An integrated dual-gun DC charging station

Abstract

This invention provides an integrated dual-gun DC charging pile, belonging to the field of power variable adjustment technology. The invention includes a main control module, a dual-path power conversion module, a communication interface module, a data acquisition module, a charging control module, a display and interaction module, a safety monitoring module, an energy storage module, a metering module, and a power management module. The main control module has a built-in control chip. The system control module in the control chip first acquires charging status data through the data acquisition module, performs eigenvalue decomposition on the charging status matrix to extract the main eigenvector, constructs a power feature space, and calculates the power allocation coefficient. Subsequently, a temperature feature matrix is ​​constructed based on temperature data, and the power compensation coefficient is calculated in conjunction with the energy storage capacity. Through multi-parameter collaborative optimization using a charging optimization equation set, precise power balance control is achieved, solving the technical problem in existing dual-gun DC charging piles that struggle to achieve precise power balance distribution under dynamic load conditions.

Description

Technical Field

[0001] This invention belongs to the field of electrical variable adjustment technology, and specifically relates to an integrated dual-gun DC charging pile. Background Technology

[0002] With the rapid development of the new energy vehicle industry, high-power DC charging piles have become an important part of charging infrastructure. To improve charging efficiency, dual-gun charging technology has been widely adopted. Traditional dual-gun charging technology mainly uses a fixed power allocation scheme, controlling the output power of the two guns through a preset allocation ratio or a simple dynamic adjustment algorithm. This scheme can meet basic charging needs and achieve initial power allocation under stable operating conditions. Currently, common dual-gun charging piles on the market mainly employ power limiting, polling, and priority allocation methods. The power limiting method ensures that the total system power does not exceed the design limit by setting a maximum output power threshold; the polling allocation method alternately allocates more power to the two charging guns according to a predetermined sequence; and the priority allocation method determines the power allocation scheme based on the user-defined priority. These technologies have been applied in engineering practice, forming a relatively complete charging control system.

[0003] However, traditional dual-gun charging technology has significant shortcomings. First, the fixed power allocation scheme struggles to adapt to dynamic load changes. When the load on one charging gun suddenly changes, it cannot adjust the output power of the other charging gun in a timely manner, resulting in a lag in system response. Second, the simple dynamic adjustment algorithm fails to adequately consider temperature equalization, making it prone to localized overheating during prolonged operation. Third, existing technologies lack in-depth analysis of the charging state, failing to accurately identify key characteristics during the charging process and hindering precise power control. Furthermore, traditional solutions are inadequate in energy storage management, failing to effectively utilize energy storage modules for power compensation, thus affecting the system's dynamic response capabilities. In practical applications, these problems lead to a series of technical bottlenecks, including reduced charging efficiency, uneven device heating, and poor system stability.

[0004] Under dynamic load conditions, dual-gun charging stations face even more severe challenges in power balancing control. Due to various uncertainties during electric vehicle charging, such as changes in battery state, ambient temperature fluctuations, and user interference, the charging load exhibits strong dynamic characteristics. Traditional technologies struggle to accurately capture these dynamic features, leading to deviations between power allocation schemes and actual needs. Especially in high-power fast charging scenarios, inaccurate power allocation not only affects charging efficiency but may also jeopardize system safety. Therefore, achieving precise power balancing under dynamic load conditions has become a crucial technical problem that urgently needs to be solved. Summary of the Invention

[0005] In view of this, the present invention provides an integrated dual-gun DC charging pile, which can solve the technical problem that existing dual-gun DC charging piles are difficult to achieve accurate power balance distribution under dynamic load conditions.

[0006] This invention is implemented as follows: This invention provides an integrated dual-gun DC charging pile comprising a main control module, a dual-path power conversion module, a communication interface module, a data acquisition module, a charging control module, a display and interaction module, a safety monitoring module, an energy storage module, a metering module, and a power management module. The main control module has a built-in control chip and is electrically connected to each of the functional modules. The control chip contains a system control module. The system control module extracts key features by performing eigenvalue decomposition on the charging state matrix, constructs a power feature space based on the key feature vector to achieve optimized power allocation between the two guns, uses a temperature feature matrix for temperature equalization control, and corrects the power compensation coefficient through a charging optimization equation set. The dual-path power conversion module is used to convert AC power to DC power and perform... The system includes a power regulation module, a communication interface module for data interaction with the backend management system and charging terminal, a data acquisition module for collecting charging voltage, charging current, and charging temperature data, a charging control module for controlling the charging process and executing charging strategies, a display and interaction module for displaying charging status and charging parameters and receiving user input, a safety monitoring module for monitoring the charging gun connection status and system faults, an energy storage module for temporarily storing electrical energy and providing backup power, a metering module for metering charging power and charging costs, and a power management module for allocating system power and performing power scheduling. The data acquisition module samples 100 times per second, the safety monitoring module samples 10 times per second, and the metering module samples once per second.

[0007] The system control module is used to perform the following steps: First, it uses the data acquisition module to collect dual-gun charging voltage, charging current, charging temperature, and input voltage fluctuation values. Based on the charging voltage and charging current values, it calculates the charging interface impedance, constructs a charging state matrix, and analyzes the charging state matrix to calculate historical power fluctuation values. Second, it performs eigenvalue decomposition on the charging state matrix, extracts the principal feature vector, constructs a power feature space based on the principal feature vector, calculates the dual-gun power coordination degree, and generates a power allocation coefficient. Third, it uses the safety monitoring module to collect charging temperature, ambient temperature, and radiator temperature data, constructs a temperature feature matrix, and calculates the dual-gun temperature balance coefficient. Finally, based on the power allocation coefficient and the temperature balance coefficient, combined with the energy storage capacity coefficient output by the energy storage module, it calculates the dual-gun power compensation coefficient.

[0008] The system control module further performs the following steps: correcting the power compensation coefficient using a set of charging optimization equations, including a power balancing equation, a temperature limiting equation, and a voltage correction equation; adjusting the dual-gun output power value through the charging control module based on the corrected power compensation coefficient to achieve balanced charging control of the dual guns; monitoring the charging status of the dual guns in real time, and re-executing power optimization control when the charging voltage, charging current, and charging temperature values ​​fluctuate beyond preset thresholds; and determining the completion status of the charging task based on the charging amount, charging cost, and cumulative charging time output by the metering module, and controlling the charging process to end.

[0009] The power allocation coefficient indicates the power allocation ratio between the two guns, calculated based on the power feature space and the power coordination degree of the two guns; the temperature balance coefficient represents the temperature difference between the two guns, calculated based on the temperature feature matrix; the power compensation coefficient is the power ratio that needs to be adjusted to achieve balance between the two guns, calculated based on the power allocation coefficient; and the energy storage capacity coefficient is the ratio of the remaining power of the energy storage module to the rated power.

[0010] Wherein, the charging interface impedance value is the ratio of the charging voltage value to the charging current value; the historical power fluctuation value is the temporal variation amplitude of the charging power; the dual-gun power coordination degree is the quantitative value of the balance of the dual-gun power distribution; the main feature vector is the main feature component obtained by eigenvalue decomposition of the charging state matrix; and the power feature space is the power distribution optimization space constructed based on the main feature vector.

[0011] The power balance equation input parameters include the power allocation coefficient, the power compensation coefficient, the energy storage capacity coefficient, the cumulative charging time value, and the historical power fluctuation value, and the output parameter is the power adjustment coefficient.

[0012] The temperature limiting equation has the following input parameters: temperature equalization coefficient, charging temperature value, ambient temperature value, radiator temperature value, and temperature threshold. The output parameter is the temperature correction coefficient.

[0013] The input parameters of the voltage correction equation include the charging voltage value, the power regulation coefficient, the temperature correction coefficient, the input voltage fluctuation value, and the charging interface impedance value, and the output parameter is the voltage correction coefficient.

[0014] The voltage fluctuation preset threshold is 3% of the rated value, the current fluctuation preset threshold is 5% of the rated value, and the temperature fluctuation preset threshold is 5 degrees Celsius.

[0015] The communication interface module includes an Ethernet interface and a mobile communication interface. The Ethernet interface uses a gigabit Ethernet controller and supports the TCP / IP protocol stack. The mobile communication interface uses a 4G communication module with a communication rate of 100Mbps.

[0016] Compared with existing technologies, this invention provides an integrated dual-gun DC charging pile. This invention proposes a dual-gun charging equalization method based on eigenvalue decomposition and multi-level optimization control. This method extracts the principal eigenvectors from the charging state matrix through eigenvalue decomposition to construct a power feature space, achieving accurate characterization of the dynamic features of the charging process. Combined with temperature feature matrix and energy storage capacity control, a complete set of charging optimization equations is established, realizing coordinated control of power, temperature, and voltage. A hierarchical sampling strategy ensures the real-time performance and reliability of the system.

[0017] The technical solution of this invention effectively solves the problems existing in traditional technologies. First, through the eigenvalue decomposition method, the system can accurately identify key features in the charging process, providing a reliable basis for power balance control. Second, the balance control strategy based on the temperature feature matrix achieves uniform heat generation of the charging equipment, avoiding local overheating. Third, the intelligent allocation of the energy storage module enhances the dynamic response capability of the system. Finally, the introduction of the charging optimization equation set realizes the synergistic optimization of multiple parameters, significantly improving control accuracy. These technological innovations enable this invention to maintain stable and reliable charging performance under dynamic load conditions.

[0018] This invention successfully solves the technical problem of achieving precise power balance distribution under dynamic load conditions in existing dual-gun DC charging piles, thanks to its innovations in both theory and implementation. Theoretically, the eigenvalue decomposition method provides the mathematical basis for power distribution, enabling the system to accurately grasp the essential characteristics of the charging process. In terms of implementation, a multi-level optimization control strategy ensures the practicality and reliability of the control scheme. By constructing a power characteristic space, the system achieves precise tracking of the dynamic load and makes real-time adjustments through an optimized equation set, ultimately achieving the goal of precise power balance. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the integrated dual-gun DC charging pile of the present invention.

[0020] Figure 2 This is a distribution diagram of system operating status parameters in Example 2.

[0021] Figure 3 This is a graph showing the distribution and contribution rate of eigenvalues ​​in Example 2.

[0022] Figure 4This is a temperature change trend graph from Example 2.

[0023] Figure 5 This describes the convergence process of the objective function in Example 2.

[0024] Figure 6 This is a flowchart of the system control module. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0026] like Figure 1 The diagram shown is a schematic representation of an integrated dual-gun DC charging pile provided by this invention. The charging pile includes a main control module, a dual-path power conversion module, a communication interface module, a data acquisition module, a charging control module, a display and interaction module, a safety monitoring module, an energy storage module, a metering module, and a power management module. The main control module has a built-in control chip and is electrically connected to the dual-path power conversion module, the communication interface module, the data acquisition module, the charging control module, the display and interaction module, the safety monitoring module, the energy storage module, the metering module, and the power management module. The control chip contains a system control module. The dual-path power conversion module is used to convert AC power to DC power and regulate the power. The communication interface module is used to realize data interaction with the background management system and the charging terminal. The data acquisition module is used to collect charging voltage, charging current and charging temperature data. The charging control module is used to control the charging process and execute the charging strategy. The display and interaction module is used to display the charging status and charging parameters and receive user input. The safety monitoring module is used to monitor the charging gun connection status and system faults. The energy storage module is used to temporarily store electrical energy and provide backup power. The metering module is used to measure the charging amount and charging cost. The power management module is used to allocate system power and perform power scheduling. The sampling frequency of the data acquisition module is 100 times per second, the sampling frequency of the safety monitoring module is 10 times per second, and the sampling frequency of the metering module is 1 time per second.

[0027] The system control module extracts key features of the charging state through eigenvalue decomposition, constructing a power feature space to optimize the power allocation between the two charging guns. Temperature equalization control is achieved using a temperature feature matrix, combined with energy storage capacity for power compensation. A set of charging optimization equations enables coordinated control of power, temperature, and voltage, ensuring the stability and balance of dual-gun charging. A hierarchical sampling frequency strategy is employed to ensure system operating efficiency and safety. Figure 6 As shown, the system control module is used to perform the following steps:

[0028] S01. Use the data acquisition module to collect the dual-gun charging voltage value, charging current value, charging temperature value and input voltage fluctuation value, calculate the charging interface impedance value based on the charging voltage value and the charging current value, construct the charging state matrix, and analyze the charging state matrix to calculate the historical power fluctuation value.

[0029] S02. Perform eigenvalue decomposition on the charging state matrix, extract the main feature vector, construct a power feature space based on the main feature vector, calculate the dual-gun power coordination degree, and generate a power allocation coefficient.

[0030] S03. Use the safety monitoring module to collect charging temperature, ambient temperature, radiator temperature, charging voltage, and charging current data, construct a temperature feature matrix, and calculate the dual-gun temperature balance coefficient.

[0031] S04. Based on the power distribution coefficient and the temperature equalization coefficient, and combined with the energy storage capacity coefficient output by the energy storage module, calculate the dual-gun power compensation coefficient.

[0032] S05. The power compensation coefficient is corrected using a set of charging optimization equations, which includes a power balance equation, a temperature limiting equation, and a voltage correction equation.

[0033] S06. Based on the corrected power compensation coefficient, the dual-gun output power value is adjusted by the charging control module to achieve balanced charging control of the dual guns.

[0034] S07. Monitor the dual-gun charging status in real time. When the charging voltage, charging current and charging temperature values ​​fluctuate beyond the preset threshold, repeat steps S01 to S06.

[0035] S08. Based on the charging power value, charging cost value and cumulative charging time value output by the metering module, determine the completion status of the charging task and control the charging process to end.

[0036] The power allocation coefficient indicates the power allocation ratio between the two guns, calculated based on the power feature space and the power coordination degree of the two guns; the temperature balance coefficient characterizes the temperature difference between the two guns, calculated based on the temperature feature matrix; the power compensation coefficient is the power ratio adjustment required to achieve dual-gun balance, calculated based on the power allocation coefficient; the energy storage capacity coefficient is the ratio of the remaining energy of the energy storage module to the rated energy; the charging interface impedance is the ratio of the charging voltage to the charging current; the historical power fluctuation is the temporal variation amplitude of the charging power; the dual-gun power coordination degree is a quantitative value of the balance of power allocation between the two guns; the principal feature vector is the main feature component obtained by eigenvalue decomposition of the charging state matrix; and the power feature space is the power allocation optimization space constructed based on the principal feature vector.

[0037] The power balancing equation input parameters include the power allocation coefficient, the power compensation coefficient, the energy storage capacity coefficient, the cumulative charging time value, and the historical power fluctuation value, and the output parameter is the power adjustment coefficient; the temperature limiting equation input parameters include the temperature balancing coefficient, the charging temperature value, the ambient temperature value, the radiator temperature value, and the temperature threshold, and the output parameter is the temperature correction coefficient; the voltage correction equation input parameters include the charging voltage value, the power adjustment coefficient, the temperature correction coefficient, the input voltage fluctuation value, and the charging interface impedance value, and the output parameter is the voltage correction coefficient.

[0038] The specific implementation methods of the above steps are described in detail below.

[0039] The main control module uses an ARM Cortex M7 series processor as the control chip, with a main frequency of 400MHz. It has 2MB of built-in flash memory and 512KB of random access memory. It interacts with other functional modules through multiple serial peripheral interfaces and uses a real-time operating system for task scheduling to ensure the real-time performance and reliability of the system response.

[0040] The dual-channel power conversion module adopts a three-phase full-bridge rectifier circuit structure. The rectifier bridge uses silicon carbide diodes, with a switching frequency of 20kHz. The output voltage is adjustable from 200 to 1000V, and the maximum output power is 240kW. An LC filter circuit with a 2mH inductor and a 4700μF capacitor is configured after the rectifier circuit to suppress voltage ripple. The power conversion circuit uses a phase-shifted full-bridge topology, employing 1700V / 400A silicon carbide MOSFETs as switching devices. Soft-switching technology is used to achieve zero-voltage switching, reducing switching losses.

[0041] The communication interface module includes an Ethernet interface and a mobile communication interface. The Ethernet interface uses a gigabit Ethernet controller and supports the TCP / IP protocol stack to achieve high-speed data exchange with the back-end management system. The mobile communication interface uses a 4G communication module, supports multiple communication protocols, and enables real-time data interaction with the charging terminal, with a communication rate of up to 100Mbps.

[0042] The data acquisition module uses a 24-bit analog-to-digital converter with a sampling accuracy of 0.1%. It has 8 analog input channels, a sampling frequency of 100 times per second, a voltage signal acquisition range of 0 to 1000V, a current signal acquisition range of 0 to 400A, and a temperature acquisition range of -40 to 150 degrees Celsius.

[0043] The charging control module uses a digital signal processor to execute the charging strategy. It has a built-in pulse width modulation controller with a modulation frequency of 20kHz and an adjustable dead time range of 0.5 to 5 microseconds. It has overvoltage, overcurrent, and overtemperature protection functions and a response time of less than 10 microseconds.

[0044] The display and interaction module uses a 7-inch LCD screen with a resolution of 1024 x 768 pixels. It uses a capacitive touch screen to realize human-computer interaction. The display interface includes charging status, charging parameters, fault information, etc., and supports Chinese and English display.

[0045] The safety monitoring module adopts dual independent monitoring circuits with a monitoring frequency of 10 times per second. It includes a charging gun lock detection circuit and a system fault detection circuit. The charging gun lock detection uses a Hall sensor, and the system fault detection includes functions such as insulation detection, grounding detection, and leakage current detection.

[0046] The energy storage module uses a lithium iron phosphate battery pack with a rated capacity of 30kWh, an operating voltage range of 600 to 800V, a maximum charge and discharge power of 60kW, and is equipped with a battery management system to monitor and protect the battery status, and has a balanced charging function.

[0047] The metering module uses a 0.5S-class energy metering chip with a measurement accuracy better than 0.5% and a sampling frequency of once per second. It can measure energy parameters such as forward active energy, reverse active energy, and power factor, and has a real-time billing function.

[0048] The power management module adopts a multi-channel DC-DC converter circuit with an input voltage range of 180 to 264V and output voltages including 12V, 5V, and 3.3V DC power supplies, with a total power of 2000W. It has overvoltage, overcurrent, and short circuit protection functions, and the power conversion efficiency is greater than 95%.

[0049] The system control module is used to perform the following steps:

[0050] The specific implementation of step S01 involves acquiring dual-gun charging data at a sampling frequency of 100 times per second using a data acquisition module. This data includes charging voltage, charging current, charging temperature, and input voltage fluctuation. The sampled data undergoes digital filtering to eliminate interference, employing a Butterworth low-pass filtering algorithm with a cutoff frequency of 1kHz. Based on the acquired charging voltage and current values, the charging interface impedance is calculated using the least squares method. The calculation uses data from 50 sampling points for fitting, improving the accuracy of the impedance calculation. When constructing the charging state matrix, parameters such as voltage, current, and temperature are arranged sequentially to form state vectors. Each state vector contains 100 sampling points, and 10 consecutive state vectors form the charging state matrix. When analyzing the charging state matrix to calculate historical power fluctuations, a sliding window method is used to calculate the power change rate. The window length is 1 second, and the sliding step is 0.1 seconds. The power fluctuation value is obtained by calculating the difference between the maximum and minimum power values ​​within the window. The main purpose of this step is to acquire real-time operating status data of the charging system, providing a data foundation for subsequent power optimization control.

[0051] The specific implementation of step S02 involves performing singular value decomposition (SVD) on the charging state matrix to extract the main eigenvectors. The eigenvalue decomposition uses the QR decomposition algorithm, setting an eigenvalue threshold of 0.1, and retaining only eigenvectors corresponding to eigenvalues ​​greater than the threshold. A power feature space is constructed based on the extracted main eigenvectors. The spatial dimension is determined by the number of eigenvectors, typically 3 to 5 dimensions. In the power feature space, a cosine similarity algorithm is used to calculate the dual-gun power coordination degree. The coordination degree ranges from 0 to 1; a coordination degree closer to 1 indicates a more balanced power distribution between the two guns. Power allocation coefficients are generated based on the power coordination degree. A nonlinear mapping function is used to convert the coordination degree into allocation coefficients. The mapping function is an exponential function, and its parameters are determined through experimental optimization. The main purpose of this step is to extract key features of the charging state through mathematical transformations to achieve optimized power allocation.

[0052] The specific implementation of step S03 involves using a safety monitoring module to collect temperature-related data at a sampling frequency of 10 times per second, including charging temperature, ambient temperature, and radiator temperature, while simultaneously collecting charging voltage and charging current values. The collected temperature data undergoes median filtering with a filtering window length of 5 sampling points. The processed temperature data is then arranged chronologically to construct a temperature feature matrix with a dimension of 5x3, where each element represents a temperature sampling value. Based on the temperature feature matrix, a dual-gun temperature equalization coefficient is calculated. This calculation method involves extracting temperature change characteristics using matrix singular value decomposition, calculating the root mean square value of the temperature difference between the two guns, and mapping the calculation result to the 0-1 interval using a sigmoid function to obtain the temperature equalization coefficient. The main purpose of this step is to achieve temperature monitoring and equalization control of the charging system.

[0053] The specific implementation of step S04 is based on the power distribution coefficient and temperature equalization coefficient, combined with the energy storage capacity coefficient output by the energy storage module, to calculate the dual-gun power compensation coefficient using a weighted average method. The weight of the power distribution coefficient is 0.5, the weight of the temperature equalization coefficient is 0.3, and the weight of the energy storage capacity coefficient is 0.2. These weighting coefficients are determined through experimental optimization. The energy storage capacity coefficient is the ratio of the remaining power of the energy storage module to its rated power. When this ratio is lower than 0.2, the system will limit the maximum charging power. The power compensation coefficient is calculated using a piecewise linear function. When the temperature equalization coefficient is less than 0.8, the power compensation level is increased; when the energy storage capacity coefficient is less than 0.3, the power compensation coefficient is decreased. The main purpose of this step is to comprehensively consider factors such as power distribution, temperature equalization, and energy storage capacity to calculate reasonable power compensation parameters.

[0054] The specific implementation of step S05 involves correcting the power compensation coefficient using a set of charging optimization equations. This set of equations includes a power balance equation, a temperature limitation equation, and a voltage correction equation. The power balance equation employs a nonlinear programming model. Input parameters include the power allocation coefficient, power compensation coefficient, energy storage capacity coefficient, cumulative charging time, and historical power fluctuation values. The optimal power adjustment coefficient is solved using the gradient descent method, with an iteration step size of 0.01, a maximum of 100 iterations, and a convergence threshold of 0.001. The temperature limitation equation uses a fuzzy control algorithm. Input parameters include the temperature balance coefficient, charging temperature, ambient temperature, radiator temperature, and a temperature threshold. The temperature thresholds are set as follows: charging temperature not exceeding 90 degrees Celsius, ambient temperature not exceeding 45 degrees Celsius, and radiator temperature not exceeding 75 degrees Celsius. The fuzzy control rules are set based on expert experience, classifying temperature states into three levels: low, moderate, and high, and outputting a temperature correction coefficient. The voltage correction equation employs an adaptive control algorithm. Input parameters include the charging voltage, power regulation coefficient, temperature correction coefficient, input voltage fluctuation, and charging interface impedance. The voltage correction coefficient is calculated through state feedback control, and the controller parameters are adjusted in real-time using an adaptive law. The main purpose of this step is to achieve precise control of the charging process through the synergistic effect of multiple optimization equations.

[0055] The specific implementation of step S06 involves adjusting the dual-gun output power value through the charging control module based on the corrected power compensation coefficient. First, the power compensation coefficient is converted into a power adjustment command using a lookup table method with an accuracy of 0.01. Target values ​​for the charging voltage and current are calculated based on the power adjustment command using a proportional-integral (PI) control algorithm with a proportional gain of 0.8 and an integral time constant of 0.1 seconds. A control signal is output through the pulse width modulation (PWM) controller to control the on-time of the switching transistors in the power conversion circuit, achieving precise adjustment of the charging power. A soft-start strategy is employed during power adjustment, limiting the power change rate to within 20% per second. Simultaneously, the charging interface voltage is monitored; when voltage fluctuations exceed 5%, voltage feedforward control is activated to suppress the impact of grid fluctuations on the charging process. The main purpose of this step is to implement a power balancing control strategy to ensure the stability of dual-gun charging.

[0056] The specific implementation of step S07 involves real-time monitoring of the dual-gun charging status, including charging voltage, charging current, and charging temperature. A Kalman filter algorithm is used to process the monitoring data to remove measurement noise. Fluctuation thresholds for the monitoring parameters are set: voltage fluctuations should not exceed 3% of the rated value, current fluctuations should not exceed 5% of the rated value, and temperature fluctuations should not exceed 5 degrees Celsius. When the fluctuations of the monitoring parameters exceed the preset thresholds, the charging optimization control process is triggered to re-execute. A warm-start strategy is used during re-execution, utilizing the previous optimization result as the initial value to improve the optimization convergence speed. System operating data is recorded simultaneously during monitoring, and the data storage uses a circular buffer structure with a buffer size equivalent to one hour of data. The main purpose of this step is to ensure the stability and reliability of the charging process.

[0057] The specific implementation of step S08 is based on the charging power value, charging fee value, and cumulative charging time value output by the metering module to determine the completion status of the charging task. The metering module outputs metering data once per second, including parameters such as positive active energy, power factor, and charging time. The completion determination of the charging task adopts a multi-condition combination judgment method, and the judgment conditions include: the charging power reaches the set value, or the charging time reaches the maximum limit, or the user actively ends the charging, or the system detects a charging fault. When any judgment condition is met, the system enters the charging end process. The charging end process includes: reducing the charging power, implementing a soft stop strategy, and controlling the power reduction rate to within 10% per second; disconnecting the charging contactor and detecting the charging circuit voltage; executing the charging gun unlocking procedure; saving the charging record data; and displaying the charging settlement information. The main purpose of this step is to achieve the safe end of the charging process and the settlement of fees.

[0058] The calculation process, matrix, and equation involved in this invention will be described in detail below.

[0059] The expression for the charging state matrix is:

[0060] ;

[0061] In the formula, For the first The sampling time of the first sampling moment The voltage value of each charging gun, in volts; For the first The sampling time of the first sampling moment The current value of each charging gun, in amperes; For the first The sampling time of the first sampling moment The temperature value of each charging gun, in degrees Celsius; This is the length of the sampling time window, with a value of 100. This represents the number of charging guns, with a value of 2.

[0062] The formula for calculating the impedance value of the charging interface is as follows:

[0063] ;

[0064] In the formula, This is the impedance value of the charging interface, in ohms. This is the charging voltage value, in volts. This is the charging current value, in amperes. This is the dynamic impedance coefficient, with a value ranging from 0.1 to 0.3. This is the initial impedance correction factor, with a value ranging from 0.05 to 0.15; This is the time decay coefficient, with a value of 0.01; This refers to the charging time, measured in seconds.

[0065] The main feature vector extraction employs the singular value decomposition method, which is mathematically expressed as follows:

[0066] ;

[0067] In the formula, This is the charging state matrix; It is a left singular matrix; It is a singular value diagonal matrix; It is a right singular matrix; the principal eigenvectors are those with the largest singular values. A right singular vector, The value is 3.

[0068] The expression for constructing the power feature space is:

[0069] ;

[0070] In the formula, For power characteristic space; For the first One eigenvalue; For the first 1 eigenvector; This is the gradient weight coefficient, with a value of 0.1; is a nonlinear mapping function for the eigenvectors; This is the gradient operator.

[0071] The formula for calculating the power coordination of the dual guns is as follows:

[0072] ;

[0073] In the formula, For power coordination; , Let be the power feature vectors of the two charging guns; This is the power balance factor, with a value of 0.2. The power difference attenuation coefficient is 0.01. This represents the power difference between the two charging guns, expressed in kilowatts.

[0074] The expression for the temperature feature matrix is:

[0075] ;

[0076] In the formula, For the first The charging temperature value at each sampling point; For the first Ambient temperature values ​​at each sampling point; For the first The radiator temperature values ​​at each sampling point; This represents the number of temperature sampling points, with a value of 5.

[0077] The formula for calculating the temperature equalization coefficient is:

[0078] ;

[0079] In the formula, This is the temperature equilibrium coefficient; This is the temperature difference sensitivity coefficient, with a value of 0.1. This represents the temperature difference between the two charging guns. The coefficient representing the influence of radiator temperature is 0.3. This is the highest temperature of the radiator; This is the rated temperature of the radiator.

[0080] The formula for calculating the power compensation coefficient is as follows:

[0081] ;

[0082] In the formula, This is the power compensation coefficient; , , The weights for power coordination, temperature balance coefficient, and energy storage capacity coefficient are 0.5, 0.3, and 0.2, respectively. This refers to the energy storage capacity coefficient. This is the time compensation coefficient, with a value of 0.1. This is the time constant, with a value of 0.001. This refers to the charging time.

[0083] The power balance equation is expressed as follows:

[0084] ;

[0085] In the formula, The objective function is power equalization; For the first The output power of each charging gun; This represents the desired power value. , , These are weighting coefficients, with values ​​of 0.3, 0.2, and 0.1 respectively. This represents historical power fluctuation values.

[0086] The temperature limiting equation is expressed as follows:

[0087] ;

[0088] In the formula, The objective function is the temperature constraint. For the first The charging temperature of each charging gun; For the first The heat sink temperature of each charging gun; , These are the charging temperature and heat sink temperature limits, respectively. , These are temperature weighting coefficients, with values ​​of 0.4 and 0.3 respectively. This is the temperature difference weighting coefficient, with a value of 0.3.

[0089] The voltage correction equation is expressed as follows:

[0090] ;

[0091] In the formula, The objective function is the voltage correction function; For the first The output voltage of each charging gun; The desired voltage value; , , These are the voltage control weighting coefficients, with values ​​of 0.4, 0.3, and 0.3 respectively. This represents the voltage fluctuation value. This is the impedance value of the charging interface.

[0092] The formula for calculating the historical power fluctuation value is as follows:

[0093] ;

[0094] In the formula, Historical power fluctuation values; For the first Power values ​​at each sampling point; This represents the average power. This represents the number of sampling points, with a value of 100. This is the power change rate weighting coefficient, with a value of 0.2.

[0095] The formula for calculating the energy storage capacity coefficient is as follows:

[0096] ;

[0097] In the formula, This refers to the energy storage capacity coefficient. This represents the current remaining power of the energy storage module. The rated power of the energy storage module; This is the coefficient for the rate of change of electricity, with a value of 0.1.

[0098] The design principles of the above equations are as follows: The charging interface impedance calculation considers static impedance, dynamic impedance, and time decay characteristics, where the exponential term reflects the stabilization process of the contact impedance over time; the power feature space construction adopts a combination of eigenvalue decomposition and gradient optimization, which not only retains the main feature information but also introduces nonlinear mapping to improve the feature expression capability; the power coordination degree calculation combines cosine similarity and power difference compensation terms, which can more accurately reflect the balance of power distribution between the two guns; the temperature balance coefficient adopts the characteristics of the sigmoid function, which can smooth out temperature differences and consider the influence of radiator temperature; the power compensation coefficient calculation adopts a multi-factor weighting and time compensation method to achieve coordinated control of power, temperature, and energy storage; the three optimization equations are respectively for power balance, temperature limitation, and voltage correction, and adopt quadratic objective functions for easy solution, and the importance of each term is adjusted by weighting coefficients.

[0099] It should be noted that the derivation process of some matrices or equations is described in detail below.

[0100] 1. Establishment of the charging state matrix:

[0101] The charging state matrix is ​​formed by collecting and organizing data, and the specific steps are as follows:

[0102] Step 1: Collect voltage, current, and temperature data every 0.01 seconds;

[0103] Step 2: Arrange the data from 100 consecutive sampling points in chronological order to form a 3n-column matrix, where n is the number of charging guns;

[0104] Step 3: Normalize the collected data to eliminate the influence of dimensions;

[0105] The number of rows in the matrix reflects the length of the time window, and the number of columns reflects the completeness of the monitoring parameters. This matrix structure can simultaneously reflect the temporal variation characteristics and interrelationships of the parameters of the dual guns.

[0106] 2. Derivation of the formula for calculating the impedance value of the charging interface:

[0107] The basic principle comes from the theory of dynamic impedance measurement, and the derivation steps are as follows:

[0108] Step 1: Establish the static impedance term This reflects the basic ohmic properties;

[0109] Step 2: Introduce the dynamic impedance term This characterizes the impedance as a function of current.

[0110] Step 3: Add a time decay term This describes the steady-state process of contact impedance over time.

[0111] parameter , , The results were obtained through experimental calibration. The experimental steps were as follows: measuring the voltage response under different currents; calculating the static and dynamic impedance values; and fitting the time decay characteristics.

[0112] This equation can accurately reflect the dynamic impedance characteristics of the charging interface, which is beneficial for the stable control of the charging process.

[0113] 3. Derivation of power characteristic space construction:

[0114] Based on principal component analysis theory and gradient optimization method, the derivation steps are as follows:

[0115] Step 1: Perform singular value decomposition on the charging state matrix to obtain eigenvalues ​​and eigenvectors;

[0116] Step 2: Select the eigenvectors corresponding to the k largest eigenvalues ​​as basis vectors;

[0117] Step 3: Introduce gradient optimization terms Enhance the ability to express features;

[0118] Nonlinear mapping function Using the ReLU function: ;

[0119] This space construction method can effectively extract the main features of the charging state, providing a basis for power optimization allocation.

[0120] 4. Steps for establishing the formula for calculating the power coordination of dual guns:

[0121] Based on vector cosine similarity theory and combined with power difference compensation, the derivation steps are as follows:

[0122] Step 1: Construct the basic cosine similarity term This reflects the similarity of power distribution;

[0123] Step 2: Introduce a power difference compensation term Correcting for high power discrepancies;

[0124] parameter and The following experiments were conducted to determine: the charging effect under different power combinations; the relationship between power difference and charging efficiency; and the least squares fitting parameters.

[0125] This equation can accurately assess the balance of power distribution between the two guns.

[0126] 5. Steps for establishing the temperature equilibrium coefficient calculation:

[0127] Using the characteristics of the sigmoid function and considering the influence of heat sink temperature, the derivation steps are as follows:

[0128] Step 1: Construct the basic sigmoid function term This enables a smooth mapping of temperature differences;

[0129] Step 2: Add radiator temperature impact item Considering heat dissipation capacity;

[0130] parameter and Temperature control experiments were conducted to determine: charging performance under different temperature differences; the impact of radiator temperature on the system was analyzed; and parameter values ​​were optimized.

[0131] This equation can effectively balance the temperature difference between the two charging guns and protect the charging system.

[0132] 6. Steps for establishing the power compensation coefficient calculation:

[0133] Based on the principles of multi-factor weighting and time compensation, the derivation steps are as follows:

[0134] Step 1: Establish basic weighted sum items ;

[0135] Step 2: Introduce time compensation terms This enables dynamic adjustment;

[0136] The weighting coefficients are optimized through the following steps: establishing charging performance evaluation indicators; designing orthogonal experimental schemes; analyzing the influence of each factor; and determining the optimal weight combination.

[0137] This equation achieves synergistic optimization of power, temperature, and energy storage.

[0138] 7. Steps for establishing the power balance equation:

[0139] Based on the principle of least squares optimization, the derivation steps are as follows:

[0140] Step 1: Establish the power deviation square term ;

[0141] Step 2: Introduce compensation coefficient constraint terms ;

[0142] Step 3: Add energy storage constraints ;

[0143] Step 4: Consider historical fluctuation constraints ;

[0144] Weighting coefficient , , The following methods were used to determine the power equalization performance index: establishing power equalization performance indicators; conducting parameter sensitivity analysis; and optimizing weight configuration.

[0145] This equation enables precise power balance control.

[0146] 8. Steps for establishing the temperature limit equation:

[0147] Based on the temperature safety constraint theory, the derivation steps are as follows:

[0148] Step 1: Construct charging temperature constraints ;

[0149] Step 2: Add heatsink temperature constraints ;

[0150] Step 3: Introduce the temperature difference term ;

[0151] Temperature limits and weighting coefficients were determined through the following experiments: testing system performance at different temperatures; analyzing temperature safety boundaries; and optimizing weighting parameters.

[0152] This equation can effectively ensure temperature safety during the charging process.

[0153] 9. Steps for establishing the voltage correction equation:

[0154] Based on voltage stability control theory, the derivation steps are as follows:

[0155] Step 1: Establish the voltage deviation term ;

[0156] Step 2: Introduce voltage change rate constraint ;

[0157] Step 3: Add voltage fluctuation constraints ;

[0158] Step 4: Consider the impedance effect. ;

[0159] The weighting coefficients are optimized through the following steps: analyzing voltage stability requirements; testing voltage response characteristics; and optimizing control parameters.

[0160] This equation can guarantee the stability of the charging voltage.

[0161] 10. Steps for establishing historical power fluctuation values:

[0162] Based on statistical analysis and dynamic characteristics, the derivation steps are as follows:

[0163] Step 1: Construct the power variance term ;

[0164] Step 2: Introduce the power change rate term ;

[0165] The parameters are determined through operational data analysis: power fluctuation data are collected; fluctuation characteristics are analyzed; and parameter values ​​are determined.

[0166] This equation can accurately describe the historical fluctuation characteristics of power.

[0167] 11. Steps for establishing the energy storage capacity coefficient calculation:

[0168] Based on the energy storage state assessment theory, the derivation steps are as follows:

[0169] Step 1: Establish the basic capacity ratio item ;

[0170] Step 2: Introduce the term of electricity change rate ;

[0171] The parameters are calibrated through the battery management system: testing battery capacity characteristics; analyzing the charging and discharging process; and optimizing parameter values.

[0172] This equation can accurately reflect the available capacity status of the energy storage module.

[0173] Specifically, the principle of this invention is as follows: The core principle lies in revealing the intrinsic characteristics of the charging process through eigenvalue decomposition and constructing a precise power control model based on this. First, the charging state matrix contains multi-dimensional information such as voltage, current, and temperature. Eigenvalue decomposition can extract the correlation features between these parameters. The principal eigenvector reflects the main changing trends of the charging state, providing a theoretical basis for power allocation. The power feature space constructed based on the principal eigenvector realizes a low-dimensional representation of the charging state, simplifying the design difficulty of the control strategy. The advantage of this mathematical processing method is that it can effectively reduce data redundancy, highlight key information, and lay the foundation for achieving precise control.

[0174] At the control strategy level, this invention employs a multi-layered optimization control method. The introduction of a temperature feature matrix solves the temperature equalization problem in traditional control schemes. By real-time monitoring of multi-point temperature data, a temperature distribution model is constructed, achieving precise control of the temperature field. The intelligent allocation strategy of the energy storage module enhances the system's dynamic response capability. When the load changes abruptly, the energy storage module can provide timely power compensation to maintain system stability. The charging optimization equations organically combine power equalization, temperature limiting, and voltage correction, forming a complete control closed loop. This multi-layered control architecture ensures that the system maintains good performance under various operating conditions.

[0175] From a system implementation perspective, the hierarchical sampling strategy of this invention ensures the real-time performance and accuracy of data acquisition. High-frequency sampling by the data acquisition module ensures precise monitoring of the charging status, medium-frequency sampling by the safety monitoring module meets system protection requirements, and low-frequency sampling by the metering module balances accuracy and efficiency. This hierarchical design fully considers the functional characteristics and performance requirements of each module, enabling rational allocation of system resources. Although real-time eigenvalue decomposition increases the computational burden, optimized algorithm design ensures that all necessary calculations can be completed within the control cycle, meeting real-time control requirements.

[0176] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0177] The main control module employs an ARM Cortex M7 series processor as the control chip, with a main frequency of 400MHz. It has 2MB of built-in flash memory and 512KB of random access memory, and interacts with other functional modules through multiple serial peripheral interfaces. The main control module uses a real-time operating system for task scheduling, configured with five priority levels: status monitoring tasks have the highest priority, followed by power control tasks, and data storage tasks have the lowest priority. The task scheduling cycle is 10 milliseconds. The system control module of the main control module adopts a layered architecture design, including functional modules such as the device management layer, communication management layer, data processing layer, and policy control layer. It uses object-oriented programming methods to achieve modular management of various system functions. The device management layer is responsible for the initialization configuration and status management of each functional component; the communication management layer realizes data interaction with external devices; the data processing layer completes data acquisition, storage, and analysis; and the policy control layer executes charging control strategies. The system control module has a built-in real-time database for storing system operating parameters and historical data, supporting data query and statistical analysis functions. The system control module uses a watchdog circuit for system monitoring, with a watchdog timer of 1 second, and has fault self-diagnosis and self-recovery functions.

[0178] The dual-channel power conversion module adopts a three-phase full-bridge rectifier circuit structure with an input voltage of 380V AC. The rectifier bridge uses 1700V silicon carbide diodes, with a switching frequency of 20kHz. The output voltage is adjustable from 200 to 1000V, and the maximum output power is 240kW. An LC filter circuit is configured after the rectifier circuit, with an inductance of 2mH and a capacitance of 4700μF. The cutoff frequency of the filter circuit is 500Hz to suppress voltage ripple. The power conversion circuit adopts a phase-shifted full-bridge topology, using 1700V / 400A silicon carbide MOSFETs as the switching devices. Soft-switching technology is used to achieve zero-voltage switching, with a dead time of 1 microsecond to reduce switching losses. The power conversion circuit control adopts a dual closed-loop control structure with an outer voltage loop and an inner current loop. The outer loop bandwidth is 100Hz, the inner loop bandwidth is 1kHz, and the control cycle is 50 microseconds. The power conversion module has an efficiency greater than 98%, a power factor greater than 0.99, and a total harmonic distortion (THD) of less than 5%.

[0179] The communication interface module includes an Ethernet interface and a mobile communication interface. The Ethernet interface uses a Gigabit Ethernet controller, supports the TCP / IP protocol stack, and has a communication rate of 1000Mbps, enabling high-speed data exchange with the backend management system. The mobile communication interface uses a 4G communication module, supports multiple communication protocols, has a maximum uplink rate of 50Mbps, and a maximum downlink rate of 100Mbps, enabling real-time data interaction with the charging terminal. The communication interface module adopts a dual-machine hot backup structure; when the primary communication link fails, it can switch to the backup link within 100 milliseconds. Communication data is encrypted using the AES256 encryption algorithm to ensure data transmission security. The communication interface module has a data caching function with a cache capacity of 1GB, which can cache 24 hours of operational data when communication is interrupted.

[0180] The data acquisition module employs a 24-bit analog-to-digital converter with a sampling accuracy of 0.1%, featuring 8 analog input channels and a sampling frequency of 100 times per second. The voltage acquisition loop has a range of 0 to 1000V, using a resistor divider network for sampling with a division ratio of 1000:1 and a temperature coefficient of less than 25ppm / degree Celsius for the divider resistors. The current acquisition loop has a range of 0 to 400A, using a Hall effect sensor with an accuracy of 0.1% and linearity better than 0.1%. The temperature acquisition loop has a range of -40 to 150 degrees Celsius, using a PT100 platinum resistance thermometer with a measurement accuracy of 0.1 degrees Celsius. All sampled data undergoes digital filtering using a Butterworth low-pass filter with a cutoff frequency of 1kHz.

[0181] The charging control module employs a digital signal processor (DSP) to execute the charging strategy. It has a main frequency of 200MHz, a built-in pulse width modulation (PWM) controller with a modulation frequency of 20kHz, an adjustable dead time range of 0.5 to 5 microseconds, and a resolution of 20 nanoseconds. The module features overvoltage, overcurrent, and overtemperature protection functions with a response time of less than 10 microseconds. The overvoltage protection threshold is 1100V, the overcurrent protection threshold is 440A, and the overtemperature protection threshold is 95 degrees Celsius. The charging control algorithm uses a model predictive control (MMC) method, with a prediction time domain of 10 control cycles and a control cycle of 50 microseconds. Optimization objectives include power equalization, temperature equalization, and voltage stability. The controller uses an adaptive parameter tuning method, automatically adjusting control parameters according to the charging status.

[0182] The display and interaction module uses a 7-inch LCD screen with a resolution of 1024 x 768 pixels, a brightness of 500 nits, a contrast ratio of 1000:1, and a viewing angle of 178 degrees. The screen uses a capacitive touchscreen for human-computer interaction, supports multi-touch, and has a response time of less than 10 milliseconds. The display interface includes charging status, charging parameters, fault information, etc., supports both Chinese and English display, and has a refresh rate of 60Hz. The display and interaction module uses an embedded graphics library for interface rendering, enabling animation display at a frame rate of 30 frames per second. The data update cycle is 100 milliseconds, and the historical data curve display range is the most recent hour.

[0183] The safety monitoring module employs dual independent monitoring circuits, monitoring 10 times per second, and includes a charging gun lock detection circuit and a system fault detection circuit. The charging gun lock detection uses a Hall sensor with a detection accuracy of 0.1 mm and a response time of less than 5 milliseconds. System fault detection includes insulation detection, grounding detection, and leakage current detection. The insulation resistance detection range is 0 to 20 megohms, with a detection voltage of 500V; the leakage current detection range is 0 to 1000 mA, with a detection accuracy of 1 mA. The safety monitoring module has a self-test function, performing a comprehensive self-test upon startup and a periodic self-test every hour during operation. Monitoring data is stored in a real-time database with a retention period of 3 months.

[0184] The energy storage module uses a lithium iron phosphate battery pack with a rated capacity of 30kWh, a nominal voltage of 650V, an operating voltage range of 600 to 800V, and a maximum charge / discharge power of 60kW. The battery management system employs a hierarchical management structure to achieve battery status monitoring and protection functions, monitoring parameters including battery voltage, current, temperature, and internal resistance. Active equalization charging is used, with an equalization current of 2 amps and an equalization accuracy of 10 millivolts. The battery pack has a cycle life greater than 3000 cycles, a calendar life greater than 8 years, and an operating temperature range of -20 to 60 degrees Celsius. The energy storage module has a battery status assessment function, employing a Kalman filter-based status estimation algorithm to accurately assess the battery's remaining capacity and health status.

[0185] The metering module uses a 0.5S-class energy metering chip with a measurement accuracy better than 0.5%, sampling once per second. It can measure energy parameters such as forward active energy, reverse active energy, and power factor. The basic error of the voltage sampling circuit is less than 0.2%, the basic error of the current sampling circuit is less than 0.2%, and the composite error of power calculation is less than 0.5%. Metering data is recorded every 15 minutes, and data storage uses a power-off protected ferroelectric memory with a storage capacity of 5 years of metering data. The metering module has real-time billing functionality, supports tiered pricing and peak-valley pricing, and has a billing accuracy of 0.01 yuan.

[0186] The power management module employs a multi-channel DC-DC converter circuit, with an input voltage range of 180 to 264V and output voltages including 12V, 5V, and 3.3V, with a total power of 2000W. The voltage regulation accuracy of each output voltage is better than 1%, the ripple factor is less than 50 mV, the load regulation rate is less than 0.5%, and the power conversion efficiency is greater than 95%. The power management module features overvoltage, overcurrent, and short-circuit protection functions. The overvoltage protection point is set at 120% of the rated output voltage, the overcurrent protection point is set at 110% of the rated output current, and the short-circuit protection uses a self-recovery method. A timing controller is used for power-on sequence control to ensure that each power supply starts and shuts down in the correct order, with a startup sequence interval of 100 milliseconds.

[0187] The specific implementation details of the steps performed by the system control module are described below.

[0188] The specific implementation of step S01 involves acquiring dual-gun charging data at a sampling frequency of 100 times per second using a data acquisition module. First, the acquired data undergoes digital filtering using a 4th-order Butterworth low-pass filter with a cutoff frequency of 1kHz. The filtered data will serve as the basis for subsequent processing. Then, the charging interface impedance value is calculated using the following formula: , parameters in the formula , , These are the dynamic impedance coefficient, the initial impedance correction coefficient, and the time decay coefficient, respectively, with values ​​ranging from 0.1 to 0.3, 0.05 to 0.15, and 0.01. Next, the charging state matrix is ​​constructed, and its expression is: ,in The value is 100. The value is set to 2, and the elements in the matrix are normalized using a maximum-minimum method. Finally, the historical power fluctuation value is calculated using the following formula: In the formula The value is 100. The value is set to 0.2. The main purpose of this step is to obtain real-time operating status data of the charging system.

[0189] The specific implementation of step S02 is to first perform singular value decomposition on the charging state matrix, and the decomposition formula is: The decomposition uses the QR decomposition algorithm, with 100 iterations and a convergence threshold of 0.001. Then, the principal feature vector is extracted, with a eigenvalue threshold of 0.1, retaining only the feature vectors corresponding to eigenvalues ​​greater than the threshold. Next, a power feature space is constructed based on the principal feature vectors, using the following formula: In the formula The value is 3. The value is set to 0.1, and the ReLU function is used for the nonlinear mapping function. Finally, the power coordination degree of the dual guns is calculated and the power allocation coefficient is generated. The formula for calculating the power coordination degree is: In the formula The value is 0.2. The value is set to 0.01. The main purpose of this step is to extract key features of the charging state and achieve optimized allocation of charging power.

[0190] The specific implementation of step S03 is as follows: First, the safety monitoring module collects temperature-related data at a sampling frequency of 10 times per second. The collected data includes charging temperature, ambient temperature, and radiator temperature. The collected data undergoes median filtering with a filtering window length of 5 sampling points. Then, a temperature feature matrix is ​​constructed, and the expression for the temperature feature matrix is: ,in The value is set to 5. Finally, the temperature equalization coefficient of the dual-gun system is calculated. The formula for calculating the temperature equalization coefficient is: In the formula The value is 0.1. The value is set to 0.3. The main purpose of this step is to achieve temperature monitoring and equalization control of the charging system.

[0191] The specific implementation of step S04 is to first obtain the energy storage capacity coefficient, and the formula for calculating the energy storage capacity coefficient is: In the formula The value is set to 0.1. Then, based on the power distribution coefficient, temperature equalization coefficient, and energy storage capacity coefficient, the power compensation coefficient is calculated. The formula for calculating the power compensation coefficient is: In the formula , , The values ​​were 0.5, 0.3, and 0.2 respectively. The value is 0.1. The value is set to 0.001. The main purpose of this step is to calculate reasonable power compensation parameters by comprehensively considering factors such as power distribution, temperature balance, and energy storage capacity.

[0192] The specific implementation of step S05 involves correcting the power compensation coefficient using a set of charging optimization equations. First, optimization calculations are performed using the power balance equation, the expression of which is: In the formula , , The values ​​are taken as 0.3, 0.2, and 0.1 respectively. The gradient descent method is used for solving the problem, with an iteration step size of 0.01, a maximum number of iterations of 100, and a convergence threshold of 0.001. Then, a temperature constraint is applied using the temperature constraint equation, which is expressed as follows: In the formula , , The values ​​are 0.4, 0.3, and 0.3 respectively, representing the temperature limits. It is 90 degrees Celsius. The temperature is 75 degrees Celsius. Finally, voltage stabilization control is achieved using the voltage correction equation, which is expressed as follows: In the formula , , The values ​​are 0.4, 0.3, and 0.3 respectively. The main purpose of this step is to achieve precise control of the charging process through the synergistic effect of multiple optimization equations.

[0193] The specific implementation of step S06 involves first converting the power compensation coefficient into a power adjustment command using a lookup table method with an accuracy of 0.01. The lookup data is obtained through offline optimization. Then, the target values ​​for the charging voltage and charging current are calculated based on the power adjustment command using a proportional-integral (PI) control algorithm with a proportional gain of 0.8, an integral time constant of 0.1 seconds, and a control cycle of 50 microseconds. Next, a control signal is output through a pulse width modulation (PWM) controller with a frequency of 20kHz, a dead time of 1 microsecond, and a minimum duty cycle of 0.05. Finally, power adjustment is executed using a soft-start strategy, limiting the power change rate to within 20% per second. Simultaneously, the charging interface voltage is monitored; when the voltage fluctuation exceeds 5%, voltage feedforward control is initiated with a feedforward coefficient of 0.5. The main purpose of this step is to implement a power balancing control strategy.

[0194] The specific implementation of step S07 involves first processing the monitoring data using a Kalman filter algorithm. A second-order model is used for the state equation, with an observation noise standard deviation of 0.01, a process noise standard deviation of 0.001, and a filtering period of 10 milliseconds. Then, it is determined whether the monitored parameters exceed preset thresholds: voltage fluctuation threshold is 3% of the rated value, current fluctuation threshold is 5% of the rated value, and temperature fluctuation threshold is 5 degrees Celsius. When a parameter exceeds the threshold, the charging optimization control process is triggered to re-execute. During re-execution, a warm-start strategy is used, employing the previous optimization result as the initial value. Simultaneously, system operating data is recorded, and data storage uses a circular buffer structure with a buffer size equivalent to one hour of data. The main purpose of this step is to ensure the stability and reliability of the charging process.

[0195] The specific implementation of step S08 involves first determining the completion status of the charging task based on the metering data output by the metering module. The determination conditions include: the charging capacity reaching the set value, the charging time reaching the maximum limit, the user actively ending the charging process, and the system detecting a charging fault. Then, the charging termination process is executed. First, the charging power is reduced using a soft-stop strategy, with the power reduction rate controlled within 10% per second. Next, the charging contactor is disconnected, and the charging circuit voltage is detected for 1 second with a detection interval of 10 milliseconds. Then, the charging gun unlocking procedure is executed, with the unlocking signal lasting for 100 milliseconds. Finally, the charging record data is saved, and the charging settlement information is displayed. The data is stored using a ferroelectric memory with power-off protection, with a storage capacity of 5 years of charging records. The main purpose of this step is to achieve safe termination of the charging process and payment settlement.

[0196] To better understand and implement this invention, the following is a specific application scenario of this invention, Example 2:

[0197] In developing a new generation of charging piles, a research and development team addressed the issues of uneven power distribution and unstable temperature control inherent in traditional dual-gun charging piles by developing a dual-gun charging control scheme based on feature decomposition and multi-objective optimization. This scheme was first tested in a laboratory environment and then demonstrated at a real charging station. During the laboratory testing phase, the team used a prototype with a rated power of 240kW, with an ambient temperature of 25 degrees Celsius and a relative humidity of 65%. During the testing, loads of 100kW and 140kW were used to simulate the charging needs of two different vehicle models, with data collected every 10 minutes. Key parameters during the charging process are shown in Table 1.

[0198] Table 1 Key Parameters for Dual-Gun Charging Process

[0199]

[0200] Based on the collected data, the system calculates the charging interface impedance value using the following formula: The dynamic impedance coefficient The value is 0.2, which is the initial impedance correction factor. The value is 0.1, representing the time decay coefficient. The value was set to 0.01. Calculation results show that the impedance values ​​of the two charging guns fluctuate within a reasonable range, effectively ensuring charging safety.

[0201] Table 2 shows the statistics of the system's status parameters during operation:

[0202] Table 2 System Operating Status Parameter Statistics Table

[0203]

[0204] Figure 2 (System operating status parameter distribution chart): A grouped bar chart is used to show the minimum, average, and maximum values ​​of four key status parameters (power coordination, temperature equalization coefficient, energy storage capacity coefficient, and power compensation coefficient). A charging state matrix is ​​constructed based on the collected data, and the principal eigenvectors are extracted through eigenvalue decomposition. The distribution of the calculated eigenvalues ​​is shown in Table 3.

[0205] Table 3. Distribution of Eigenvalues ​​of the Charging State Matrix

[0206]

[0207] Figure 3 (Eigenvalue Distribution and Contribution Rate Analysis Chart): This chart shows the eigenvalue distribution and cumulative contribution rate of the charging state matrix. A bar chart represents the eigenvalue magnitudes, while a line chart displays the cumulative contribution rate, employing a dual-axis design. The chart shows the distribution of the five eigenvalues, clearly displaying the contribution of the main eigenvalues. The eigenvectors corresponding to the first three eigenvalues ​​are selected to construct the power feature space. The formula for constructing the power feature space is: Where gradient weight coefficients The value is set to 0.1. The power coordination degree of the dual-gun system is calculated based on the constructed power characteristic space. The calculation formula is as follows: The power balance coefficient The value is 0.2, representing the power difference attenuation coefficient. The value is 0.01.

[0208] The temperature control effect during the charging process is shown in Table 4:

[0209] Table 4 Comparison of Temperature Control Effects

[0210]

[0211] Figure 4 (Temperature Change Trend Graph): This graph shows the temperature change trends of the dual charging guns and the heat sink during the charging process. A smooth curve is used to illustrate the temperature change over time, including scatter plots of the actual sampling points. The graph contains three curves: the temperature of charging gun 1, the temperature of charging gun 2, and the temperature of the heat sink, clearly showing the temperature change trend and temperature difference. The system uses a set of charging optimization equations to correct the power compensation coefficient. The changes in the objective function value during the optimization process are shown in Table 5.

[0212] Table 5. Changes in the Objective Function Value

[0213]

[0214] Figure 5(Convergence Process of Optimized Objective Functions): This section demonstrates the trends of the three objective function values ​​in the charging optimization equations as the number of iterations increases. Smooth curves are used to illustrate the convergence process of the power equalization objective value, temperature limitation objective value, and voltage correction objective value, including scatter plots of actual iteration points. After three months of laboratory testing and one month of field demonstration operation, the charging pile system exhibited excellent performance. Compared to traditional dual-gun charging control schemes, this invention has the following advantages: Traditional schemes mainly employ simple power allocation strategies, considering only the balance of charging power, failing to fully consider temperature equalization and system stability, and using fixed control parameters, resulting in poor adaptability. This invention, however, uses eigenvalue decomposition to extract the main features of the charging state, constructs a power feature space to achieve intelligent power allocation, and utilizes a temperature feature matrix for temperature equalization control, achieving coordinated control of power, temperature, and voltage through the charging optimization equations. Test data shows that this invention improves power allocation balance by 25%, temperature control accuracy by 35%, system operational stability by 40%, and charging efficiency by 15%, significantly enhancing the overall performance of the charging pile. Furthermore, this invention employs a hierarchical sampling frequency strategy, with the data acquisition module sampling at 100 times per second, the safety monitoring module sampling at 10 times per second, and the metering module sampling at 1 time per second. This rationally allocates system resources and improves control accuracy and system reliability.

[0215] It should be noted that the variables involved in this invention are explained in detail in Table 6 below.

[0216] Table 6. Variable Explanation Table

[0217]

[0218] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An integrated dual-gun DC charging pile, characterized in that, The system includes a main control module, a dual-channel power conversion module, a communication interface module, a data acquisition module, a charging control module, a display and interaction module, a safety monitoring module, an energy storage module, a metering module, and a power management module. The main control module has a built-in control chip and is electrically connected to each functional module. The control chip contains a system control module. The system control module extracts the main features by performing eigenvalue decomposition on the charging state matrix, constructs a power feature space based on the main feature vector to achieve optimized power allocation between the two charging guns, uses a temperature feature matrix for temperature equalization control, and corrects the power compensation coefficient through a set of charging optimization equations. The dual-channel power conversion module is used to convert AC power to DC power and regulate the power. The system control module is used to perform the following steps: First, it uses the data acquisition module to collect dual-gun charging voltage, charging current, charging temperature, and input voltage fluctuation values. Based on the charging voltage and charging current values, it calculates the charging interface impedance, constructs a charging state matrix, and analyzes the charging state matrix to calculate historical power fluctuation values. Second, it performs eigenvalue decomposition on the charging state matrix, extracts the principal feature vector, constructs a power feature space based on the principal feature vector, calculates the dual-gun power coordination degree, and generates a power allocation coefficient. Third, it uses the safety monitoring module to collect charging temperature, ambient temperature, and radiator temperature data, constructs a temperature feature matrix, and calculates the dual-gun temperature balance coefficient. Finally, based on the power allocation coefficient and the temperature balance coefficient, combined with the energy storage capacity coefficient output by the energy storage module, it calculates the dual-gun power compensation coefficient.

2. The integrated dual-gun DC charging pile according to claim 1, characterized in that, The system control module further performs the following steps: correcting the power compensation coefficient using a set of charging optimization equations, which includes a power equalization equation, a temperature limiting equation, and a voltage correction equation; and adjusting the dual-gun output power value through the charging control module based on the corrected power compensation coefficient to achieve dual-gun charging equalization control. The dual-gun charging status is monitored in real time. When the charging voltage, charging current, and charging temperature values ​​fluctuate beyond the preset threshold, power optimization control is re-executed. Based on the charging power value, charging cost value, and cumulative charging time value output by the metering module, the completion status of the charging task is determined, and the charging process is controlled to end.

3. The integrated dual-gun DC charging pile according to claim 2, characterized in that, The power allocation coefficient indicates the power allocation ratio between the two guns, and is calculated based on the power feature space and the power coordination degree of the two guns; the temperature balance coefficient represents the temperature difference between the two guns, and is calculated based on the temperature feature matrix; the power compensation coefficient is the power ratio value that needs to be adjusted to achieve the balance between the two guns, and is calculated based on the power allocation coefficient; the energy storage capacity coefficient is the ratio of the remaining power of the energy storage module to the rated power.

4. The integrated dual-gun DC charging pile according to claim 3, characterized in that, The charging interface impedance value is the ratio of the charging voltage value to the charging current value; the historical power fluctuation value is the temporal variation amplitude of the charging power; the dual-gun power coordination degree is the quantitative value of the balance of the dual-gun power distribution; the main feature vector is the main feature component obtained by eigenvalue decomposition of the charging state matrix; the power feature space is the power distribution optimization space constructed based on the main feature vector.

5. The integrated dual-gun DC charging pile according to claim 4, characterized in that, The input parameters of the power balance equation include the power allocation coefficient, the power compensation coefficient, the energy storage capacity coefficient, the cumulative value of charging time, and the historical power fluctuation value, and the output parameter is the power adjustment coefficient.

6. The integrated dual-gun DC charging pile according to claim 5, characterized in that, The temperature limiting equation has the following input parameters: temperature equalization coefficient, charging temperature value, ambient temperature value, radiator temperature value, and temperature threshold. The output parameter is the temperature correction coefficient.

7. The integrated dual-gun DC charging pile according to claim 6, characterized in that, The input parameters of the voltage correction equation include the charging voltage value, the power regulation coefficient, the temperature correction coefficient, the input voltage fluctuation value, and the charging interface impedance value, and the output parameter is the voltage correction coefficient.

8. The integrated dual-gun DC charging pile according to claim 7, characterized in that, The preset threshold for voltage fluctuation is 3% of the rated value, the preset threshold for current fluctuation is 5% of the rated value, and the preset threshold for temperature fluctuation is 5 degrees Celsius.

9. The integrated dual-gun DC charging pile according to claim 8, characterized in that, The communication interface module is used to realize data interaction with the background management system and the charging terminal. The data acquisition module is used to collect charging voltage, charging current and charging temperature data. The charging control module is used to control the charging process and execute the charging strategy. The display and interaction module is used to display the charging status and charging parameters and receive user input. The safety monitoring module is used to monitor the charging gun connection status and system faults. The energy storage module is used to temporarily store electrical energy and provide backup power. The metering module is used to measure the charging power and charging cost. The power management module is used to allocate system power and perform power scheduling. The sampling frequency of the data acquisition module is 100 times per second, the sampling frequency of the safety monitoring module is 10 times per second, and the sampling frequency of the metering module is 1 time per second.

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