized power data matrix containing core data such as the baseline values ​​of charging and discharging power for each region, the baseline ratio of equipment power allocation, the power transmission capacity threshold, and the charging demand response power baseline. The cloud platform performs quantitative analysis on the microgrid resource topology, extracting quantitative indicators such as the connectivity of each device node (the number of connections between the node and other energy storage and charging units), the connection weight of the power transmission links between nodes, and the power transmission loss weight calculated based on the physical distance of the devices and the line specifications. Based on these quantitative indicators, the platform performs spatial dimension adaptation reconstruction and numerical variation on the basic charging and discharging power array through data processing methods such as array dimension stretching and compression, node power value mapping and reorganization, and loss weight correction. This ensures that the mutated first charging and discharging power array is deeply matched with the actual resource topology characteristics and power transmission characteristics of the community DC microgrid. Each data dimension and value within the array corresponds to a specific device node and connection relationship in the topology graph, eliminating the disconnect between the basic array and the actual physical operation scenario of the microgrid, and making the power data more consistent with the actual power transmission and device networking of the microgrid.

[0090] The cloud platform performs refined feature extraction on the power output characteristic curves previously constructed for each parking space-level energy storage unit. It accurately identifies various power threshold inflection points in the curves, including the maximum output power inflection point, power output efficiency decay inflection point, and safe operation power threshold inflection point for different energy storage unit states. Simultaneously, it extracts core features from the curves, such as high-efficiency output range parameters and power output gradient change parameters that characterize the optimal operating state of the energy storage unit. Using these power threshold inflection points and high-efficiency output range parameters as the core optimization basis, the cloud platform conducts targeted local power value optimization and range limitation processing on the first charging and discharging power array: for relevant data dimensions where the power output of the corresponding energy storage unit in the array is within the high-efficiency range, power value weights are increased and output priorities are strengthened to ensure that the energy storage unit fully utilizes its power output capability under optimal conditions; for power values ​​close to the power threshold inflection point, gradient correction and smoothing adjustments are performed to avoid equipment instability or malfunctions caused by sudden power changes; for power values ​​exceeding the power threshold inflection point or not conforming to the actual output capability of the energy storage unit, they are directly eliminated and range-limited to ensure that all power values ​​within the array are within the safe and stable operating range of the energy storage unit. Through the above series of optimizations, adjustments and interval limitations, a second charging and discharging power array is obtained. This array not only fits the actual resource topology and power transmission characteristics of the community's DC microgrid, but also is highly compatible with the actual power output capabilities of each energy storage unit, achieving precise matching between charging and discharging power data and the actual operating status of the equipment.

[0091] The cloud platform collects real-time charging demand information from all electric vehicles connected to the system through the sensing and communication modules of the community's DC microgrid. It focuses on analyzing and extracting power-related information (such as the rated charging power, real-time requested charging power, and fast / slow charging power demand) and energy-related information (such as the remaining battery capacity, target charging capacity, and estimated charging time). This information is then comprehensively analyzed in conjunction with the usage status of each charging space and the priority of charging demands. The cloud platform uses a second charging and discharging power array, optimized by both the microgrid resource topology and the power output capability of the energy storage units, as the core scheduling basis. Combining the power and energy-related information of the real-time charging demands of electric vehicles, and relying on power resource optimization and allocation algorithms, it dynamically schedules and precisely allocates power to the energy storage resources within the microgrid. This clarifies the charging and discharging operating status, real-time output / input power values, power output duration, and corresponding charging units for each parking space-level energy storage unit, generating precise charging and discharging scheduling instructions for each parking space-level energy storage unit. The cloud platform uses a standardized communication protocol to send the aforementioned scheduling instructions to the microgrid control subsystem of the community's DC microgrid in real time. The microgrid control subsystem then breaks down the scheduling instructions into specific equipment execution instructions and sends them to the corresponding parking space-level energy storage units and charging units. This enables refined and precise scheduling of charging and discharging power within the community's DC microgrid, ensuring efficient and timely response to electric vehicle charging needs, while also achieving optimal allocation and utilization of energy storage resources.

[0092] In one embodiment, the cloud platform is further used for: Based on the real-time operating status data and historical charging and discharging condition data of the parking space-level energy storage unit, a power output adaptation curve corresponding to each energy storage unit is constructed. Obtain the physical deployment location and power transmission link topology of each parking space-level energy storage unit and charging unit in the community's DC microgrid, simulate it as a microgrid power transmission topology diagram, and calibrate the power transmission loss coefficient of each link. The basic charging and discharging power array based on the community's power capacity and historical charging load is retrieved. Combined with the node connection relationship and transmission loss coefficient of the microgrid power transmission topology, the node power value mapping and dimension reconstruction of the basic charging and discharging power array are performed to obtain the third charging and discharging power array. Based on the high-efficiency output range, power threshold inflection point, and SOH-related power limitation of the power output adaptation curve, the third charge-discharge power array is locally optimized and invalid values ​​are removed to obtain the fourth charge-discharge power array. By combining the power demand, energy demand and charging priority information of electric vehicles in real time, precise charging and discharging scheduling instructions for each vehicle-level energy storage unit are generated based on the fourth charging and discharging power array and sent to the microgrid control subsystem for execution, so as to realize the dynamic matching of charging and discharging power of the energy storage unit.

[0093] In this embodiment, the cloud platform continuously collects real-time operating status data for each parking space-level energy storage unit through the communication subsystem and sensing modules of the community's DC microgrid. This data covers core dynamic indicators such as remaining battery charge (SOC), state of health (SOH), real-time output power, battery operating temperature, charge / discharge conversion efficiency, and equipment operating mode. Simultaneously, it retrieves historical charge / discharge data for each energy storage unit throughout its entire lifecycle, including power output stability data across different SOC ranges, power decay data under different temperature conditions, correlation data between the number of charge / discharge cycles and power output capability, and continuous operating time data under different output powers. The cloud platform integrates the collected real-time dynamic data and historical operating data, performing preprocessing operations such as data cleaning, outlier removal, and standardization. Then, relying on battery performance modeling algorithms and big data correlation analysis technology, it uncovers the inherent coupling patterns between the energy storage unit's power output capability and various operating indicators and historical operating conditions, independently constructing a unique power output adaptation curve for each parking space-level energy storage unit. This curve can accurately characterize the optimal power output range, safe power output threshold, and power output efficiency characteristics of the energy storage unit under different real-time operating states and different historical operating conditions, and becomes the core basis for adapting the actual output capacity of the energy storage unit in subsequent charging and discharging power scheduling.

[0094] Next, the cloud platform, through the device management and spatial positioning modules of the community's DC microgrid, accurately obtains the actual physical deployment coordinates of all parking space-level energy storage units and charging units within the microgrid's coverage area. Simultaneously, it collects the layout, specifications, and connection methods of DC power transmission lines between each device, clarifying the direct and indirect connections and power transmission paths between energy storage and charging units. Based on spatial geometric modeling and digital topology generation technology, the cloud platform integrates and models the aforementioned physical deployment locations and power transmission link topology information, transforming them into a quantifiable and visualized microgrid power transmission topology diagram. This diagram fully restores the spatial distribution of energy storage and charging devices within the microgrid and the logical associations of power transmission links between devices. Furthermore, based on parameters such as the material, length, and specifications of the power transmission lines, and combined with the DC power transmission loss calculation formula, the cloud platform accurately calculates each power transmission link in the topology diagram, assigning a unique power transmission loss coefficient to each link. This coefficient directly reflects the proportion of power loss in the corresponding link, becoming a core quantitative indicator for considering actual power transmission losses in subsequent power array reconfiguration.

[0095] The cloud platform first retrieves a pre-calibrated baseline charging and discharging power array. This array is a standardized power data matrix preset by the cloud platform using power load prediction algorithms and system capacity calculation models, taking into account multiple dimensions such as the upper limit of the community's grid access capacity, the total installed power of parking space-level energy storage and charging units, the spatiotemporal distribution characteristics of the community's historical charging load, and the microgrid's safe operating power threshold. It includes core basic data such as the baseline values ​​of charging and discharging power for each region, the basic proportion of equipment power allocation, and the baseline values ​​of charging demand response power, serving as the original data carrier for microgrid charging and discharging power scheduling. Subsequently, the cloud platform performs quantitative analysis on the microgrid's power transmission topology, extracting quantitative indicators of node connectivity, such as the connectivity of each device node and the point-to-point connection relationship between energy storage units and charging units. Combined with the calibrated power transmission loss coefficients of each link, a quantitative weighting system for power array reconstruction is constructed. Based on this system, the cloud platform performs node power value mapping and dimension reconstruction on the basic charging and discharging power array: the basic power values ​​in the array are accurately matched with the specific device nodes in the topology graph, the row and column dimensions of the array are stretched, compressed and reorganized based on the node connection relationship, and the power values ​​corresponding to each node are corrected and adjusted by weighting the power transmission loss coefficient. This makes the reconstructed power array highly compatible with the actual power transmission topology and link loss characteristics of the microgrid, and finally obtains the third charging and discharging power array. This array eliminates the disconnect between the basic power array and the physical operation scenario of the microgrid, and makes the power data more consistent with the actual power transmission and device networking situation.

[0096] The cloud platform performs refined feature extraction on the constructed power output adaptation curve, accurately identifying the high-efficiency output range in the curve that represents the optimal operating state of the energy storage unit, and clarifying that the energy storage unit has the highest power output efficiency and the most stable operation within this range. At the same time, it extracts the power threshold inflection points in the curve, including the maximum output power inflection point, the power output efficiency decay inflection point, and the safe operating power lower limit inflection point, defining the power output boundary of the energy storage unit. In addition, combined with the SOH-related power limit set based on the SOH parameter in the curve, it clarifies the upper limit of the power output of the energy storage unit under different health conditions, avoiding irreversible damage to the battery caused by excessive power output. The cloud platform uses efficient output range, power threshold inflection point, and SOH-related power limits as core optimization criteria to perform targeted local power value optimization and invalid value removal operations on the third charging and discharging power array. Specifically, it increases the weight of power output values ​​corresponding to energy storage units within the efficient output range, strengthening their priority in power scheduling; it performs gradient correction and smoothing adjustments on values ​​approaching the power threshold inflection point to avoid equipment instability caused by sudden power changes; and it directly determines and removes power values ​​exceeding the power threshold inflection point or not conforming to SOH-related power limits. Simultaneously, it limits the range of all retained power values ​​to ensure they remain within the safe and efficient power output range of the energy storage units. Through these operations, a fourth charging and discharging power array is obtained. This array not only conforms to the power transmission topology and link loss characteristics of the microgrid but also highly matches the actual power output capabilities of each energy storage unit, providing reliable power data support for subsequent precise scheduling.

[0097] Finally, the cloud platform collects real-time charging demand information from all electric vehicles connected to the system through the charging unit sensing modules of the community's DC microgrid. It focuses on analyzing and extracting power demand (rated charging power of electric vehicles, real-time requested fast / slow charging power, power demand fluctuation range) and energy demand (remaining battery capacity of electric vehicles, target charging capacity, and estimated charging capacity difference). Simultaneously, it combines user charging reservation information, community charging load peak-hour scheduling rules, and emergency charging needs of electric vehicles to assign corresponding charging priority information to each charging demand. The cloud platform uses a fourth charging and discharging power array, optimized through power transmission topology adaptation and energy storage unit power capability optimization, as the core scheduling basis. Combining the power demand, energy demand, and charging priority information of electric vehicles, and relying on power resource optimization allocation algorithms and multi-objective scheduling models, it dynamically plans and precisely allocates the charging and discharging resources of all parking space-level energy storage units within the microgrid. This clarifies the working status (charging / discharging / standby), real-time charging and discharging power values, power output / input duration, the corresponding charging unit, power adjustment gradient, and other core scheduling parameters for each energy storage unit. Based on the above allocation results, the cloud platform generates a unique and precise charging and discharging scheduling command for each parking space-level energy storage unit, and sends the command to the microgrid control subsystem of the community's DC microgrid in real time through a standardized communication protocol. After receiving the scheduling command, the microgrid control subsystem breaks it down into specific equipment execution commands and sends them to the corresponding parking space-level energy storage unit and charging unit, realizing the coordinated operation of each device. Ultimately, it achieves a dynamic and precise match between the charging and discharging power of the energy storage and charging units and the real-time charging demand of electric vehicles, ensuring efficient and timely response to charging needs while achieving optimal allocation and utilization of energy storage resources within the microgrid.

[0098] In summary, the electric vehicle charging system provided in this embodiment of the invention includes a parking space-level energy storage and charging unit. The parking space-level energy storage and charging unit is used for communication connection with a community DC microgrid, and the community DC microgrid is communicated with a cloud platform. The parking space-level energy storage and charging unit includes multiple parking space-level energy storage units and multiple parking space-level charging units. The parking space-level energy storage units and parking space-level charging units are connected through the community DC microgrid and are all used for charging electric vehicles. The community DC microgrid is used for unified scheduling of the charging and discharging of the parking space-level energy storage and charging units, and the cloud platform is used for unified monitoring and management and the formulation of charging and discharging scheduling strategies. In this invention, the parking space-level energy storage and charging unit includes multiple parking space-level energy storage units and multiple parking space-level charging units. The parking space-level energy storage units and parking space-level charging units are connected through the community DC microgrid and are all used for charging electric vehicles. In charging scenarios with limited space and insufficient power capacity, the normal and effective charging of electric vehicles can be guaranteed, overcoming the shortcomings of current charging systems that cannot adapt to charging scenarios with limited space and insufficient power capacity.

[0099] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0100] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An electric vehicle charging system, characterized in that, It includes a parking space-level energy storage and charging unit; the parking space-level energy storage and charging unit is used to communicate with the community DC microgrid, and the community DC microgrid is used to communicate with the cloud platform; The parking space-level energy storage and charging unit includes multiple parking space-level energy storage units and multiple parking space-level charging units. The parking space-level energy storage units and parking space-level charging units are connected through a community DC microgrid and are all used to charge electric vehicles. The community DC microgrid is used for unified scheduling of charging and discharging of parking space-level energy storage and charging units, and the cloud platform is used for unified monitoring and management and the formulation of charging and discharging scheduling strategies.

2. The electric vehicle charging system according to claim 1, characterized in that, The connection relationship between the parking space-level energy storage unit and the parking space-level charging unit is one-to-one, many-to-one, one-to-many, or many-to-many.

3. The electric vehicle charging system according to claim 1, characterized in that, The parking space-level energy storage unit includes a battery pack, a first DC / DC conversion circuit, a first control circuit, and a first communication circuit; the parking space-level charging unit includes a second DC / DC conversion circuit, a charging circuit, a second control circuit, and a second communication circuit.

4. The electric vehicle charging system according to claim 3, characterized in that, Both the parking space-level energy storage unit and the parking space-level charging unit adopt a pure DC solution; the parking space-level energy storage and charging unit is installed in each parking space of the community parking lot.

5. The electric vehicle charging system according to claim 1, characterized in that, The cloud platform integrates dispersed power resources, all parking space-level energy storage units, and electric vehicle battery resources into a virtual power plant, enabling it to participate in electricity market transactions.

6. The electric vehicle charging system according to claim 1, characterized in that, The community's DC microgrid includes a centralized inverter, a microgrid control subsystem, a DC power transmission and distribution line subsystem, and a communication subsystem; The centralized inverter is used to realize bidirectional conversion between AC power from the power grid or photovoltaic / wind power generation and DC power within the microgrid; The microgrid control subsystem communicates with the centralized inverter, parking space-level energy storage unit, and parking space-level charging unit through the communication subsystem, and connects to the cloud platform through the network to receive cloud platform instructions and perform charging and discharging management.

7. The electric vehicle charging system according to claim 6, characterized in that, The microgrid control subsystem collects power data of each parking space-level energy storage unit through the communication subsystem. When the power of the parking space-level energy storage unit in the target parking space is insufficient, it dispatches the power of the parking space-level energy storage units in other parking spaces to charge the electric vehicle in the target parking space.

8. The electric vehicle charging system according to claim 1, characterized in that, The community DC microgrid includes an active power control module, which is communicatively connected to each parking space-level energy storage unit and parking space-level charging unit. The active power control module collects the status and performance data of each parking space-level energy storage unit, and issues commands to each parking space-level energy storage unit regarding its working status, maximum operating power output, and specified output voltage. Each parking space-level energy storage unit adjusts its output according to the commands to respond to the load's power demand.

9. The electric vehicle charging system according to claim 8, characterized in that, The status and performance data of the parking space-level energy storage unit include whether the parking space-level energy storage unit is available, the maximum power supply, the continuous power supply time, the optimal operating power, whether it is an energy storage power source, the battery capacity, and the SOH parameter.

10. The electric vehicle charging system according to claim 1, characterized in that, The parking space-level energy storage unit can be charged using the power grid, photovoltaic, or wind power generation when the grid load is low and the electricity price is low; when the grid capacity is insufficient or the electricity price is high, it can supply power to electric vehicles that need charging.

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