Distributed energy storage wireless intelligent charging system based on the Internet of Things

Abstract

The present invention discloses a distributed energy storage wireless intelligent charging system based on the Internet of Things, which relates to the field of new energy charging infrastructure, including charging nodes, multiple charging nodes forming a mesh topology structure, and the charging nodes including charging piles, energy storage units, edge computing modules, communication modules, and sensor modules. In the present invention, by integrating distributed energy storage, edge intelligent computing, mesh topology networks, dual-mode communications, magnetic resonance coupled wireless charging, fuzzy control algorithms, and mobile application technologies, intelligent management of the charging process, efficient energy scheduling, and dynamic expansion of the system are achieved. It has significant creative breakthroughs in energy storage optimization, network reliability, data processing accuracy, user interaction experience, and system flexibility, and solves problems such as low energy utilization efficiency, high grid pressure, and slow fault recovery. It has higher technical value and practical application prospects.

Description

Technical Field

[0001] The present invention relates to the field of new energy charging infrastructure, and in particular to a distributed energy storage wireless intelligent charging system based on the Internet of Things. Background Art

[0002] With the popularization of new energy vehicles, charging piles and related infrastructure have gradually become one of the important infrastructures. Charging piles are the main equipment for charging electric vehicles. They are usually divided into AC charging piles and DC charging piles. The latter charges faster. With the development of technology, wireless charging technology is also gradually being put into use. Charging piles are connected through power networks, communication networks and the Internet to form a charging network, allowing car owners to easily find charging piles and complete payment and other operations.

[0003] Published patent: A smart charging system and method (publication number: CN106374555A), comprising a service terminal, a server, and a charging device terminal. The charging device terminal includes a network module, a local storage module, a controller, a power module, and a local receiving module. The service terminal is connected to the server, which is connected to the charging device network via the network module. The service terminal obtains charging authorization via the local receiving module and charges by connecting to the power module. This smart charging system and method provides charging service hardware and a permissions management and control system. This permissions management and control system embeds media advertising, big data statistics, and other services, enabling the rational conservation and management of charging device resources, the precise delivery and statistics of audience information, and the rational and effective integration of public charging needs and corporate data requirements. This system and method has broad application prospects.

[0004] The above patent has the following defects: its technical architecture is limited to authority management and basic charging functions. It cannot use energy storage units to supply power to reduce grid pressure during peak grid load, nor can it perform energy storage charging when electricity prices are low to reduce energy costs. Its charging and discharging process is completely dependent on the real-time load of the grid. It can neither optimize energy distribution efficiency nor alleviate grid fluctuations, resulting in low energy utilization efficiency and high operating costs. The lack of real-time monitoring and prediction capabilities makes it impossible to actively prevent safety hazards such as thermal runaway. Equipment safety relies on manual intervention and lacks self-organizing network capabilities. It cannot automatically isolate when a node fails, and the access of new nodes requires manual configuration and matching authorization. It cannot meet the comprehensive requirements of modern smart charging systems for efficiency, stability, adaptability and user-friendliness. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a distributed energy storage wireless intelligent charging system based on the Internet of Things.

[0006] In order to achieve the above-mentioned objectives, the present invention adopts the following technical solutions: a distributed energy storage wireless intelligent charging system based on the Internet of Things, including charging nodes, multiple charging nodes forming a mesh topology structure, the charging nodes including charging piles, energy storage units, edge computing modules, communication modules and sensor modules, the charging piles are used to provide wired DC fast charging interfaces and wireless charging interfaces, and the wireless charging adopts magnetic resonance coupling technology to support contactless power transmission; the energy storage unit includes a battery pack and a bidirectional DC-DC converter, which is responsible for the charging and discharging management of the stored energy, and the power exchange with the power grid is realized through a bidirectional converter; the edge computing module is based on the Cortex-A series processor to realize local data processing and intelligent control, and perform real-time monitoring and data analysis of the temperature, voltage and internal resistance of the battery, charging pile and energy storage unit; the communication module adopts LoRa / NB-IoT dual-mode communication technology, and supports real-time data exchange with the Internet of Things data center and other charging nodes; the sensor module is equipped with multi-point infrared temperature sensors and voltage / current sensors for real-time acquisition of temperature, voltage and internal resistance information of the charging pile, energy storage unit and battery.

[0007] As a further description of the above technical solution:

[0008] The charging nodes are connected to the cloud platform through a communication module. Each charging node forms a reliable self-organizing network in the area through wireless communication to achieve coordinated management of charging and energy storage in the area, support dynamic increase and decrease of nodes, have self-healing and fault isolation functions, and can automatically disconnect the faulty node when a node fails. New charging nodes can be seamlessly connected to existing charging nodes through standardized communication protocols, supporting rapid access, exit and automatic upgrade of charging nodes.

[0009] As a further description of the above technical solution:

[0010] The communication module can seamlessly switch between low-power wide area networks and 5G slicing networks. The communication module of each charging node can automatically select the optimal communication mode according to the actual network environment. The edge computing module has a built-in Kalman filter algorithm for noise removal and signal fusion of the temperature, voltage, and current data collected by the sensor module, and real-time prediction of the temperature trends of batteries, energy storage units, and charging piles to determine whether there is a potential risk of overheating or thermal runaway; the LSTM model is used to make long-term predictions on the health status of the equipment and automatically issue alarms or control instructions when the risk of failure is approaching.

[0011] As a further description of the above technical solution:

[0012] The charging pile supports two charging modes: wireless charging and wired charging. When the state of charge of the rechargeable battery is low, the system gives priority to the wireless charging mode; when the SOC is high or the wireless charging efficiency is insufficient, the system will remind the owner to switch to the wired DC charging mode to provide higher charging power. The infrared temperature sensor is distributedly installed on the charging pile, battery and energy storage unit to collect device temperature data in real time, and smooth and pre-process the data through the Kalman filter algorithm to avoid equipment damage caused by temperature abnormalities. When temperature abnormalities are detected, the charging power will be automatically adjusted. The voltage / current sensor is installed on the energy storage unit, and the system is connected to the electric vehicle's on-board system through a communication module to obtain the battery power and voltage.

[0013] As a further description of the above technical solution:

[0014] The power dispatching function is achieved through a fuzzy control algorithm. This algorithm automatically optimizes power flow and dispatching strategies based on real-time data on the battery status of the charging node, the energy storage status in the area, the grid load, and electricity prices. The charging and discharging process of the energy storage unit is managed by a bidirectional DC-DC converter, and the charging or discharging power of the energy storage unit is adjusted according to the grid load conditions. During peak grid load periods, power is provided by the energy storage unit first, and during low grid load periods, power is supplied by the grid. During low electricity prices, the energy storage unit is charged. Power can be transmitted between the energy storage units. When the energy storage unit does not have enough energy to fully charge the battery, the energy is replenished through the charging node.

[0015] As a further description of the above technical solution:

[0016] The system comes with a mobile app that provides the following features:

[0017] A. Push real-time electricity price change information based on grid load and energy storage status;

[0018] B. Predict charging completion time based on current battery SOC and charging power;

[0019] C. Calculate the charging fee based on the charging time and electricity price, and provide the user with a detailed fee statement after the charging is completed;

[0020] D. Real-time display of battery charging status, health report, charging history, and temperature curve information.

[0021] As a further description of the above technical solution:

[0022] The process of charging node replenishing power is as follows:

[0023] S1: It is detected that the power of the energy storage unit in the charging node is insufficient to complete the charge, and the charging node sends information to the IoT data center;

[0024] S2. The IoT data center sends information to other charging nodes to obtain the node status, and determines whether the charging node is charging and whether the energy storage unit has power.

[0025] S2. If the energy storage power supply of the charging node has no power, the node does not need to transmit power to the power-deficient node;

[0026] S3. If the energy storage unit of the charging node has power and the charging node is charging, determine whether the energy storage power supply of the charging node has any remaining power after charging is completed;

[0027] S4. If the energy storage power source of the charging node has remaining power after charging is completed, the energy less than the remaining power will be transmitted to the node with power shortage. If there is no remaining power, the node does not need to transmit power to the node with power shortage.

[0028] S5. If the charging node is no longer charging and there is surplus power in the energy storage unit, the node needs to transmit power to the power-deficient node.

[0029] As a further description of the above technical solution:

[0030] The charging control method includes the following steps:

[0031] A. Obtain the battery's state of charge, maximum battery capacity, charging pile temperature, energy storage unit power, and grid load information;

[0032] B. Select the appropriate charging mode based on charging demand and energy storage status, and dynamically adjust the charging power according to the battery SOC;

[0033] C. Monitor the device temperature and voltage in real time during the charging process. If the device temperature is abnormal or the battery SOC is close to overcharge, the system will automatically reduce the power or suspend charging.

[0034] As a further description of the above technical solution:

[0035] The power scheduling method includes the following steps:

[0036] A. The node generates a power demand report based on the battery SOC, energy storage unit power, and grid load information, and publishes it to other nodes in the region through the IoT gateway;

[0037] B. Based on the current grid load conditions, a fuzzy control algorithm is used for power dispatching. During peak hours, energy storage units are given priority for discharge.

[0038] C. During low electricity consumption periods, energy storage units are dispatched for charging via smart contracts.

[0039] D. Power transmission between nodes through regional mesh topology.

[0040] As a further description of the above technical solution:

[0041] During the power scheduling process, the system dynamically adjusts the charging power and energy storage battery charging and discharging strategies, and gives priority to the energy storage unit during peak charging periods.

[0042] The present invention has the following beneficial effects:

[0043] 1. The present invention integrates distributed energy storage, edge intelligent computing, mesh topology network, dual-mode communication, magnetic resonance coupling wireless charging, fuzzy control algorithm and mobile application technology to achieve intelligent management of the charging process, efficient energy scheduling and dynamic expansion of the system. It has significant creative breakthroughs in energy storage optimization, network reliability, data processing accuracy, user interaction experience and system flexibility, and solves problems such as low energy utilization efficiency, high grid pressure and slow fault recovery. It has higher technical value and practical application prospects.

[0044] 2. In the present invention, a mesh topology is adopted to make the charging nodes in the same scene form a local area network, and the energy scheduling function is realized through the fuzzy control algorithm. The charging nodes dynamically optimize the charging and discharging strategy according to the real-time data of the grid load, electricity price and battery status, give priority to using the energy storage unit for power supply during peak grid load, and charge through the grid during low load, significantly reducing the grid pressure and optimizing energy. The communication module supports seamless switching between low-power wide area networks and 5G slicing networks, and automatically selects the optimal communication mode according to the actual network environment. The charging nodes form a self-organizing network through LoRa / NB-IoT dual-mode communication technology, support dynamic addition and subtraction of nodes and self-healing functions, seamless access and automatic upgrade of new nodes, ensuring the scalability and compatibility of the system. When a node fails, the system automatically disconnects the faulty node and isolates the fault to ensure the normal operation of other nodes, and supports energy transfer between charging nodes. When the energy storage unit of a node is insufficient, the system coordinates other nodes to transmit power through the Internet of Things data center to achieve point-to-point energy transfer.

[0045] 3. In the present invention, the temperature of charging piles, energy storage units and batteries is monitored by multi-point distributed infrared sensors, and the Kalman filter algorithm is used to remove noise and perform signal fusion processing on the data, and the LSTM model is combined to perform long-term health status prediction. This technical solution can monitor the temperature of the equipment in real time and automatically adjust the charging power when the risk of thermal runaway is detected to avoid equipment damage or overcharging. The LSTM model is used to analyze historical data and make long-term predictions on the health status of batteries, charging piles and energy storage units. When the system detects the risk of thermal runaway or failure, it can issue an alarm in advance and take corresponding control measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] Reference Figure 1 , the present invention provides an embodiment: a distributed energy storage wireless intelligent charging system based on the Internet of Things, including charging nodes, multiple charging nodes forming a mesh topology structure, the charging nodes including charging piles, energy storage units, edge computing modules, communication modules and sensor modules, the charging piles are used to provide wired DC fast charging interfaces and wireless charging interfaces, the wireless charging adopts magnetic resonance coupling technology and supports contactless power transmission; the energy storage unit includes a battery pack and a bidirectional DC-DC converter, which is responsible for the charging and discharging management of the stored energy, and the power exchange with the power grid is realized through a bidirectional converter; the edge computing module is based on the Cortex-A series processor to realize local data processing and intelligent control, and performs real-time monitoring and data analysis on the temperature, voltage and internal resistance of the battery, charging pile and energy storage unit; the communication module adopts LoRa / NB-IoT dual-mode communication technology and supports real-time data exchange with the Internet of Things data center and other charging nodes; the sensor module is equipped with multi-point infrared temperature sensors and voltage / current sensors for real-time acquisition of temperature, voltage and internal resistance information of the charging pile, energy storage unit and battery.

[0049] The charging nodes are connected to the cloud platform through a communication module. Each charging node forms a reliable self-organizing network in the area through wireless communication to achieve coordinated management of charging and energy storage in the area, support dynamic increase and decrease of nodes, and have self-healing and fault isolation functions. When a node fails, it can automatically disconnect the faulty node to ensure the normal operation of other nodes. New charging nodes can be seamlessly connected to existing charging nodes through standardized communication protocols, supporting rapid access, exit and automatic upgrade of charging nodes.

[0050] The communication module can seamlessly switch between low-power wide area networks and 5G slicing networks. The communication module of each charging node can automatically select the optimal communication mode according to the actual network environment. The edge computing module has a built-in Kalman filter algorithm for noise removal and signal fusion of the temperature, voltage, and current data collected by the sensor module, and real-time prediction of the temperature trends of batteries, energy storage units, and charging piles to determine whether there is a potential risk of overheating or thermal runaway; the LSTM model is used to make long-term predictions on the health status of the equipment and automatically issue alarms or control instructions when the risk of failure is approaching.

[0051] The charging pile supports two charging modes: wireless charging and wired charging. When the state of charge of the rechargeable battery is low, the system gives priority to the wireless charging mode; when the SOC is high or the wireless charging efficiency is insufficient, the system will remind the owner to switch to the wired DC charging mode to provide higher charging power. The infrared temperature sensor is distributedly installed on the charging pile, battery and energy storage unit to collect device temperature data in real time, and smooth and pre-process the data through the Kalman filter algorithm to avoid equipment damage caused by abnormal temperature. When a temperature abnormality is detected, the charging power will be automatically adjusted to avoid thermal runaway caused by overcharging. The voltage / current sensor is installed on the energy storage unit, and the system is connected to the electric vehicle's on-board system through a communication module to obtain the battery power and voltage.

[0052] The power dispatching function is achieved through a fuzzy control algorithm. This algorithm automatically optimizes power flow and dispatching strategies based on real-time data on the battery status of the charging node, the energy storage status in the area, the grid load, and electricity prices. The charging and discharging process of the energy storage unit is managed by a bidirectional DC-DC converter, and the charging or discharging power of the energy storage unit is adjusted according to the grid load conditions. When the grid load is peak, power is provided by the energy storage unit first to reduce the burden on the grid. When the grid load is off-peak, power is supplied by the grid. When the electricity price is low, the energy storage unit is charged. Power can be transmitted between the energy storage units. When the energy storage unit does not have enough energy to fully charge the battery, the power is supplemented by the charging node.

[0053] The system comes with a mobile app that provides the following features:

[0054] A. Push real-time electricity price change information based on grid load and energy storage status;

[0055] B. Predict charging completion time based on current battery SOC and charging power;

[0056] C. Calculate the charging fee based on the charging time and electricity price, and provide the user with a detailed fee statement after the charging is completed;

[0057] D. Real-time display of battery charging status, health report, charging history, and temperature curve information.

[0058] The process of charging node replenishing power is as follows:

[0059] S1: It is detected that the power of the energy storage unit in the charging node is insufficient to complete the charge, and the charging node sends information to the IoT data center;

[0060] S2. The IoT data center sends information to other charging nodes to obtain the node status, and determines whether the charging node is charging and whether the energy storage unit has power.

[0061] S2. If the energy storage power supply of the charging node has no power, the node does not need to transmit power to the power-deficient node;

[0062] S3. If the energy storage unit of the charging node has power and the charging node is charging, determine whether the energy storage power supply of the charging node has any remaining power after charging is completed;

[0063] S4. If the energy storage power source of the charging node has remaining power after charging is completed, the energy less than the remaining power will be transmitted to the node with power shortage. If there is no remaining power, the node does not need to transmit power to the node with power shortage.

[0064] S5. If the charging node is no longer charging and there is surplus power in the energy storage unit, the node needs to transmit power to the power-deficient node.

[0065] The charging control method includes the following steps:

[0066] A. Obtain the battery's state of charge, maximum battery capacity, charging pile temperature, energy storage unit power, and grid load information;

[0067] B. Select the appropriate charging mode based on charging demand and energy storage status, and dynamically adjust the charging power according to the battery SOC to reduce grid pressure;

[0068] C. Monitor the device temperature and voltage in real time during the charging process. If the device temperature is abnormal or the battery SOC is close to overcharge, the system will automatically reduce the power or suspend charging.

[0069] The power scheduling method includes the following steps:

[0070] A. The node generates a power demand report based on the battery SOC, energy storage unit power, and grid load information, and publishes it to other nodes in the region through the IoT gateway;

[0071] B. Based on the current grid load conditions, a fuzzy control algorithm is used for power dispatching. During peak hours, energy storage units are given priority for discharge.

[0072] C. During low electricity consumption periods, smart contracts are used to schedule energy storage units for charging, optimizing power distribution and reducing grid pressure.

[0073] D. Power transmission between nodes through regional mesh topology.

[0074] During the power scheduling process, the system dynamically adjusts the charging power and energy storage battery charging and discharging strategies, and gives priority to the energy storage unit during peak charging periods.

[0075] Example 1: Each charging pile is equipped with an energy storage unit to form a charging node. Multiple charging nodes are interconnected in the park through LoRa / NB-IoT dual-mode communication technology and work together with the cloud platform. The charging pile of each charging node supports both wireless charging and wired charging. Wireless charging is achieved through a wireless charging base, and wired charging is achieved through a charging gun. The charging power is dynamically adjusted according to the SOC of the electric vehicle battery to avoid overcharging. When the vehicle's battery SOC is low, the system automatically selects the wireless charging mode and uses magnetic resonance coupling technology to achieve contactless power transmission through a high-frequency electromagnetic field. When the battery SOC is high, the owner can insert the charging gun, and the system will automatically switch to the wired DC charging mode to provide higher charging power and ensure charging efficiency. During the charging process, the temperature, internal resistance and terminal voltage data of the battery and charging pile are monitored in real time through temperature sensors and voltage / current sensors to ensure charging safety and battery health. The data is processed in real time through the edge computing module to adjust the charging strategy in time.

[0076] Example 2: In order to ensure the health status of the charging pile, energy storage unit and battery, this system uses multi-point distributed infrared sensors to monitor the temperature of the charging pile, energy storage unit and battery, processes the collected data through the Kalman filter algorithm, calculates the temperature change curve of the battery and equipment in real time, and evaluates the health status of the equipment in combination with the LSTM model. Each time charging, the system automatically records the remaining power, charging time, maximum capacity and number of charge and discharge times of the battery. Based on the historical data of the battery, the system calculates the health status of the battery and generates a health status report. The report is pushed to the car owner in real time through the APP, and the battery temperature curve is displayed to help the car owner understand the battery usage.

[0077] Example 3: During system operation, especially during peak charging periods, energy storage units are given priority to supply power to reduce the load on the power grid. During low power consumption periods, the power grid charges the energy storage units to reduce overall power costs. Using a fuzzy control algorithm, the system intelligently allocates charging and discharging power based on information such as the battery status, grid load, and energy storage status of each charging node to ensure smooth operation of the charging node. Energy storage units are connected through a mesh topology to achieve power transmission. When the energy storage unit of a charging node is insufficient, the system will send a request to the data center to find out whether the charging node has surplus power. If the energy storage unit of the charging node has surplus power and is not charging, the charging node with surplus power can transmit power to the node lacking power through the Internet of Things data center. If the charging node does not have surplus power, it will refuse to transmit power. If it is charging, it will determine whether the remaining power will remain after charging is completed. If there is surplus power, the power less than the remaining power will be transmitted to the node lacking power, completing point-to-point power transfer. The node with surplus power will transmit power to the node lacking power through the Internet of Things data center.

[0078] Example 4: The charging node adopts a standardized communication protocol, supports seamless access of new nodes, and can automatically disconnect when a faulty node occurs. If a charging node fails, the system can automatically detect and isolate the faulty node through the Internet of Things gateway to ensure the normal operation of other nodes. The physically dispersed charging node structure effectively avoids the paralysis of the entire network caused by a single point of failure. When the system detects a fault in a charging node, it automatically analyzes the working status of the node through the edge computing module and the cloud platform, promptly cuts off the power supply to the node, and notifies the management personnel to carry out maintenance.

[0079] Example 5: Users obtain charging information, dynamic electricity prices, estimated charging time, charging costs, and battery health reports through a mobile phone app. The system pushes electricity price changes in real time based on the grid load and energy storage unit status. Users can choose whether to start charging based on the prompts. The app also has a voice prompt function, and users can query charging status, temperature, and other information through voice. The system automatically adjusts the charging fee based on the real-time power demand of the grid and the power of the energy storage unit, and pushes it to the user through the app. The user can check the charging status at any time through the app. The system dynamically adjusts the charging power based on the real-time battery SOC and grid load to avoid battery overcharging or excessive charging time.

[0080] The above examples demonstrate the application of the IoT-based distributed energy storage wireless intelligent charging system in different scenarios, including intelligent charging of charging piles and energy storage units, temperature monitoring, battery health analysis, and power routing for power scheduling. This system not only improves charging efficiency and reduces dependence on the power grid, but also ensures system stability and efficiency through intelligent management and remote control.

[0081] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The distributed energy storage wireless intelligent charging system based on the Internet of Things is characterized by: It includes charging nodes, and multiple charging nodes form a mesh topology. The charging nodes include charging piles, energy storage units, edge computing modules, communication modules and sensor modules. The charging piles are used to provide wired DC fast charging interfaces and wireless charging interfaces. Wireless charging uses magnetic resonance coupling technology and supports contactless power transmission. The energy storage unit includes a battery pack and a bidirectional DC-DC converter, which is responsible for the charging and discharging management of the stored energy. The power exchange with the power grid is achieved through a bidirectional converter. The edge computing module is based on the Cortex-A series processor to realize local data processing and intelligent control, and to perform real-time monitoring and data analysis on the temperature, voltage and internal resistance of the battery, charging pile and energy storage unit. The communication module adopts LoRa / NB-IoT dual-mode communication technology and supports real-time data exchange with the IoT data center and other charging nodes. The sensor module is equipped with multi-point infrared temperature sensors and voltage / current sensors to obtain real-time temperature, voltage and internal resistance information of the charging pile, energy storage unit and battery. The charging nodes are connected to the cloud platform through a communication module. Each charging node forms a reliable self-organizing network in the area through wireless communication to achieve coordinated management of charging and energy storage in the area, support dynamic addition and reduction of nodes, have self-healing and fault isolation functions, and can automatically disconnect the faulty node when a node fails. New charging nodes can seamlessly connect to existing charging nodes through standardized communication protocols, supporting rapid access, exit and automatic upgrade of charging nodes; the communication module can seamlessly switch between low-power wide area networks and 5G slicing networks. The communication module of each charging node can automatically select the optimal communication mode according to the actual network environment. The edge computing module has a built-in Kalman filter algorithm for noise removal and signal fusion of the temperature, voltage and current data collected by the sensor module, and real-time prediction of the temperature trend of the battery, energy storage unit and charging pile to determine whether there is a potential risk of overheating or thermal runaway; the LSTM model is used to make long-term predictions on the health status of the equipment and automatically issue alarms or control instructions when the risk of failure is approaching; the charging pile supports two charging modes: wireless charging and wired charging. When the state of charge of the rechargeable battery is low, the system gives priority to the wireless charging mode; when the SOC is high, the system gives priority to the wireless charging mode. Or when the wireless charging efficiency is insufficient, the system will remind the owner to switch to wired DC charging mode to provide higher charging power. The infrared temperature sensors are distributedly installed on the charging piles, batteries and energy storage units to collect device temperature data in real time and smooth and pre-process the data through the Kalman filter algorithm to avoid equipment damage caused by temperature anomalies. When temperature anomalies are detected, the charging power will be automatically adjusted. The voltage / current sensor is installed on the energy storage unit, and the system is connected to the tram's on-board system through a communication module to obtain the battery power and voltage; power scheduling is achieved through the fuzzy control algorithm Function: The algorithm automatically optimizes the power flow and scheduling strategy based on the real-time data of the battery status of the charging node, the energy storage status in the area, the grid load and electricity price. The charging and discharging process of the energy storage unit is managed by a bidirectional DC-DC converter, and the charging or discharging power of the energy storage unit is adjusted according to the grid load condition. When the grid load is peak, power is provided by the energy storage unit first, and when the grid load is low, power is supplied by the grid. When the electricity price is low, the energy storage unit is charged. The energy storage units can transmit power to each other. When the energy storage unit does not have enough power to fully charge the battery, the power is supplemented by the charging node.

2. The distributed energy storage wireless intelligent charging system based on the Internet of Things according to claim 1 is characterized in that: The system comes with a mobile app that provides the following features: A. Push real-time electricity price change information based on grid load and energy storage status; B. Predict charging completion time based on current battery SOC and charging power; C. Calculate the charging fee based on the charging time and electricity price, and provide the user with a detailed fee statement after the charging is completed; D. Real-time display of battery charging status, health report, charging history, and temperature curve information.

3. The distributed energy storage wireless intelligent charging system based on the Internet of Things according to claim 2 is characterized in that: The process of charging node replenishing power is as follows: S1: It is detected that the power of the energy storage unit in the charging node is insufficient to complete the charge, and the charging node sends information to the IoT data center; S2. The IoT data center sends information to other charging nodes to obtain the node status, and determines whether the charging node is charging and whether the energy storage unit has power. S2. If the energy storage power supply of the charging node has no power, the node does not need to transmit power to the power-deficient node; S3. If the energy storage unit of the charging node has power and the charging node is charging, determine whether the energy storage power supply of the charging node has any remaining power after charging is completed; S4. If the energy storage power source of the charging node has remaining power after charging is completed, the energy less than the remaining power will be transmitted to the node with power shortage. If there is no remaining power, the node does not need to transmit power to the node with power shortage. S5. If the charging node is no longer charging and there is surplus power in the energy storage unit, the node needs to transmit power to the power-deficient node.

4. A charging control method applied to the distributed energy storage wireless intelligent charging system based on the Internet of Things according to any one of claims 1 to 3, characterized in that: The following steps are involved: A. Obtain the battery's state of charge, maximum battery capacity, charging pile temperature, energy storage unit power, and grid load information; B. Select the appropriate charging mode based on charging demand and energy storage status, and dynamically adjust the charging power according to the battery SOC; C. Monitor the device temperature and voltage in real time during the charging process. If the device temperature is abnormal or the battery SOC is close to overcharge, the system will automatically reduce the power or suspend charging.

5. A power scheduling method applied to a distributed energy storage wireless intelligent charging system based on the Internet of Things as described in any one of claims 1 to 3, characterized in that: The following steps are involved: A. The node generates a power demand report based on the battery SOC, energy storage unit power, and grid load information, and publishes it to other nodes in the region through the IoT gateway; B. Based on the current grid load conditions, a fuzzy control algorithm is used for power dispatching. During peak hours, energy storage units are given priority for discharge. C. During low electricity consumption periods, energy storage units are dispatched for charging via smart contracts. D. Power transmission between nodes through regional mesh topology.

6. The power dispatching method according to claim 5, characterized in that: During the power scheduling process, the system dynamically adjusts the charging power and energy storage battery charging and discharging strategies, and gives priority to the energy storage unit during peak charging periods.

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