Intelligent charging pile control system and method based on dynamic power distribution

Abstract

The invention belongs to the technical field of charging pile control, and particularly relates to an intelligent charging pile control system and method based on dynamic power distribution, and the method comprises the steps: a slave control board collects battery data in real time, and uploads the battery data to a main control board; the main control board receives the battery data, predicts the maximum allowable charging power of the battery through an LSTM model, and determines the safety boundary of the battery according to the maximum allowable charging power and the safety operation parameters of the battery; generating a power distribution strategy by adopting a Nash equilibrium algorithm based on the battery safety boundary in combination with the power grid load and the user priority; issuing the power distribution strategy to an intelligent charging module; the intelligent charging module adjusts the switching frequency of the LLC resonant converter based on the power distribution strategy to realize target power output; the intelligent charging module monitors output current, voltage and temperature in real time and feeds back monitoring data to the main control board to form closed-loop control. And flexible control and data feedback of the charging module are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging pile control, and in particular relates to an intelligent charging pile control system and method based on dynamic power distribution. Background Art

[0002] With the increasing popularity of electric vehicles, the performance and efficiency of charging piles, as key infrastructure for recharging electric vehicles, are gaining increasing attention. Traditional charging pile control systems often use fixed power allocation strategies that are unable to dynamically adjust based on battery status, grid load, and user demand. This leads to problems such as low charging efficiency, shortened battery life, and poor user experience.

[0003] During battery charging, the maximum allowable charging power varies with changes in battery conditions (such as voltage, current, and temperature). If the charging power exceeds the maximum allowable value, it may cause battery overheating, overcharging, or even damage, affecting battery life and safety. Furthermore, fluctuations in the grid load can affect the power output of the charging station. If the grid load is too high, the charging station may not be able to output stable power, resulting in charging interruptions or reduced efficiency.

[0004] Furthermore, users have varying needs for charging stations. For example, some want a full charge as quickly as possible, while others prioritize cost. Traditional charging station control systems are unable to allocate power based on user priorities, resulting in a poor charging experience for some users. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides an intelligent charging pile control system and method based on dynamic power distribution to solve the above-mentioned technical problems.

[0006] In a first aspect, the technical solution of the present invention provides an intelligent charging pile control system based on dynamic power allocation, comprising a main control board, multiple slave control boards, and at least one intelligent charging module, wherein the slave control board is connected to the main control board via a bus communication connection for real-time battery data collection; the intelligent charging module is connected to the main control board via a multi-protocol communication interface; The main control board is integrated with edge computing module and dynamic power distribution module; Upload battery data from the control board to the main control board; The edge computing module receives battery data uploaded from the control board, predicts the maximum allowable charging power of the battery through the LSTM model, and determines the battery's safety boundary based on the maximum allowable charging power and the battery's safe operating parameters, and sends it to the dynamic power allocation module; The dynamic power allocation module generates a power allocation strategy based on the battery safety boundary provided by the edge computing module, combined with the grid load and user priority, using the Nash equilibrium algorithm, and sends it to the smart charging module through the multi-protocol communication interface; The intelligent charging module is used to receive the power allocation strategy and adjust the switching frequency of the LLC resonant converter based on the power allocation strategy to achieve the target power output; the intelligent charging module also monitors the output current, voltage and temperature in real time, and feeds back to the main control board through the multi-protocol communication interface to form a closed-loop control.

[0007] The edge computing module predicts the battery's maximum allowable charging power and dynamically allocates power based on the battery's safety margins. This enables efficient battery charging, improving charging speed while avoiding safety hazards such as overcharging. The intelligent charging module monitors output current, voltage, and temperature in real time and feeds this data back to the main control board, forming a closed-loop control loop. This allows the system to adjust power output promptly, ensuring the stability and reliability of the charging process. The main control board and intelligent charging module are connected via a multi-protocol communication interface, supporting multiple communication protocols. This enhances the system's flexibility and compatibility, facilitating communication and integration with diverse devices.

[0008] As a preferred embodiment of the technical solution of the present invention, a multi-protocol collaborative communication module is also integrated on the main control board to monitor the status of each channel in real time and trigger protocol switching when the following conditions are met at the same time: The current channel signal strength is less than a preset first threshold, and the packet loss rate within a set time period is greater than a preset first percentage; The signal strength of the target channel is greater than a preset second threshold, and the historical packet loss rate of the target channel is less than a preset second percentage.

[0009] The multi-protocol collaborative communication module monitors the status of each channel in real time and triggers protocol switching when specific conditions are met, selecting the optimal communication channel. This improves communication reliability and stability and reduces data transmission errors and packet loss. In complex electromagnetic environments or network conditions, protocol switching automatically selects a communication protocol with strong anti-interference capabilities and high transmission quality, ensuring system operation in a variety of environments and enhancing the system's environmental adaptability.

[0010] As a preferred embodiment of the technical solution of the present invention, the multi-protocol collaborative communication module adjusts the baud rate by monitoring the network load, specifically including: Count the ratio of the number of data packets per second to the maximum theoretical load of the network; Monitor the remaining capacity of the DMA buffer; When the network load is greater than a preset third percentage and persists for N sampling periods, the multi-protocol cooperative communication module increases the baud rate from the current value to the next level; after the baud rate is switched, if the CRC error rate is greater than or equal to a preset fourth percentage, the multi-protocol cooperative communication module returns the baud rate to the original baud rate; When the remaining capacity of the DMA buffer is less than the fifth percentage, the baud rate is forced to increase.

[0011] The baud rate is dynamically adjusted by monitoring network load. When network load is high, the baud rate is increased to speed up data transmission, improve communication efficiency, and reduce data transmission delays, thereby improving the response speed and operational efficiency of the entire system. After the baud rate is switched, a CRC error rate check is performed. If the error rate is too high, the system will fall back to the original baud rate, effectively avoiding data transmission errors caused by excessive baud rates and ensuring data integrity and accuracy.

[0012] When the remaining capacity of the DMA buffer is insufficient, the baud rate is forcibly triggered to increase, which can promptly relieve the network load pressure, avoid data backlog and system crash, and further improve the stability and reliability of the system.

[0013] As a preferred embodiment of the technical solution of the present invention, the edge computing module is specifically used for: Receive battery data uploaded from the control board, the battery data including battery voltage, current, temperature, battery status and battery health status; Based on the battery data, the maximum allowable charging power of the battery is predicted using a trained LSTM neural network model; determining a safety boundary of the battery based on the maximum allowable charging power and safe operating parameters of the battery, the safety boundary including an upper temperature limit, an upper voltage limit, an upper current limit, and a range of a battery state; The battery safety margin is sent to a dynamic power allocation module.

[0014] The edge computing module uses an LSTM neural network model to predict the battery's maximum allowable charging power and determine the battery's safety margins. This allows for more precise understanding of the battery's health status and safe operating range, thereby strictly controlling the battery's operating state during the charging process, avoiding problems such as overcharging, over-discharging, and overheating, extending battery life, and improving battery safety. The use of advanced LSTM neural network models to analyze and predict battery data enhances the system's intelligence, enabling it to dynamically adjust charging strategies based on the battery's actual status, achieving smarter and more efficient charging management.

[0015] As a preferred embodiment of the technical solution of the present invention, the dynamic power allocation module is specifically used to: Receive a battery safety boundary provided by an edge computing module, where the battery safety boundary includes a temperature upper limit, a voltage upper limit, a current upper limit, and a range of a battery state; Real-time acquisition of grid load information, including real-time power, voltage, current, and frequency of the grid; Real-time acquisition of user priority information, including user charging needs, reservation time, user level, and urgency; Based on the battery safety boundary, grid load information, and user priority information, a Nash equilibrium model is constructed. The goal of the Nash equilibrium model is to optimize the overall power distribution efficiency of the charging pile system while ensuring battery safety and user satisfaction. Calculating a real-time power allocation strategy using the Nash equilibrium model, the real-time power allocation strategy including the power allocation ratio and output power of each smart charging module; The real-time power allocation strategy is sent to the intelligent charging module through the multi-protocol collaborative communication module to guide the power output of the intelligent charging module.

[0016] The dynamic power allocation module combines battery safety margins, grid load, and user priorities, using a Nash equilibrium algorithm to generate a power allocation strategy. This module rationally allocates power based on actual user needs while ensuring battery safety and grid stability, improving the overall power allocation efficiency of the charging pile system and achieving optimal resource utilization. By factoring in user priority information, it prioritizes the charging needs of important users, improving user satisfaction with the charging service and enhancing the user experience. This power allocation strategy, based on the Nash equilibrium model, ensures that each intelligent charging module achieves its optimal state given the strategies of the other modules, avoiding conflicts and instabilities in the power allocation process and further enhancing system stability.

[0017] As a preferred embodiment of the technical solution of the present invention, the specific process of the dynamic power allocation module generating a power allocation strategy by constructing a Nash equilibrium model is as follows: According to the actual operating conditions of the charging pile system, the initial parameters of the Nash equilibrium model are set, including battery safety margins, grid load limits, user priority weights, and performance parameters of the smart charging module; Converting the battery safety boundary into constraint conditions, including the battery's upper temperature limit, upper voltage limit, upper current limit, and battery state range; Convert grid load information into system power constraints, including real-time power, voltage, current, and frequency limits of the grid; Convert user priority information into priority weights, including user charging needs, reservation time, user level, and urgency; defining a utility function for each smart charging module, the utility function being based on charging efficiency, user satisfaction, and system stability; Incorporating the constraints and priority weights into a utility function to form a multi-objective optimization function; Using an iterative algorithm to solve the Nash equilibrium model and find the optimal power allocation strategy for each smart charging module; In each iteration, the power allocation ratio of each smart charging module is updated until a Nash equilibrium state is reached, that is, the utility function of each module cannot be further optimized given the strategies of other modules; Based on the solution results, the real-time power allocation ratio and output power of each intelligent charging module are generated; Verify the generated power allocation strategy to ensure its feasibility under actual operating conditions; If the verification fails, adjust the model parameters or optimize the algorithm to solve the Nash equilibrium again.

[0018] By setting the initial parameters of the Nash equilibrium model in detail and incorporating battery safety margins, grid load information, and user priority information into the model for comprehensive analysis and optimization, a more accurate and reasonable power allocation strategy can be generated, further improving the operating efficiency and performance of the charging pile system. During the strategy generation process, the generated power allocation strategy is verified. If the verification fails, the model parameters are adjusted or the optimization algorithm is re-solved. This allows the system to flexibly adjust the power allocation strategy according to different operating conditions and requirements, enhancing the system's adaptability to various complex situations.

[0019] As a preferred embodiment of the technical solution of the present invention, the intelligent charging module is specifically used for: Receiving a power allocation strategy issued by the main control board through a multi-protocol communication interface; parsing the power allocation strategy to extract a target power output value and related control parameters, wherein the control parameters include a switching frequency of an LLC resonant converter; According to the control parameters obtained by analysis, the switching frequency of the LLC resonant converter is adjusted to adjust the power conversion efficiency and output power; Real-time monitoring of output current, voltage and temperature, which is accomplished through built-in current sensor, voltage sensor and temperature sensor; The monitored current, voltage, and temperature data are fed back to the main control board via a multi-protocol communication interface. The main control board then evaluates the operating status of the intelligent charging module based on the fed-back monitoring data. If the monitoring data exceeds the preset safety range or deviates from the target power output value by more than a deviation threshold, the main control board adjusts the power allocation strategy and re-sends the data to the intelligent charging module. The intelligent charging module adjusts the power output according to the new power allocation strategy to form a closed-loop control.

[0020] The intelligent charging module can accurately receive and analyze the power allocation strategy, and adjust the switching frequency of the LLC resonant converter according to the strategy to achieve precise target power output, thereby improving the power control accuracy and performance of the charging module and ensuring the efficiency and stability of the charging process.

[0021] Real-time monitoring of output current, voltage, and temperature, along with data feedback to the main control board, allows the board to promptly assess the operating status of the intelligent charging module and make adjustments. This creates a closed-loop control system, enhancing system reliability and fault recovery capabilities, enabling timely detection and resolution of potential issues and preventing them from escalating. The main control board adjusts the power allocation strategy based on this feedback data and re-distributes it to the intelligent charging module, which then adjusts power output according to the new strategy. This enables dynamic system optimization and self-adjustment, ensuring the system is always operating optimally and improving overall system performance and efficiency.

[0022] In a second aspect, the technical solution of the present invention further provides a smart charging pile control method based on dynamic power allocation, comprising the following steps: S1. Collect battery data from the control board in real time and upload the battery data to the main control board; S2. The edge computing module on the main control board receives the battery data uploaded from the control board, predicts the maximum allowable charging power of the battery through the LSTM model, and determines the safety boundary of the battery based on the maximum allowable charging power and the battery's safe operating parameters; The dynamic power allocation module on the S3 main control board generates a power allocation strategy using the Nash equilibrium algorithm based on the battery safety boundary provided by the edge computing module, combined with the grid load and user priority. S4. The main control board sends the power allocation strategy to the intelligent charging module through the multi-protocol communication interface; S5. The intelligent charging module receives the power allocation strategy and adjusts the switching frequency of the LLC resonant converter based on the power allocation strategy to achieve the target power output; S6. The intelligent charging module monitors the output current, voltage and temperature in real time, and feeds the monitoring data back to the main control board through the multi-protocol communication interface to form a closed-loop control.

[0023] This control method achieves efficient charging management for smart charging piles by collecting real-time battery data from the control panel, combining predictions from the edge computing module with strategy generation from the dynamic power allocation module. This method dynamically adjusts charging power based on battery status and user needs, improving charging efficiency and user experience. From battery data collection to the generation and execution of power allocation strategies, the entire process strictly considers the battery's safety boundaries and operating parameters, ensuring a safe charging process and avoiding battery damage and safety accidents caused by improper charging.

[0024] By utilizing advanced technologies such as the LSTM model and Nash equilibrium algorithm, intelligent control and management of the charging pile system is achieved, enabling the system to automatically adapt to different operating conditions and user needs, thereby improving the system's intelligence level and competitiveness.

[0025] As a preferred embodiment of the technical solution of the present invention, before step S2, the following steps are further included: The multi-protocol collaborative communication module on the main control board monitors the status of each channel in real time and triggers protocol switching when the following conditions are met at the same time: the current channel signal strength is less than the preset first threshold, and the packet loss rate within the set time period is greater than the preset first percentage; the signal strength of the target channel is greater than the preset second threshold, and the historical packet loss rate of the target channel is less than the preset second percentage.

[0026] Incorporating channel status monitoring and protocol switching steps from a multi-protocol collaborative communication module into the control method enables real-time monitoring of channel quality during communication and switching to a more optimal communication protocol when necessary. This effectively improves communication stability and reliability, reducing the risk of communication interruptions and data transmission errors. This enables the system to better adapt to complex communication environments and network changes, enhancing its environmental adaptability and robustness, ensuring stable operation in a variety of complex situations and improving system availability and reliability.

[0027] As a preferred embodiment of the technical solution of the present invention, the method further comprises: The multi-protocol collaborative communication module adjusts the baud rate by monitoring the network load, including: Count the ratio of the number of data packets per second to the maximum theoretical load of the network; Monitor the remaining capacity of the DMA buffer; When the network load is greater than a preset third percentage and persists for N sampling periods, the multi-protocol cooperative communication module increases the baud rate from the current value to the next level; after the baud rate is switched, if the CRC error rate is greater than or equal to a preset fourth percentage, the multi-protocol cooperative communication module returns the baud rate to the original baud rate; When the remaining capacity of the DMA buffer is less than a preset fifth percentage, the baud rate is forcibly triggered to increase.

[0028] By dynamically adjusting the baud rate by monitoring network load and performing CRC error rate detection and a fallback mechanism after baud rate switching, communication efficiency can be effectively improved, data transmission delays and errors can be reduced, and the system's responsiveness and operational efficiency can be further enhanced. The baud rate adjustment strategy takes into account multiple factors such as network load and remaining DMA buffer capacity. It can flexibly adjust the baud rate while ensuring data transmission quality, avoiding data transmission issues caused by excessively high or low baud rates and ensuring data integrity and accuracy. When the remaining DMA buffer capacity is insufficient, a baud rate increase is forcibly triggered to alleviate network load pressure, avoiding data backlogs and system crashes, further enhancing system stability and reliability, and improving overall system performance.

[0029] As a preferred embodiment of the technical solution of the present invention, step S2 specifically includes the following steps: S21. The edge computing module receives battery data uploaded from the control board, where the battery data includes battery voltage, current, temperature, battery status, and battery health status. S22. The edge computing module predicts the maximum allowable charging power of the battery based on the battery data using a trained LSTM neural network model; S23. The edge computing module determines the safety boundary of the battery based on the maximum allowable charging power and the safe operating parameters of the battery. The safety boundary includes the upper temperature limit, the upper voltage limit, the upper current limit, and the range of the battery state. S24. The edge computing module sends the battery safety boundary to the dynamic power allocation module.

[0030] As a preferred embodiment of the technical solution of the present invention, step S3 specifically includes the following steps: S31. The dynamic power allocation module receives a battery safety boundary provided by the edge computing module, where the battery safety boundary includes a temperature upper limit, a voltage upper limit, a current upper limit, and a range of a battery state. S32. The dynamic power allocation module obtains grid load information in real time, where the grid load information includes real-time power, voltage, current, and frequency of the grid; S33. The dynamic power allocation module obtains user priority information in real time, wherein the user priority information includes the user's charging demand, reservation time, user level, and urgency; S34. The dynamic power allocation module constructs a Nash equilibrium model based on the battery safety boundary, grid load information, and user priority information. The goal of the Nash equilibrium model is to optimize the overall power allocation efficiency of the charging pile system while ensuring battery safety and user satisfaction. S35. The dynamic power allocation module calculates a real-time power allocation strategy using the Nash equilibrium model. The real-time power allocation strategy includes a power allocation ratio and output power of each smart charging module. S36. The dynamic power allocation module sends the real-time power allocation strategy to the intelligent charging module through the multi-protocol collaborative communication module to guide the power output of the intelligent charging module.

[0031] As a preferred embodiment of the technical solution of the present invention, step S34 specifically includes the following steps: S341. According to the actual operating conditions of the charging pile system, set the initial parameters of the Nash equilibrium model, including the battery safety margin, the grid load limit, the user priority weight, and the performance parameters of the smart charging module; S342, converting the battery safety boundary into constraint conditions, including the battery's upper temperature limit, upper voltage limit, upper current limit, and battery state range; S343, converting the grid load information into system power constraints, including real-time power, voltage, current, and frequency constraints of the grid; S344, converting the user priority information into a priority weight, including the user's charging demand, reservation time, user level, and urgency; S345. Define a utility function for each smart charging module, where the utility function is based on charging efficiency, user satisfaction, and system stability; incorporate the constraints and priority weights into the utility function to form a multi-objective optimization function; S346. Solve the Nash equilibrium model using an iterative algorithm to find the optimal power allocation strategy for each smart charging module; S347. In each iteration, the power allocation ratio of each smart charging module is updated until a Nash equilibrium state is reached, that is, the utility function of each module cannot be further optimized given the strategies of other modules; S348. Generate a real-time power allocation ratio and output power for each intelligent charging module based on the solution results; and verify the generated power allocation strategy to ensure its feasibility under actual operating conditions. S349. If the verification fails, adjust the model parameters or optimize the algorithm to solve the Nash equilibrium again.

[0032] As a preferred embodiment of the technical solution of the present invention, step S5 specifically includes the following steps: S51. The intelligent charging module receives the power allocation strategy issued by the main control board through the multi-protocol communication interface; S52: The intelligent charging module analyzes the power allocation strategy and extracts the target power output value and related control parameters, wherein the control parameters include the switching frequency of the LLC resonant converter; S53, the intelligent charging module adjusts the switching frequency of the LLC resonant converter according to the control parameters obtained by the analysis to adjust the power conversion efficiency and output power; S54, the intelligent charging module monitors the output current, voltage and temperature in real time, and the monitoring is completed by the built-in current sensor, voltage sensor and temperature sensor; S55. The intelligent charging module feeds the monitored current, voltage, and temperature data back to the main control board via the multi-protocol communication interface. The main control board evaluates the operating status of the intelligent charging module based on the fed-back monitoring data. If the monitoring data exceeds a preset safety range or deviates from the target power output value by more than a deviation threshold, the main control board adjusts the power allocation strategy and re-sends the data to the intelligent charging module. S56. The intelligent charging module adjusts the power output according to the new power distribution strategy to form a closed-loop control.

[0033] The beneficial effects of this invention lie in that, by integrating an edge computing module and a dynamic power allocation module, real-time monitoring of battery status and dynamic power allocation are achieved, improving charging efficiency and battery life. An LSTM model is used to predict the battery's maximum allowable charging power, ensuring the safety and reliability of the charging process. Connecting the intelligent charging module via a multi-protocol communication interface enables flexible control and data feedback of the charging module, forming a closed-loop control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0035] Figure 1 A schematic block diagram of a system according to an embodiment of the present invention.

[0036] Figure 2 A schematic flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the specific embodiments. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0038] like Figure 1As shown, an embodiment of the present invention provides an intelligent charging pile control system based on dynamic power allocation, including a main control board, multiple slave control boards and at least one intelligent charging module. The slave control board is connected to the main control board via a bus communication and is used to collect battery data in real time; the intelligent charging module is connected to the main control board via a multi-protocol communication interface; The main control board is integrated with edge computing module and dynamic power distribution module; Upload battery data from the control board to the main control board; The edge computing module receives battery data uploaded from the control board, predicts the maximum allowable charging power of the battery through the LSTM model, and determines the battery's safety boundary based on the maximum allowable charging power and the battery's safe operating parameters, and sends it to the dynamic power allocation module; The dynamic power allocation module generates a power allocation strategy based on the battery safety boundary provided by the edge computing module, combined with the grid load and user priority, using the Nash equilibrium algorithm, and sends it to the smart charging module through the multi-protocol communication interface; The intelligent charging module is used to receive the power allocation strategy and adjust the switching frequency of the LLC resonant converter based on the power allocation strategy to achieve the target power output; the intelligent charging module also monitors the output current, voltage and temperature in real time, and feeds back to the main control board through the multi-protocol communication interface to form a closed-loop control.

[0039] In this embodiment of the present invention, the main control board (MCB) is the system's core control unit, integrating an edge computing module and a dynamic power allocation module. It receives battery data uploaded by the slave control board via bus communication and communicates with the intelligent charging module via a multi-protocol communication interface. The slave control board is responsible for collecting real-time data such as battery voltage, current, temperature, state of battery (SOC), and state of health (SOH), and then uploads this data to the MCB via bus communication.

[0040] The edge computing module receives battery data uploaded from the control board and predicts the maximum allowable charging power of the battery using the trained LSTM neural network model. The specific formula is as follows:

[0041] In the formula, is the maximum allowable charging power of the battery, They are the voltage, current and temperature of the battery respectively, SOC and SOH are the battery state and battery health status respectively, Represents the prediction function of the LSTM model.

[0042] According to the maximum allowable charging power and the battery's safe operating parameters (such as the upper temperature limit , voltage upper limit , current upper limit and SOC range), determine the battery's safety boundaries, and send these boundaries to the dynamic power allocation module.

[0043] The dynamic power allocation module receives the battery safety boundary provided by the edge computing module and combines it with the grid load information (such as the rated power of the grid) ,Voltage , current and frequency ) and user priority information (such as user charging needs , appointment time t reservation, user level and urgency), and uses the Nash equilibrium algorithm to generate a real-time power allocation strategy.

[0044] After receiving the power allocation strategy, the intelligent charging module analyzes the target power output value in the strategy and the switching frequency of the LLC resonant converter According to these parameters, the switching frequency of the LLC resonant converter is adjusted to achieve the target power output. At the same time, the output current is monitored in real time. ,Voltage and temperature And feed back these data to the main control board through the multi-protocol communication interface.

[0045] In some embodiments, a multi-protocol collaborative communication module is also integrated on the main control board for real-time monitoring of the status of each channel, and triggering protocol switching when the following conditions are met at the same time: the current channel signal strength is less than a preset first threshold, and the packet loss rate within a set time period is greater than a preset first percentage; the signal strength of the target channel is greater than a preset second threshold, and the historical packet loss rate of the target channel is less than a preset second percentage.

[0046] The multi-protocol collaborative communication module on the main control board monitors the status of each channel in real time, including the signal strength of the current channel and packet loss rate , and the signal strength of the target channel and the historical packet loss rate of the target channel The protocol switch is triggered when the following conditions are met: <-75dBm, and >5% for 3 seconds; >-85dBm and <2% of the target channel.

[0047] In some embodiments, the multi-protocol cooperative communication module adjusts the baud rate by monitoring the network load, specifically including: Statistics of the number of packets per second The maximum theoretical carrying capacity of the network load The ratio of Monitor the remaining capacity of the DMA buffer ; When the network load is greater than 70% and lasts for three sampling periods, the multi-protocol cooperative communication module increases the baud rate from the current value to the next gear; after the baud rate is switched, if the CRC error rate is greater than or equal to the preset 0.1%, the multi-protocol cooperative communication module will roll back the baud rate to the original baud rate; When the remaining capacity of the DMA buffer is less than 10%, the baud rate is forced to increase.

[0048] In some embodiments, the edge computing module is specifically configured to: Receive battery data uploaded from the control board, the battery data including battery voltage, current, temperature, battery status and battery health status; Based on the battery data, the maximum allowable charging power of the battery is predicted using a trained LSTM neural network model; It should be noted that the following battery data is collected in real time from the control board: Voltage V: The current voltage value of the battery.

[0049] Current I: The current value of the battery.

[0050] Temperature T: The current temperature of the battery.

[0051] Battery State (SOC): The percentage of remaining battery power.

[0052] Battery State of Health (SOH): An indicator of the battery's health status, typically a value between 0 and 1.

[0053] In order to improve the training efficiency and prediction accuracy of the model, it is usually necessary to normalize the input data.

[0054] The normalized data is used as input to the trained LSTM model. The input of the LSTM model is usually time series data, so the real-time battery data needs to be structured into a time series format.

[0055] Assuming the time step Collect data and construct a time series : Among them, each is a vector containing voltage, current, temperature, SOC and SOH: .

[0056] The time series Input into the trained LSTM model, the model will output the predicted maximum allowed charging power .

[0057] An LSTM model typically consists of multiple LSTM layers and an output layer. Each LSTM layer contains multiple neurons, enabling it to capture long-term dependencies in time series. The output layer is typically a fully connected layer that maps the LSTM layer output to a target value (i.e., the maximum allowable charging power).

[0058] The predicted value of the model output usually needs to be denormalized to restore it to the original physical dimension. is the normalized value, and the denormalization formula is as follows:

[0059] Where, and are the historical maximum and minimum values of the maximum allowed charging power respectively.

[0060] Determine the safety boundary of the battery based on the maximum allowable charging power and the safe operating parameters of the battery, wherein the safety boundary includes the upper temperature limit, the upper voltage limit, the upper current limit and the range of the battery state; and the safe operating parameters of the battery to determine the safety boundary of the battery.

[0061] The battery safety margin is sent to a dynamic power allocation module.

[0062] In some embodiments, the dynamic power allocation module is specifically configured to: Receive a battery safety boundary provided by an edge computing module, where the battery safety boundary includes a temperature upper limit, a voltage upper limit, a current upper limit, and a range of a battery state; Real-time acquisition of grid load information, including real-time power, voltage, current, and frequency of the grid; Real-time acquisition of user priority information, including user charging needs, reservation time, user level, and urgency; Based on the battery safety boundary, grid load information, and user priority information, a Nash equilibrium model is constructed. The goal of the Nash equilibrium model is to optimize the overall power distribution efficiency of the charging pile system while ensuring battery safety and user satisfaction. Calculating a real-time power allocation strategy using the Nash equilibrium model, the real-time power allocation strategy including the power allocation ratio and output power of each smart charging module; The real-time power allocation strategy is sent to the intelligent charging module through the multi-protocol collaborative communication module to guide the power output of the intelligent charging module.

[0063] In some embodiments, the specific process of the dynamic power allocation module generating the power allocation strategy by constructing the Nash equilibrium model is as follows: Define model parameters and variables: Determine model variables, such as the power allocation ratio and output power of each smart charging module. Set model parameters, including battery safety boundaries (such as upper temperature limits, voltage limits, and current limits), grid load information (such as real-time power, voltage, current, and frequency), and user priority information (such as charging demand, reservation time, user level, and urgency).

[0064] Create constraints: Based on battery safety boundaries, battery-related constraints are established, such as ensuring that power output cannot exceed the battery's temperature, voltage, and current limits. Based on grid load information, grid-related constraints are established, such as ensuring that total power output cannot exceed the grid's carrying capacity. Based on user priority information, user-related constraints are established, such as ensuring that high-priority users receive more power.

[0065] Temperature constraint: battery temperature T Do not exceed the upper temperature limit .

[0066] Voltage constraint: The battery voltage V cannot exceed the voltage upper limit . Current constraint: The battery current I cannot exceed the current limit .

[0067] Power constraint: The battery's power output P cannot exceed the maximum allowable charging power .

[0068] Total power constraint: the total power output of all smart charging modules The carrying capacity of the power grid cannot be exceeded .

[0069]

[0070] in, N is the number of smart charging modules, It is i The power output of each smart charging module.

[0071] Priority constraint: High-priority users should get more power allocation. This can be achieved by introducing priority weights. to achieve, where Indicates thei The priority weight of each user.

[0072]

[0073] is the minimum power allocation for each user.

[0074] Define the utility function: Define a utility function for each smart charging module , the function should comprehensively consider charging efficiency, user satisfaction and system stability. The utility function can include: Power output efficiency , indicating the i The power output efficiency of a smart charging module.

[0075] User waiting time , indicating the i The waiting time of each user.

[0076] User Priority , indicating the i The priority weight of each user.

[0077] The utility function can be expressed as:

[0078] in, and It is a parameter to adjust the weight of each factor.

[0079] Construct a multi-objective optimization function: The above constraints and utility functions are combined to construct a multi-objective optimization function. The goal of this optimization function is to maximize the utility functions of all smart charging modules while satisfying all constraints.

[0080] The multi-objective optimization function can be expressed as:

[0081] Select an optimization algorithm: Select an appropriate optimization algorithm to solve the Nash equilibrium model, such as an iterative algorithm (such as gradient descent, Newton's method, etc.) or other heuristic algorithms (such as genetic algorithm, particle swarm optimization, etc.).

[0082] Solve for Nash equilibrium: Using the selected optimization algorithm, the Nash equilibrium model is iteratively solved to find the optimal power allocation strategy for each smart charging module. In each iteration, the power allocation ratio of each smart charging module is updated until a Nash equilibrium is reached, meaning that the utility function of each module cannot be further optimized given the strategies of other modules.

[0083] The generated power allocation strategy is verified to ensure its feasibility under actual operating conditions. If the verification fails, the model parameters or optimization algorithm are adjusted to solve the Nash equilibrium again.

[0084] Based on the solution, the real-time power allocation ratio and output power of each smart charging module are generated. These strategies are then sent to the smart charging modules through the multi-protocol collaborative communication module to guide their power output.

[0085] Set the initial power allocation strategy .

[0086] The iterative update formula can be expressed as:

[0087] Where k represents the number of iterations, represents the power allocation strategy of the j-th smart charging module at the k-th iteration.

[0088] In some embodiments, the intelligent charging module is specifically configured to: Receiving a power allocation strategy issued by the main control board through a multi-protocol communication interface; parsing the power allocation strategy to extract a target power output value and related control parameters, wherein the control parameters include a switching frequency of an LLC resonant converter; According to the control parameters obtained by analysis, the switching frequency of the LLC resonant converter is adjusted to adjust the power conversion efficiency and output power; Real-time monitoring of output current, voltage and temperature, which is accomplished through built-in current sensor, voltage sensor and temperature sensor; The monitored current, voltage, and temperature data are fed back to the main control board via a multi-protocol communication interface. The main control board then evaluates the operating status of the intelligent charging module based on the fed-back monitoring data. If the monitoring data exceeds the preset safety range or deviates from the target power output value by more than a deviation threshold, the main control board adjusts the power allocation strategy and re-sends the data to the intelligent charging module. The intelligent charging module adjusts the power output according to the new power allocation strategy to form a closed-loop control.

[0089] like Figure 2 As shown, in an embodiment of the present invention, a smart charging pile control method based on dynamic power allocation is characterized by comprising the following steps: S1. Collect battery data from the control board in real time and upload the battery data to the main control board; S2. The edge computing module on the main control board receives the battery data uploaded from the control board, predicts the maximum allowable charging power of the battery through the LSTM model, and determines the safety boundary of the battery based on the maximum allowable charging power and the battery's safe operating parameters; The dynamic power allocation module on the S3 main control board generates a power allocation strategy using the Nash equilibrium algorithm based on the battery safety boundary provided by the edge computing module, combined with the grid load and user priority. S4. The main control board sends the power allocation strategy to the intelligent charging module through the multi-protocol communication interface; S5. The intelligent charging module receives the power allocation strategy and adjusts the switching frequency of the LLC resonant converter based on the power allocation strategy to achieve the target power output; S6. The intelligent charging module monitors the output current, voltage and temperature in real time, and feeds the monitoring data back to the main control board through the multi-protocol communication interface to form a closed-loop control.

[0090] In some embodiments, before step S2, the method further includes the following steps: The multi-protocol collaborative communication module on the main control board monitors the status of each channel in real time and triggers protocol switching when the following conditions are met at the same time: the current channel signal strength is less than the preset first threshold, and the packet loss rate within the set time period is greater than the preset first percentage; the signal strength of the target channel is greater than the preset second threshold, and the historical packet loss rate of the target channel is less than the preset second percentage.

[0091] In some embodiments, the method further comprises: The multi-protocol collaborative communication module adjusts the baud rate by monitoring the network load, including: Count the ratio of the number of data packets per second to the maximum theoretical load of the network; Monitor the remaining capacity of the DMA buffer; When the network load is greater than a preset third percentage and persists for N sampling periods, the multi-protocol cooperative communication module increases the baud rate from the current value to the next level; after the baud rate is switched, if the CRC error rate is greater than or equal to a preset fourth percentage, the multi-protocol cooperative communication module returns the baud rate to the original baud rate; When the remaining capacity of the DMA buffer is less than a preset fifth percentage, the baud rate is forcibly triggered to increase.

[0092] In some embodiments, step S2 specifically includes the following steps: S21. The edge computing module receives battery data uploaded from the control board, where the battery data includes battery voltage, current, temperature, battery status, and battery health status. S22. The edge computing module predicts the maximum allowable charging power of the battery based on the battery data using a trained LSTM neural network model; S23. The edge computing module determines the safety boundary of the battery based on the maximum allowable charging power and the safe operating parameters of the battery. The safety boundary includes the upper temperature limit, the upper voltage limit, the upper current limit, and the range of the battery state. S24. The edge computing module sends the battery safety boundary to the dynamic power allocation module.

[0093] In some embodiments, step S3 specifically includes the following steps: S31. The dynamic power allocation module receives a battery safety boundary provided by the edge computing module, where the battery safety boundary includes a temperature upper limit, a voltage upper limit, a current upper limit, and a range of a battery state. S32. The dynamic power allocation module obtains grid load information in real time, where the grid load information includes real-time power, voltage, current, and frequency of the grid; S33. The dynamic power allocation module obtains user priority information in real time, wherein the user priority information includes the user's charging demand, reservation time, user level, and urgency; S34. The dynamic power allocation module constructs a Nash equilibrium model based on the battery safety boundary, grid load information, and user priority information. The goal of the Nash equilibrium model is to optimize the overall power allocation efficiency of the charging pile system while ensuring battery safety and user satisfaction. S35. The dynamic power allocation module calculates a real-time power allocation strategy using the Nash equilibrium model. The real-time power allocation strategy includes a power allocation ratio and output power of each smart charging module. S36. The dynamic power allocation module sends the real-time power allocation strategy to the intelligent charging module through the multi-protocol collaborative communication module to guide the power output of the intelligent charging module.

[0094] In some embodiments, step S34 specifically includes the following steps: S341. According to the actual operating conditions of the charging pile system, set the initial parameters of the Nash equilibrium model, including the battery safety margin, the grid load limit, the user priority weight, and the performance parameters of the smart charging module; S342, converting the battery safety boundary into constraint conditions, including the battery's upper temperature limit, upper voltage limit, upper current limit, and battery state range; S343, converting the grid load information into system power constraints, including real-time power, voltage, current, and frequency constraints of the grid; S344, converting the user priority information into a priority weight, including the user's charging demand, reservation time, user level, and urgency; S345. Define a utility function for each smart charging module, where the utility function is based on charging efficiency, user satisfaction, and system stability; incorporate the constraints and priority weights into the utility function to form a multi-objective optimization function; S346. Solve the Nash equilibrium model using an iterative algorithm to find the optimal power allocation strategy for each smart charging module; S347. In each iteration, the power allocation ratio of each smart charging module is updated until a Nash equilibrium state is reached, that is, the utility function of each module cannot be further optimized given the strategies of other modules; S348. Generate a real-time power allocation ratio and output power for each intelligent charging module based on the solution results; and verify the generated power allocation strategy to ensure its feasibility under actual operating conditions. S349. If the verification fails, adjust the model parameters or optimize the algorithm to solve the Nash equilibrium again.

[0095] In some embodiments, step S5 specifically includes the following steps: S51. The intelligent charging module receives the power allocation strategy issued by the main control board through the multi-protocol communication interface; S52: The intelligent charging module analyzes the power allocation strategy and extracts the target power output value and related control parameters, wherein the control parameters include the switching frequency of the LLC resonant converter; S53, the intelligent charging module adjusts the switching frequency of the LLC resonant converter according to the control parameters obtained by the analysis to adjust the power conversion efficiency and output power; S54, the intelligent charging module monitors the output current, voltage and temperature in real time, and the monitoring is completed by the built-in current sensor, voltage sensor and temperature sensor; S55. The intelligent charging module feeds the monitored current, voltage, and temperature data back to the main control board via the multi-protocol communication interface. The main control board evaluates the operating status of the intelligent charging module based on the fed-back monitoring data. If the monitoring data exceeds a preset safety range or deviates from the target power output value by more than a deviation threshold, the main control board adjusts the power allocation strategy and re-sends the data to the intelligent charging module. S56. The intelligent charging module adjusts the power output according to the new power distribution strategy to form a closed-loop control.

[0096] Although the present invention has been described in detail with reference to the accompanying drawings and in conjunction with preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, persons of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and such modifications or substitutions shall be within the scope of the present invention. Any changes or substitutions that can be easily conceived by persons skilled in the art within the technical scope disclosed in the present invention shall be within the scope of protection of the present invention.

Claims

1. An intelligent charging pile control system based on dynamic power distribution, characterized in that: It includes a main control board, multiple slave control boards and at least one intelligent charging module. The slave control board is connected to the main control board via a bus communication and is used to collect battery data in real time. The intelligent charging module is connected to the main control board via a multi-protocol communication interface. The main control board is integrated with edge computing module and dynamic power distribution module; Upload battery data from the control board to the main control board; The edge computing module receives battery data uploaded from the control board, predicts the maximum allowable charging power of the battery through the LSTM model, and determines the battery's safety boundary based on the maximum allowable charging power and the battery's safe operating parameters, and sends it to the dynamic power allocation module; The dynamic power allocation module generates a power allocation strategy based on the battery safety boundary provided by the edge computing module, combined with the grid load and user priority, using the Nash equilibrium algorithm, and sends it to the smart charging module through the multi-protocol communication interface; The intelligent charging module is used to receive the power allocation strategy and adjust the switching frequency of the LLC resonant converter based on the power allocation strategy to achieve the target power output; the intelligent charging module also monitors the output current, voltage and temperature in real time, and feeds back to the main control board through the multi-protocol communication interface to form a closed-loop control.

2. The intelligent charging pile control system based on dynamic power allocation according to claim 1 is characterized in that: The main control board also integrates a multi-protocol collaborative communication module for real-time monitoring of the status of each channel, triggering protocol switching when the following conditions are met simultaneously: The current channel signal strength is less than a preset first threshold, and the packet loss rate within a set time period is greater than a preset first percentage; The signal strength of the target channel is greater than a preset second threshold, and the historical packet loss rate of the target channel is less than a preset second percentage.

3. The intelligent charging pile control system based on dynamic power allocation according to claim 2 is characterized in that: The multi-protocol collaborative communication module adjusts the baud rate by monitoring the network load, specifically including: Count the ratio of the number of data packets per second to the maximum theoretical load of the network; Monitor the remaining capacity of the DMA buffer; When the network load is greater than a preset third percentage and persists for N sampling periods, the multi-protocol cooperative communication module increases the baud rate from the current value to the next level; after the baud rate is switched, if the CRC error rate is greater than or equal to a preset fourth percentage, the multi-protocol cooperative communication module returns the baud rate to the original baud rate; When the remaining capacity of the DMA buffer is less than the fifth percentage, the baud rate is forced to increase.

4. The intelligent charging pile control system based on dynamic power allocation according to claim 3 is characterized in that: The edge computing module is specifically used for: Receive battery data uploaded from the control board, the battery data including battery voltage, current, temperature, battery status and battery health status; Based on the battery data, the maximum allowable charging power of the battery is predicted using a trained LSTM neural network model; determining a safety boundary of the battery based on the maximum allowable charging power and safe operating parameters of the battery, the safety boundary including an upper temperature limit, an upper voltage limit, an upper current limit, and a range of a battery state; The battery safety margin is sent to a dynamic power allocation module.

5. The intelligent charging pile control system based on dynamic power allocation according to claim 4 is characterized in that: The dynamic power allocation module is specifically used for: Receive a battery safety boundary provided by an edge computing module, where the battery safety boundary includes a temperature upper limit, a voltage upper limit, a current upper limit, and a range of a battery state; Real-time acquisition of grid load information, including real-time power, voltage, current, and frequency of the grid; Real-time acquisition of user priority information, including user charging needs, reservation time, user level, and urgency; Based on the battery safety boundary, grid load information, and user priority information, a Nash equilibrium model is constructed. The goal of the Nash equilibrium model is to optimize the overall power distribution efficiency of the charging pile system while ensuring battery safety and user satisfaction. Calculating a real-time power allocation strategy using the Nash equilibrium model, the real-time power allocation strategy including the power allocation ratio and output power of each smart charging module; The real-time power allocation strategy is sent to the intelligent charging module through the multi-protocol collaborative communication module to guide the power output of the intelligent charging module.

6. The intelligent charging pile control system based on dynamic power allocation according to claim 5 is characterized in that: The specific process of the dynamic power allocation module generating a power allocation strategy by building a Nash equilibrium model is as follows: According to the actual operating conditions of the charging pile system, the initial parameters of the Nash equilibrium model are set, including battery safety margins, grid load limits, user priority weights, and performance parameters of the smart charging module; Converting the battery safety boundary into constraint conditions, including the battery's upper temperature limit, upper voltage limit, upper current limit, and battery state range; Convert grid load information into system power constraints, including real-time power, voltage, current, and frequency limits of the grid; Convert user priority information into priority weights, including user charging needs, reservation time, user level, and urgency; defining a utility function for each smart charging module, the utility function being based on charging efficiency, user satisfaction, and system stability; Incorporating the constraints and priority weights into a utility function to form a multi-objective optimization function; Using an iterative algorithm to solve the Nash equilibrium model and find the optimal power allocation strategy for each smart charging module; In each iteration, the power allocation ratio of each smart charging module is updated until a Nash equilibrium state is reached, that is, the utility function of each module cannot be further optimized given the strategies of other modules; Based on the solution results, the real-time power allocation ratio and output power of each intelligent charging module are generated; Verify the generated power allocation strategy to ensure its feasibility under actual operating conditions; If the verification fails, adjust the model parameters or optimize the algorithm to solve the Nash equilibrium again.

7. The intelligent charging pile control system based on dynamic power allocation according to claim 6 is characterized in that: The intelligent charging module is specifically used for: Receiving a power allocation strategy issued by the main control board through a multi-protocol communication interface; parsing the power allocation strategy to extract a target power output value and related control parameters, wherein the control parameters include a switching frequency of an LLC resonant converter; According to the control parameters obtained by analysis, the switching frequency of the LLC resonant converter is adjusted to adjust the power conversion efficiency and output power; Real-time monitoring of output current, voltage and temperature, which is accomplished through built-in current sensor, voltage sensor and temperature sensor; The monitored current, voltage, and temperature data are fed back to the main control board via a multi-protocol communication interface. The main control board then evaluates the operating status of the intelligent charging module based on the fed-back monitoring data. If the monitoring data exceeds the preset safety range or deviates from the target power output value by more than a deviation threshold, the main control board adjusts the power allocation strategy and re-sends the data to the intelligent charging module. The intelligent charging module adjusts the power output according to the new power allocation strategy to form a closed-loop control.

8. A smart charging pile control method based on dynamic power allocation, characterized in that: The following steps are involved: S1. Collect battery data from the control board in real time and upload the battery data to the main control board; S2. The edge computing module on the main control board receives the battery data uploaded from the control board, predicts the maximum allowable charging power of the battery through the LSTM model, and determines the safety boundary of the battery based on the maximum allowable charging power and the battery's safe operating parameters; The dynamic power allocation module on the S3 main control board generates a power allocation strategy using the Nash equilibrium algorithm based on the battery safety boundary provided by the edge computing module, combined with the grid load and user priority. S4. The main control board sends the power allocation strategy to the intelligent charging module through the multi-protocol communication interface; S5. The intelligent charging module receives the power allocation strategy and adjusts the switching frequency of the LLC resonant converter based on the power allocation strategy to achieve the target power output; S6. The intelligent charging module monitors the output current, voltage and temperature in real time, and feeds the monitoring data back to the main control board through the multi-protocol communication interface to form a closed-loop control.

9. The intelligent charging pile control method based on dynamic power allocation according to claim 8, characterized in that: Before step S2, the method further includes the following steps: The multi-protocol collaborative communication module on the main control board monitors the status of each channel in real time and triggers protocol switching when the following conditions are met at the same time: the current channel signal strength is less than the preset first threshold, and the packet loss rate within the set time period is greater than the preset first percentage; the signal strength of the target channel is greater than the preset second threshold, and the historical packet loss rate of the target channel is less than the preset second percentage.

10. The intelligent charging pile control method based on dynamic power allocation according to claim 9, characterized in that: The method further includes: The multi-protocol collaborative communication module adjusts the baud rate by monitoring the network load, including: Count the ratio of the number of data packets per second to the maximum theoretical load of the network; Monitor the remaining capacity of the DMA buffer; When the network load is greater than a preset third percentage and persists for N sampling periods, the multi-protocol cooperative communication module increases the baud rate from the current value to the next level; after the baud rate is switched, if the CRC error rate is greater than or equal to a preset fourth percentage, the multi-protocol cooperative communication module returns the baud rate to the original baud rate; When the remaining capacity of the DMA buffer is less than a preset fifth percentage, the baud rate is forcibly triggered to increase.

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