A fully flexible power distribution and switching charging stack

Abstract

This invention discloses a fully flexible power allocation and switching charging pile, relating to the field of charging pile technology. The fully flexible power allocation method is applied to the switching of charging piles and includes the following steps: Step 1: Real-time acquisition of voltage, current, and temperature data of the charging pile through a sensor network; real-time updating of the digital twin model based on grid load and user demand; simulation prediction using the digital twin model to optimize the power allocation strategy, predict potential faults in advance, and achieve proactive maintenance; Step 2: Equipping each charging terminal with an edge computing module. Through collaborative control based on digital twin technology and edge computing, the system can monitor and predict charging demand and grid load in real time, dynamically adjust the power allocation strategy, significantly improve charging efficiency and grid stability, and avoid resource waste or grid overload problems caused by fixed power in traditional charging piles.

Description

Technical Field

[0001] This invention relates to the field of charging pile technology, and in particular to a fully flexible power distribution and switching charging pile. Background Technology

[0002] With the increasing popularity of electric vehicles, the contradiction between their charging demand and the grid load is becoming more and more prominent.

[0003] As disclosed in CN118438914A, a distributed charging pile power scheduling method and a distributed charging pile are described. Adjacent charging piles exchange power resource information via communication lines. When a current charging pile starts charging a device, it acquires the device's current charging demand and allocates its output capacity based on that demand. If the current charging pile's output capacity is less than the current charging demand, it searches among adjacent charging piles for a first target charging pile that meets preset conditions. If a first target charging pile meets these conditions, the current charging pile sends a first power control command to the target charging pile, causing it to switch its output capacity to the current charging pile according to the command. This method eliminates the need to construct new high-power charging piles, enabling the upgrading and transformation of existing charging stations.

[0004] However, traditional charging piles have many problems. First, their power is fixed and cannot be flexibly adjusted according to actual charging demand and grid conditions, leading to resource waste or grid overload. Second, traditional power modules have low energy conversion efficiency, high energy loss, large equipment size, and high construction and operation costs, making it difficult to meet the growing charging demand of electric vehicles and the requirements for stable grid operation, and thus inconvenient to use. Therefore, we propose a fully flexible power distribution and switching charging pile to solve the above-mentioned technical problems. Summary of the Invention

[0005] The purpose of this invention is to provide a fully flexible power distribution and switching charging pile to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: the fully flexible power allocation method is applied to a fully flexible power allocation switching charging pile, and the fully flexible power allocation method includes the following steps:

[0007] Step 1: Collect voltage, current and temperature data of the charging pile in real time through sensor network, and update the digital twin model in real time by combining grid load and user demand. Use the digital twin model to perform simulation prediction, optimize power allocation strategy, predict potential faults in advance and realize proactive maintenance.

[0008] Step 2: Equip each charging terminal with an edge computing module to process local data in real time and execute control commands. Through collaborative communication between edge computing nodes, achieve dynamic power balance and fault isolation within the charging pile.

[0009] Step 3: Construct the state space, action space, and reward function of the charging environment, train the reinforcement learning model, and dynamically adjust the charging power and sequence based on real-time grid load and electricity price fluctuation information;

[0010] Step 4: Collect battery voltage, current and temperature data, train an AI model to predict SOH, and dynamically adjust charging current and voltage based on SOH prediction results to avoid overcharging or over-discharging the battery.

[0011] Step 5: Deploy 5G communication modules to enable high-speed data transmission between the charging pile and the cloud and user terminals, supporting remote monitoring, fault diagnosis and software upgrades, and improving the maintainability of the system.

[0012] In step one, the power allocation strategy is optimized based on the simulation results of the digital twin model. The optimization formula for the power allocation strategy is as follows:

[0013] ,in, The optimized power allocation scheme, Power allocation for the i-th charging terminal. Let i be the power requirement of the i-th charging terminal. These are weighting coefficients used for balancing power distribution and load balancing. This is a load imbalance function that measures the balance of power distribution.

[0014] In step three, the state space, action space, and reward function of the component charging environment are defined, and a reinforcement learning model is trained. The training formula for the reinforcement learning model is as follows:

[0015] ,in, Perform an action in state s The value function is denoted by r, where r represents the immediate rewards for charging efficiency and user satisfaction. As a discount factor, For the next state, For the next action, in step three, the charging power and sequence are dynamically adjusted based on real-time grid load and electricity price fluctuations. The formula for dynamically adjusting the charging power is: ,in For the optimized charging power, To adjust charging power Or charging sequence The action.

[0016] As a preferred option, in step one, more specifically, the voltage, current and temperature data of the charging pile are collected in real time through a sensor network, the collected data is uploaded to a digital twin platform, a digital twin model is constructed based on the topology and operating parameters of the physical charging pile, the digital twin model is used for simulation, future charging demand and grid load are predicted, and the power allocation strategy of the charging pile is dynamically adjusted according to the simulation results.

[0017] The update formula for the digital twin model is: ,in The state of the digital twin model at time t. The voltage at time t Current and temperature Sensor data, Charging power at time t and charging time user needs, Power of the grid at time t and power grid frequency The power grid load, This is the update function for the digital twin model.

[0018] As a preferred embodiment, in step two, the edge computing module of each charging terminal processes local data in real time, and the edge computing nodes exchange data through a communication network to achieve collaborative control. Based on the collaborative communication results, the output power of each charging terminal is dynamically adjusted, and when a fault is detected, the faulty node is automatically isolated to ensure stable operation.

[0019] Each charging terminal's edge module executes control commands based on local data, and the edge calculation formula is expressed as: ,in Charging power of the i-th charging terminal and charging status Control commands, For the local sensor data of the i-th charging terminal, Let be the local digital twin model state of the i-th charging terminal. This is the decision function for the edge computing module.

[0020] As a preferred embodiment, in step two, dynamic power balancing is achieved through collaborative communication between edge computing nodes. The dynamic power balancing formula is: ,in This represents the total power balance value of the charging pile. Let i be the output power of the i-th charging terminal. For the first Power loss.

[0021] As a preferred embodiment, in step four, the battery's state of health (SOH) is predicted using an AI model, and the prediction model formula is as follows: Where SOH represents the battery's state of health, with an output value ranging from 0 to 1, where 1 indicates fully healthy; V represents the battery voltage; I represents the battery current; and T represents the battery temperature. This is a prediction model for SOH.

[0022] As a preferred embodiment, in step four, the charging current and voltage need to be dynamically adjusted based on the SOH prediction results. This process is expressed as follows: ,in For the optimized charging current, For the optimized charging voltage, This is the proportionality coefficient.

[0023] As a preferred embodiment, in step five, the 5G communication module supports high-speed data transmission and enables remote monitoring and fault diagnosis through 5G communication.

[0024] As a preferred embodiment, the system includes a sensor unit comprising a voltage sensor, a current sensor, a temperature sensor, a humidity sensor, and a battery status detector.

[0025] A power conversion unit, the power conversion unit including an AD / DC converter and a DC / DC converter;

[0026] An edge computing node unit, comprising an ARM processor, an SSD memory, and a communication interface component;

[0027] The digital twin platform hardware unit includes a high-performance server for running digital twin models, a GPU accelerator card for supporting large-scale data simulation and machine learning computation, and a data storage device for storage.

[0028] A communication module unit, wherein the communication module unit includes a 5G communication module;

[0029] The user interaction unit includes at least one touchscreen.

[0030] The distributed energy interface unit includes a photovoltaic inverter, a wind power converter, and an energy management system (EMS).

[0031] As a preferred embodiment, the communication module unit further includes Ethernet and Wi-Fi.

[0032] Compared with the prior art, the present invention has significant advantages and beneficial effects. Specifically, as can be seen from the above technical solution, its main features are:

[0033] This invention enables a comprehensive improvement in the efficiency, flexibility, safety, and economy of charging infrastructure. Through collaborative control based on digital twin technology and edge computing, the system can monitor and predict charging demand and grid load in real time, dynamically adjusting power allocation strategies. This significantly improves charging efficiency and grid stability, avoiding resource waste or grid overload problems caused by fixed power in traditional charging piles. By introducing reinforcement learning and AI algorithms, the system can adaptively optimize charging strategies, extending the lifespan of electric vehicle batteries and achieving peak shaving and valley filling, thus reducing user charging costs. Furthermore, the use of high-efficiency power modules with wide bandgap semiconductor technology significantly improves energy conversion efficiency, reduces energy loss, and shrinks equipment size, lowering construction and operating costs. More importantly, through bidirectional energy interaction, electric vehicles become distributed energy storage units for the grid, further enhancing grid flexibility and the ability to absorb renewable energy. Through 5G / 6G ultra-low latency communication and quantum key distribution technology, the system achieves high-speed, secure remote control and data transmission, ensuring user privacy and data security.

[0034] To more clearly illustrate the structural features and effects of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the fully flexible power distribution method according to an embodiment of the present invention. Detailed Implementation

[0036] 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.

[0037] It should be noted that when a component is said to be "fixed to" another component, it can be directly attached to the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0038] Please see Figure 1This invention provides a fully flexible power allocation and switching charging pile, wherein the fully flexible power allocation method is applied to a fully flexible power allocation and switching charging pile, and the fully flexible power allocation method includes the following steps:

[0039] Step 1: Collect voltage, current and temperature data of the charging pile in real time through sensor network, and update the digital twin model in real time by combining grid load and user demand. Use the digital twin model for simulation prediction, optimize power allocation strategy, predict potential faults in advance and realize proactive maintenance; introduce digital twin technology to construct a virtual mirror model of the charging pile to map the operating status of the physical system in real time.

[0040] Step 2: Equip each charging terminal with an edge computing module to process local data and execute control commands in real time. Through collaborative communication between edge computing nodes, achieve dynamic power balance and fault isolation within the charging pile; reduce dependence on cloud computing, provide real-time performance and robustness of the system, and thus adapt to large-scale distributed charging scenarios.

[0041] Step 3: Construct the state space, action space, and reward function of the charging environment, train the reinforcement learning model, and dynamically adjust the charging power and sequence based on real-time grid load and electricity price fluctuation information; this can improve the economics of the charging pile and user satisfaction, achieve peak shaving and valley filling of the grid load, and have a more balanced effect.

[0042] Step 4: Collect battery voltage, current, and temperature data, train an AI model to predict SOH (State of Harm), and dynamically adjust charging current and voltage based on the SOH prediction results to avoid overcharging or over-discharging the battery. This step can improve battery life and reduce user costs.

[0043] Step 5: Deploy a 5G communication module to enable high-speed data transmission between the charging pile and the cloud and user terminals, supporting remote monitoring, fault diagnosis and software upgrades, and improving the maintainability of the system. This step can improve the response speed of the charging pile and the user experience, adapting to the needs of modern smart grids.

[0044] In step one, more specifically, the voltage, current and temperature data of the charging pile are collected in real time through a sensor network, and the collected data is uploaded to a digital twin platform. Based on the topology and operating parameters of the physical charging pile, a digital twin model is constructed, and simulation is performed using the digital twin model to predict future charging demand and grid load. Based on the simulation results, the power allocation strategy of the charging pile is dynamically adjusted.

[0045] The update formula for the digital twin model is: ,in The state of the digital twin model at time t. The voltage at time t Current and temperature Sensor data, Charging power at time t and charging time user needs, Power of the grid at time t and power grid frequency The power grid load, This is the update function for the digital twin model.

[0046] The power allocation strategy is optimized based on the simulation results of the digital twin model. The optimization formula for the power allocation strategy is as follows:

[0047] ,in, The optimized power allocation scheme, Power allocation for the i-th charging terminal. Let i be the power requirement of the i-th charging terminal. These are weighting coefficients used for balancing power distribution and load balancing. This is a load imbalance function that measures the balance of power distribution.

[0048] In step two, the edge computing module of each charging terminal processes local data in real time, and the edge computing nodes exchange data through a communication network to achieve collaborative control. Based on the collaborative communication results, the output power of each charging terminal is dynamically adjusted, and when a fault is detected, the faulty node is automatically isolated to ensure stable operation.

[0049] Each charging terminal's edge module executes control commands based on local data, and the edge calculation formula is expressed as: ,in Charging power of the i-th charging terminal and charging status Control commands, For the local sensor data of the i-th charging terminal, Let be the local digital twin model state of the i-th charging terminal. This is the decision function for the edge computing module;

[0050] Power dynamic balance is achieved through collaborative communication between edge computing nodes. The power dynamic balance formula is as follows: ,in This represents the total power balance value of the charging pile. Let i be the output power of the i-th charging terminal. For the first By reducing power loss and deploying edge computing nodes in the charging pile, localized intelligent decision-making and distributed collaborative control can be achieved, thereby reducing reliance on the cloud, improving the real-time performance and robustness of the system, and adapting to large-scale distributed charging scenarios.

[0051] In step three, the state space, action space, and reward function of the component charging environment are defined, and a reinforcement learning model is trained. The training formula for the reinforcement learning model is as follows:

[0052] ,in, Perform an action in state s The value function is denoted by r, where r represents the immediate rewards for charging efficiency and user satisfaction. As a discount factor, For the next state, For the next action, in step three, the charging power and sequence are dynamically adjusted based on real-time grid load and electricity price fluctuations. The formula for dynamically adjusting the charging power is: ,in For the optimized charging power, To adjust charging power Or charging sequence This action, through a reinforcement learning algorithm, achieves adaptive optimization of the charging strategy.

[0053] In step four, the battery's state of health (SOH) is predicted using an AI model. The prediction model formula is as follows: Where SOH represents the battery's state of health, with an output value ranging from 0 to 1, where 1 indicates fully healthy; V represents the battery voltage; I represents the battery current; and T represents the battery temperature. The SOH prediction model is a neural network that can reduce energy loss, shrink equipment size, improve convenience, and enhance the overall performance of the charging pile.

[0054] In step four, the charging current and voltage also need to be dynamically adjusted based on the SOH prediction results. This process is described as follows: ,in For the optimized charging current, For the optimized charging voltage, This is the proportionality coefficient.

[0055] In step five, the 5G communication module supports high-speed data transmission and enables remote monitoring and fault diagnosis through 5G communication.

[0056] The fully flexible power distribution and switching charging pile includes a sensor unit, which includes a voltage sensor for detecting voltage, a current sensor for detecting charging pile current, a temperature sensor for detecting charging pile temperature, a humidity sensor for detecting charging pile humidity, and a battery status detector for detecting battery status.

[0057] A power conversion unit, the power conversion unit including an AD / DC converter and a DC / DC converter;

[0058] An edge computing node unit includes an ARM processor for data processing, an SSD memory for data storage, and a communication interface component, wherein the communication module unit includes Ethernet and Wi-Fi.

[0059] The digital twin platform hardware unit includes a high-performance server for running digital twin models, a GPU accelerator card for supporting large-scale data simulation and machine learning computation, and a data storage device for storage.

[0060] A communication module unit, wherein the communication module unit includes a 5G communication module;

[0061] The user interaction unit includes at least one touch screen, and also includes a QR code scanner for user payment, and a microphone and speaker for voice interaction with the user.

[0062] The distributed energy interface unit includes a photovoltaic inverter, a wind power converter, and an energy management system (EMS).

[0063] In summary, this invention can achieve a comprehensive improvement in the efficiency, flexibility, safety, and economy of charging infrastructure;

[0064] Specifically, based on the collaborative control of digital twin technology and edge computing, the system can monitor and predict charging demand and grid load in real time, dynamically adjust power allocation strategies, significantly improve charging efficiency and grid stability, and avoid resource waste or grid overload problems caused by fixed power in traditional charging piles.

[0065] Secondly, by introducing reinforcement learning and AI algorithms, the system can adaptively optimize charging strategies, which not only extends the lifespan of electric vehicle batteries but also achieves peak shaving and valley filling, reducing charging costs for users. Furthermore, the high-efficiency power modules employing wide-bandgap semiconductor (SiC / GaN) technology significantly improve energy conversion efficiency, reduce energy loss, and simultaneously shrink equipment size, lowering construction and operating costs.

[0066] More importantly, this solution supports V2G (Vehicle-to-Grid) bidirectional energy interaction, enabling electric vehicles to become distributed energy storage units for the power grid, further enhancing the grid's flexibility and the ability to absorb renewable energy.

[0067] Through 5G / 6G ultra-low latency communication and quantum key distribution technology, the system achieves high-speed and secure remote control and data transmission, ensuring user privacy and data security.

[0068] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A fully flexible power distribution method, characterized in that: This includes a fully flexible power allocation method, which is applied to the fully flexible power allocation switching of charging piles. The fully flexible power allocation method includes the following steps: Step 1: Collect voltage, current and temperature data of the charging pile in real time through sensor network, and update the digital twin model in real time by combining grid load and user demand. Use the digital twin model to perform simulation prediction, optimize power allocation strategy, predict potential faults in advance and realize proactive maintenance. Step 2: Equip each charging terminal with an edge computing module to process local data and execute control commands in real time. Through collaborative communication between edge computing nodes, achieve dynamic power balance and fault isolation within the charging pile. Step 3: Construct the state space, action space, and reward function of the charging environment, train the reinforcement learning model, and dynamically adjust the charging power and sequence based on real-time grid load and electricity price fluctuation information; Step 4: Collect battery voltage, current and temperature data, train an AI model to predict SOH, and dynamically adjust charging current and voltage based on SOH prediction results to avoid overcharging or over-discharging the battery. Step 5: Deploy 5G communication modules to enable high-speed data transmission between the charging pile and the cloud and user terminals, supporting remote monitoring, fault diagnosis and software upgrades, and improving the maintainability of the system. In step one, the power allocation strategy is optimized based on the simulation results of the digital twin model. The optimization formula for the power allocation strategy is as follows: ,in, The optimized power allocation scheme, Power allocation for the i-th charging terminal. Let i be the power requirement of the i-th charging terminal. These are weighting coefficients used for balancing power distribution and load balancing. This is a load imbalance function that measures the balance of power distribution. In step three, the state space, action space, and reward function of the component charging environment are defined, and a reinforcement learning model is trained. The training formula for the reinforcement learning model is as follows: ,in, Perform an action in state s The value function is denoted by r, where r represents the immediate rewards for charging efficiency and user satisfaction. As a discount factor, For the next state, For the next action, in step three, the charging power and sequence are dynamically adjusted based on real-time grid load and electricity price fluctuations. The formula for dynamically adjusting the charging power is: ,in For the optimized charging power, To adjust charging power Or charging sequence action.

2. The fully flexible power distribution method according to claim 1, characterized in that: In step one, more specifically, the voltage, current and temperature data of the charging pile are collected in real time through a sensor network, and the collected data is uploaded to a digital twin platform. Based on the topology and operating parameters of the physical charging pile, a digital twin model is constructed, and simulation is performed using the digital twin model to predict future charging demand and grid load. Based on the simulation results, the power allocation strategy of the charging pile is dynamically adjusted. The update formula for the digital twin model is: ,in The state of the digital twin model at time t. The voltage at time t Current and temperature Sensor data, Charging power at time t and charging time user needs, Power of the grid at time t and power grid frequency The power grid load, This is the update function for the digital twin model.

3. The fully flexible power distribution method according to claim 1, characterized in that: In step two, the edge computing module of each charging terminal processes local data in real time, and the edge computing nodes exchange data through a communication network to achieve collaborative control. Based on the collaborative communication results, the output power of each charging terminal is dynamically adjusted, and when a fault is detected, the faulty node is automatically isolated to ensure stable operation. Each charging terminal's edge module executes control commands based on local data. The edge computing formula is expressed as: ,in Charging power of the i-th charging terminal and charging status Control commands, For the local sensor data of the i-th charging terminal, Let represent the local digital twin model state of the i-th charging terminal. This is the decision function for the edge computing module.

4. The fully flexible power distribution method according to claim 3, characterized in that: In step two, dynamic power balancing is achieved through collaborative communication between edge computing nodes. The dynamic power balancing formula is as follows: ,in This represents the total power balance value of the charging pile. Let i be the output power of the i-th charging terminal. For the first Power loss.

5. The fully flexible power distribution method according to claim 1, characterized in that: In step four, the battery's state of health (SOH) is predicted using an AI model. The prediction model formula is as follows: Where SOH represents the battery's state of health, V is the battery voltage, I is the battery current, and T is the battery temperature. This is a prediction model for SOH.

6. The fully flexible power distribution method according to claim 5, characterized in that: In step four, the charging current and voltage also need to be dynamically adjusted based on the SOH prediction results. This process is described as follows: ,in For the optimized charging current, For the optimized charging voltage, This is the proportionality coefficient.

7. The fully flexible power distribution method according to claim 1, characterized in that: In step five, the 5G communication module supports high-speed data transmission and enables remote monitoring and fault diagnosis through 5G communication.

8. A fully flexible power distribution switching charging pile, applied to the fully flexible power distribution method according to any one of claims 1 to 7, characterized in that: It includes a sensor unit, which includes a voltage sensor, a current sensor, a temperature sensor, a humidity sensor, and a battery status detector; A power conversion unit, the power conversion unit including an AD / DC converter and a DC / DC converter; An edge computing node unit, comprising an ARM processor, an SSD memory, and a communication interface component; The digital twin platform hardware unit includes a high-performance server for running digital twin models, a GPU accelerator card for supporting large-scale data simulation and machine learning computation, and a data storage device for storage. A communication module unit, wherein the communication module unit includes a 5G communication module; The user interaction unit includes at least one touchscreen. The distributed energy interface unit includes a photovoltaic inverter, a wind power converter, and an energy management system (EMS).

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