Integrated one-machine double-gun direct current charging pile

Abstract

The invention, which belongs to the technical field of electrical variable adjustment, provides an integrated one-machine double-gun direct-current charging pile comprising a main control module, a double-path power conversion module, a communication interface module, a data acquisition module, a charging control module, a display interaction module, a safety monitoring module, an energy storage module, a metering module and a power management module. The main control module is internally provided with a control chip, a system control module in the control chip firstly obtains charging state data through a data acquisition module, performs eigenvalue decomposition on a charging state matrix to extract a main eigenvector, and constructs a power feature space to calculate a power distribution coefficient. Constructing a temperature characteristic matrix based on the temperature data, and calculating a power compensation coefficient in combination with the energy storage capacity; multi-parameter collaborative optimization is carried out through the charging optimization equation set, accurate power balance control is achieved, and the technical problem that in the prior art, a double-gun direct current charging pile is difficult to achieve accurate power balance distribution under the dynamic load condition is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of regulating electrical variables, and more particularly, relates to an integrated DC charging pile with two guns in one machine. Background Art

[0002] With the rapid development of the new energy vehicle industry, high-power DC charging piles have become an important part of the charging infrastructure. To improve the charging efficiency, the two-gun charging pile technology has been widely applied. Traditional two-gun charging technologies mainly adopt fixed power distribution schemes, and control the output power of the two guns through preset distribution ratios or simple dynamic adjustment algorithms. This scheme can meet the basic charging needs under stable working conditions and achieve the initial distribution of the power supply. Currently, common two-gun charging piles on the market mainly adopt technical means such as power limit method, polling distribution method, and priority distribution method. The power limit method ensures that the total system power does not exceed the design limit by setting a maximum output power threshold; the polling distribution method alternately allocates more power to the two charging guns according to a preset time sequence; the priority distribution method determines the power distribution scheme according to the priority set by the user. These technologies have been applied to a certain extent in engineering practice, forming a relatively complete charging control system.

[0003] However, traditional two-gun charging technologies have obvious deficiencies. First of all, the fixed power distribution scheme is difficult to adapt to dynamic load changes. When the load of one charging gun suddenly changes, it is unable to timely adjust the output power of the other charging gun, resulting in a lag in the system response. Secondly, the simple dynamic adjustment algorithm fails to fully consider the temperature balance problem, and local overheating is likely to occur during long-term operation. Thirdly, the existing technologies lack in-depth analysis of the charging state, are unable to accurately identify the key features during the charging process, and it is difficult to achieve precise power control. In addition, traditional schemes have deficiencies in energy storage management, and fail to effectively use the energy storage module for power compensation, affecting the dynamic response ability of the system. In practical applications, these problems lead to a series of technical bottlenecks such as reduced charging efficiency, uneven equipment heating, and poor system stability.

[0004] Under dynamic load conditions, the two-gun charging pile faces more severe challenges in power balance control. Due to various uncertain factors during the charging process of electric vehicles, such as battery state changes, environmental temperature fluctuations, user operation interference, etc., the charging load shows strong dynamic characteristics. Traditional technologies are difficult to accurately capture these dynamic features, resulting in a deviation between the power distribution scheme and the actual demand. Especially in high-power fast charging scenarios, inaccurate power distribution not only affects the charging efficiency, but also may endanger the system safety. Therefore, how to achieve precise power balance distribution under dynamic load conditions has become the core technical problem to be solved urgently. Summary of the Invention

[0005] In view of this, the present invention provides an integrated one-machine two-gun DC charging pile, which can solve the technical problem in the prior art that it is difficult to achieve precise power balance distribution for a two-gun DC charging pile under dynamic load conditions.

[0006] The present invention is implemented as follows: The present invention provides an integrated one-machine two-gun DC charging pile, which 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 is built with a control chip, and the main control module is electrically connected to each functional module. The control chip is provided with 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 eigenvector to achieve dual-gun power optimization distribution, performs temperature balance control using the temperature feature matrix, and corrects the power compensation coefficient through the charging optimization equation set. The dual-channel power conversion module is used to convert alternating current into direct current and perform power regulation. 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 acquire 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 state and charging parameters and receive user input. The safety monitoring module is used to monitor the charging gun connection state and system faults. The energy storage module is used to temporarily store electrical energy and provide a backup power supply. The metering module is used to meter the charging electricity and charging fees. The power management module is used to distribute the system power and perform power scheduling. Among them, 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.

[0007] Among them, the system control module is used to execute the following steps: Use the data acquisition module to acquire 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 a charging state matrix, and analyze the charging state matrix to calculate the historical power fluctuation value; perform eigenvalue decomposition on the charging state matrix, extract the main eigenvector, construct a power feature space based on the main eigenvector, calculate the dual-gun power coordination degree, and generate a power distribution coefficient; use the safety monitoring module to acquire the charging temperature value, ambient temperature value, and radiator temperature value data, construct a temperature feature matrix, and calculate the dual-gun temperature balance coefficient; based on the power distribution coefficient and the temperature balance coefficient, combined with the energy storage capacity coefficient output by the energy storage module, calculate the dual-gun power compensation coefficient.

[0008] Among them, the system control module further performs the following steps: correcting the power compensation coefficient by using a charging optimization equation set, where the charging optimization equation set includes a power balance equation, a temperature limit equation, and a voltage correction equation; adjusting the dual-gun output power value through the charging control module according to the corrected power compensation coefficient to achieve dual-gun charging balance control; monitoring the dual-gun charging status in real time, and when the fluctuations of the charging voltage value, charging current value, and charging temperature value exceed the preset threshold, re-performing power optimization control; judging the completion status of the charging task based on the charging power value, charging cost value, and cumulative charging duration value output by the metering module, and controlling the end of the charging process.

[0009] Among them, the power distribution coefficient indicates the power distribution ratio value between the two guns, which is calculated based on the power feature space and the dual-gun power coordination degree; the temperature balance coefficient characterizes the dual-gun temperature difference value, which is calculated based on the temperature feature matrix; the power compensation coefficient is the power ratio value required to be adjusted to achieve dual-gun balance, which is calculated based on the power distribution coefficient; the energy storage capacity coefficient is the ratio of the remaining power of the energy storage module to the rated power.

[0010] Among them, 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 time-sequential change amplitude of the charging power; the dual-gun power coordination degree is the quantization value of the balance degree of the dual-gun power distribution; the main eigenvector is the main eigencomponent obtained by eigenvalue decomposition of the charging state matrix; the power feature space is the power distribution optimization space constructed based on the main eigenvector.

[0011] Among them, the input parameters of the power balance equation include the power distribution coefficient, the power compensation coefficient, the energy storage capacity coefficient, the cumulative charging duration value, and the historical power fluctuation value, and the output parameter is the power adjustment coefficient.

[0012] Among them, the input parameters of the temperature limit equation include the temperature balance 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.

[0013] Among them, the input parameters of the voltage correction equation 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.

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

[0015] Among them, 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 100 Mbps.

[0016] Compared with the prior art, the present invention provides an integrated one-machine two-gun DC charging pile, and the present invention proposes a dual-gun charging balancing method based on eigenvalue decomposition and multi-level optimization control. This method performs eigenvalue decomposition on the charging state matrix, extracts the main eigenvector to construct the power feature space, and realizes the accurate characterization of the dynamic characteristics of the charging process. Combining the temperature feature matrix and energy storage capacity control, a complete charging optimization equation set is established to achieve the coordinated control of power, temperature, and voltage. Adopting a hierarchical sampling strategy ensures the real-time performance and reliability of the system.

[0017] The technical solution of the present invention effectively solves the problems existing in the traditional technology. First, through the eigenvalue decomposition method, the system can accurately identify the key characteristics during the charging process, providing a reliable basis for power balancing control. Second, based on the balancing control strategy of the temperature feature matrix, uniform heating of the charging equipment is achieved, avoiding local overheating. Third, through the intelligent allocation of the energy storage module, the dynamic response ability of the system is enhanced. Finally, the introduction of the charging optimization equation set realizes the coordinated optimization of multiple parameters, significantly improving the control accuracy. These technological innovations enable the present invention to maintain stable and reliable charging performance under dynamic load conditions.

[0018] The present invention successfully solves the technical problem that it is difficult to achieve accurate power balancing distribution in a two-gun DC charging pile under dynamic load conditions in the prior art, which benefits from its innovations in theory and implementation. At the theoretical level, the eigenvalue decomposition method provides a mathematical basis for power distribution, enabling the system to accurately grasp the essential characteristics of the charging process. At the implementation level, the multi-level optimization control strategy ensures the practicality and reliability of the control scheme. Through the construction of the power feature space, the system realizes the accurate tracking of dynamic loads and makes real-time adjustments through the optimization equation set, ultimately achieving the goal of accurate power balance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the composition of the integrated one-machine two-gun DC charging pile of the present invention.

[0020] Figure 2 It is a distribution diagram of the system operation state parameters in Embodiment 2.

[0021] Figure 3 It is an analysis diagram of eigenvalue distribution and contribution rate in Embodiment 2.

[0022] Figure 4It is the temperature change trend graph in Embodiment 2.

[0023] Figure 5 It is the convergence process of the optimization objective function in Embodiment 2.

[0024] Figure 6 It is the flowchart of the system control module. Detailed implementation manners

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

[0026] As Figure 1 shown, it is a schematic diagram of the composition of an integrated one-machine two-gun DC charging pile provided by the present invention. The charging pile 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 is built-in with a control chip. The main control module is electrically connected to the dual-channel 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 respectively. The control chip is provided with a system control module. The dual-channel power conversion module is used to convert alternating current into direct current and perform power regulation. 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 electric energy and provide a backup power supply. The metering module is used to measure the charging power and charging cost. The power management module is used to distribute the 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 the main features of the charging status through eigenvalue decomposition, constructs a power feature space to realize the optimal allocation of the two-gun power. It performs temperature equilibrium control using the temperature feature matrix and combines the energy storage capacity for power compensation. It realizes the coordinated control of power, temperature, and voltage through the charging optimization equations to ensure the stability and balance of two-gun charging. It adopts a hierarchical sampling frequency strategy to ensure the system operation efficiency and safety. As Figure 6 shown, the system control module is used to execute the following steps: 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 a charging state matrix, and analyze the charging state matrix to calculate the historical power fluctuation value; S02. Perform eigenvalue decomposition on the charging state matrix, extract the main eigenvector, construct a power feature space based on the main eigenvector, calculate the dual-gun power coordination degree, and generate a power distribution coefficient; S03. Use the safety monitoring module to collect data such as the charging temperature value, ambient temperature value, radiator temperature value, charging voltage value, and charging current value, construct a temperature feature matrix, and calculate the dual-gun temperature equilibrium coefficient; S04. Based on the power distribution coefficient and the temperature equilibrium coefficient, combined with the energy storage capacity coefficient output by the energy storage module, calculate the dual-gun power compensation coefficient; S05. Use the charging optimization equations to correct the power compensation coefficient. The charging optimization equations include a power balance equation, a temperature limit equation, and a voltage correction equation; S06. According to the corrected power compensation coefficient, adjust the dual-gun output power value through the charging control module to achieve dual-gun charging balance control; S07. Real-time monitor the dual-gun charging state. When the fluctuations of the charging voltage value, charging current value, and charging temperature value exceed the preset threshold, re-execute steps S01 to S06; S08. Based on the charging power value, charging cost value, and cumulative charging duration value output by the metering module, judge the charging task completion status and control the end of the charging process.

[0028] The power distribution coefficient indicates the power distribution ratio value between the two guns, which is calculated based on the power feature space and the dual-gun power coordination degree; the temperature equilibrium coefficient characterizes the dual-gun temperature difference value, which is calculated based on the temperature feature matrix; the power compensation coefficient is the power ratio value required to achieve dual-gun balance, which is calculated based on the power distribution coefficient; the energy storage capacity coefficient is the ratio of the remaining power of the energy storage module to the rated power; 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 change amplitude of the charging power; the dual-gun power coordination degree is the quantitative value of the balance degree of dual-gun power distribution; the main eigenvector 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 eigenvector.

[0029] The input parameters of the power balance equation include the power distribution coefficient, the power compensation coefficient, the energy storage capacity coefficient, the cumulative value of the charging duration, and the historical power fluctuation value, and the output parameter is the power adjustment coefficient; the input parameters of the temperature limit equation include the temperature balance 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 input parameters of the voltage correction equation 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.

[0030] The following describes the specific implementation manners of the above steps in detail.

[0031] The main control module uses an ARM Cortex M7 series processor as the control chip, with the main frequency set at 400 MHz, built-in 2 MB flash memory and 512 KB random access memory, conducts data interaction 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 system response.

[0032] 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 20 kHz, an adjustable output voltage range of 200 to 1000 V, and a maximum output power of 240 kW. An LC filter circuit is configured at the rear stage of the rectifier circuit, with an inductance value of 2 mH and a capacitance value of 4700 μF, used to suppress voltage ripples. The power conversion circuit adopts a phase-shifted full-bridge topology structure, and the switching device selects a 1700 V / 400 A silicon carbide field effect transistor, and zero-voltage switching is achieved using soft-switching technology to reduce switching losses.

[0033] The communication interface module includes an Ethernet interface and a mobile communication interface. Among them, the Ethernet interface uses a gigabit Ethernet controller, supports the TCP / IP protocol stack, and realizes high-speed data exchange with the background management system; the mobile communication interface uses a 4G communication module, supports multiple communication protocols, realizes real-time data interaction with the charging terminal, and the communication rate can reach 100 Mbps.

[0034] The data acquisition module uses a 24-bit analog-to-digital converter, with a sampling accuracy reaching 0.1%, has 8 analog input channels, a sampling frequency of 100 times per second, a voltage signal acquisition range of 0 to 1000 V, a current signal acquisition range of 0 to 400 A, and a temperature acquisition range of -40 to 150 degrees Celsius.

[0035] The charging control module uses a digital signal processor to implement the execution of the charging strategy. It is built-in with a pulse width modulation controller, with a modulation frequency of 20 kHz and an adjustable dead time range of 0.5 to 5 microseconds. It has protection functions such as overvoltage, overcurrent, and overtemperature, and the response time is less than 10 microseconds.

[0036] The display and interaction module uses a 7-inch liquid crystal display with a resolution of 1024 by 768 pixels. It uses a capacitive touch screen to achieve human-computer interaction. The display interface includes content such as charging status, charging parameters, and fault information, and supports Chinese and English display.

[0037] The safety monitoring module uses a dual-channel independent monitoring circuit with a monitoring frequency of 10 times per second. It includes a charging gun locking detection circuit and a system fault detection circuit. The charging gun locking detection uses a Hall sensor, and the system fault detection includes functions such as insulation detection, grounding detection, and leakage current detection.

[0038] The energy storage module uses a lithium iron phosphate battery pack with a rated capacity of 30 kWh, a working voltage range of 600 to 800 V, and a maximum charge and discharge power of 60 kW. It is configured with a battery management system to achieve battery status monitoring and protection, and has an equalization charging function.

[0039] The metering module uses a 0.5S-level electrical energy metering chip with a measurement accuracy better than 0.5%. The sampling frequency is 1 time per second, and it can achieve the metering of electrical energy parameters such as forward active electrical energy, reverse active electrical energy, and power factor, and has a real-time billing function.

[0040] The power management module uses a multi-channel DC conversion circuit with an input voltage range of 180 to 264 V. The output voltages include multiple DC power supplies such as 12 V, 5 V, and 3.3 V, with a total power of 2000 W. It has protection functions such as overvoltage, overcurrent, and short circuit, and the power conversion efficiency is greater than 95%.

[0041] Among them, the system control module is used to execute the following steps: The specific implementation of step S01 is to collect dual-gun charging data through a data acquisition module at a sampling frequency of 100 times per second, including charging voltage value, charging current value, charging temperature value, and input voltage fluctuation value. The sampled data is processed by digital filtering to eliminate interference. The filtering uses the Butterworth low-pass filtering algorithm with a cut-off frequency set to 1 kHz. Based on the collected charging voltage value and charging current value, the least squares method is used to calculate the charging interface impedance value. When calculating, 50 sampling point data are used for fitting to improve the impedance calculation accuracy. When constructing the charging state matrix, parameters such as voltage, current, and temperature are arranged in time series to form a state vector. Each state vector contains 100 sampling point data, and 10 consecutive state vectors are combined to form the charging state matrix. When analyzing the charging state matrix to calculate the historical power fluctuation value, the sliding window method is used to calculate the power change rate. The window length is 1 second, and the sliding step size is 0.1 second. 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 obtain the real-time operation state data of the charging system and provide a data basis for subsequent power optimization control.

[0042] The specific implementation of step S02 is to perform singular value decomposition on the charging state matrix to extract the main eigenvectors. The eigenvalue decomposition uses the QR decomposition algorithm, and the eigenvalue threshold is set to 0.1. Only the eigenvectors corresponding to the eigenvalues greater than the threshold are retained. Based on the extracted main eigenvectors, a power feature space is constructed. The space dimension is determined by the number of eigenvectors, usually 3 to 5 dimensions. In the power feature space, the cosine similarity algorithm is used to calculate the dual-gun power coordination degree. The coordination degree ranges from 0 to 1, and the closer the coordination degree is to 1, the more balanced the dual-gun power distribution is. Based on the power coordination degree, a power distribution coefficient is generated. A nonlinear mapping function is used to convert the coordination degree into the distribution coefficient. The mapping function is an exponential function, and the function parameters are determined by experimental optimization. The main purpose of this step is to extract the key features of the charging state through mathematical transformation and achieve the optimal distribution of charging power.

[0043] The specific implementation of step S03 is to use a safety monitoring module to collect temperature-related data at a sampling frequency of 10 times per second, including charging temperature value, ambient temperature value, radiator temperature value, and at the same time collect the charging voltage value and charging current value. The collected temperature data is processed by median filtering, and the filtering window length is 5 sampling points. The processed temperature data is arranged in time series to construct a temperature feature matrix. The matrix dimension is 5 by 3, and the matrix elements are temperature sampling values. Based on the temperature feature matrix, the dual-gun temperature equilibrium coefficient is calculated. The calculation method is to use matrix singular value decomposition to extract the temperature change characteristics, calculate the root mean square value of the dual-gun temperature difference, and map the calculation result to the 0 to 1 interval through the sigmoid function to obtain the temperature equilibrium coefficient. The main purpose of this step is to achieve temperature monitoring and equilibrium control of the charging system.

[0044] The specific implementation of step S04 is to calculate the dual-gun power compensation coefficient using the weighted average method based on the power distribution coefficient, temperature balance coefficient, and the energy storage capacity coefficient output by the energy storage module. The weight of the power distribution coefficient is 0.5, the weight of the temperature balance coefficient is 0.3, and the weight of the energy storage capacity coefficient is 0.2. The weight coefficients are determined by experimental optimization. The energy storage capacity coefficient is the ratio of the remaining power of the energy storage module to the rated power. When this ratio is less than 0.2, the system will limit the maximum charging power. The calculation of the power compensation coefficient uses a piecewise linear function. When the temperature balance coefficient is less than 0.8, the power compensation intensity 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 balance, and energy storage capacity, and calculate reasonable power compensation parameters.

[0045] The specific implementation of step S05 is to correct the power compensation coefficient using a charging optimization equation set, which includes a power balance equation, a temperature limit equation, and a voltage correction equation. The power balance equation uses a nonlinear programming model. The input parameters include the power distribution coefficient, power compensation coefficient, energy storage capacity coefficient, cumulative charging duration value, and historical power fluctuation value. The gradient descent method is used to solve the optimal power adjustment coefficient, with an iteration step size of 0.01, a maximum number of iterations of 100 times, and a convergence threshold of 0.001. The temperature limit equation uses a fuzzy control algorithm. The input parameters include the temperature balance coefficient, charging temperature value, ambient temperature value, radiator temperature value, and temperature threshold. The temperature threshold is set as follows: the charging temperature does not exceed 90 degrees Celsius, the ambient temperature does not exceed 45 degrees Celsius, and the radiator temperature does not exceed 75 degrees Celsius. The fuzzy control rules are set based on expert experience, and the temperature state is divided into three levels: low temperature, moderate temperature, and high temperature, and the temperature correction coefficient is output. The voltage correction equation uses an adaptive control algorithm. The input parameters include the charging voltage value, power adjustment coefficient, temperature correction coefficient, input voltage fluctuation value, and charging interface impedance value. 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 coordinated action of multiple optimization equations.

[0046] The specific implementation of step S06 is to adjust the output power values of the two charging guns through the charging control module according to the corrected power compensation coefficient. First, the power compensation coefficient is converted into a power adjustment instruction, and the conversion process uses the look-up table method with a look-up precision of 0.01. The target values of the charging voltage and charging current are calculated according to the power adjustment instruction, and the calculation uses the proportional-integral control algorithm with a proportional coefficient of 0.8 and an integral time constant of 0.1 second. A control signal is output through the pulse width modulation controller to control the on-time of the switching tubes of the power conversion circuit, so as to achieve precise adjustment of the charging power. The soft start strategy is adopted during the power adjustment process, and the power change rate is limited within 20% per second. At the same time, the voltage of the charging interface is monitored. When the voltage fluctuation exceeds 5%, the voltage feed-forward control is started to suppress the influence of the power grid fluctuation on the charging process. The main purpose of this step is to execute the power balance control strategy to ensure the stability of the two-gun charging.

[0047] The specific implementation of step S07 is to monitor the two-gun charging status in real time, and the monitored parameters include the charging voltage value, the charging current value, and the charging temperature value. The Kalman filter algorithm is used to process the monitored data to remove the influence of measurement noise. The fluctuation thresholds of the monitored parameters are set as follows: the voltage fluctuation does not exceed 3% of the rated value, the current fluctuation does not exceed 5% of the rated value, and the temperature fluctuation does not exceed 5 degrees Celsius. When the fluctuation of the monitored parameters exceeds the preset threshold, the charging optimization control process is triggered to execute again. When executing again, the warm start strategy is adopted, and the previous optimization result is used as the initial value to improve the optimization convergence speed. The system operation data is recorded simultaneously during the monitoring process, and the data storage uses a circular buffer structure with a buffer size of one hour's worth of data. The main purpose of this step is to ensure the stability and reliability of the charging process.

[0048] The specific implementation of step S08 is to judge the charging task completion status based on the charging power value, the charging cost value, and the cumulative charging duration value output by the metering module. The metering module outputs the metering data once per second, and the data includes parameters such as forward active power, power factor, and charging duration. The charging task completion judgment uses the multi-condition combination judgment method, and the judgment conditions include: the charging power reaches the set value, or the charging duration 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, executing the soft stop strategy, and controlling the power reduction rate within 10% per second; disconnecting the charging contactor and detecting the charging loop voltage; executing the charging gun unlocking program; 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 cost settlement.

[0049] The following details the calculation processes, matrices, and equations involved in the present invention.

[0050] The expression of the charging status matrix is as follows: ; Wherein, is the voltage value of the th charging gun at the th sampling moment, with the unit of volt; is the current value of the th charging gun at the th sampling moment, with the unit of ampere; is the temperature value of the th charging gun at the th sampling moment, with the unit of degree Celsius; is the length of the sampling time window, with the value of 100; is the number of charging guns, with the value of 2.

[0051] The calculation formula for the impedance value of the charging interface is: ; Wherein, is the impedance value of the charging interface, with the unit of ohm; is the charging voltage value, with the unit of volt; is the charging current value, with the unit of ampere; is the dynamic impedance coefficient, with the value range of 0.1 to 0.3; is the initial impedance correction coefficient, with the value range of 0.05 to 0.15; is the time decay coefficient, with the value of 0.01; is the charging time, with the unit of second.

[0052] The extraction of the main eigenvector adopts the singular value decomposition method, and its mathematical expression is: ; Wherein, is the charging state matrix; is the left singular matrix; is the singular value diagonal matrix; is the right singular matrix; the main eigenvector is the right singular vectors with the largest singular value, and the value of

[0053] The expression for constructing the power eigen-space is: ; Wherein, is the power eigen-space; is the th eigenvalue; is the th eigenvector; is the gradient weight coefficient, with the value of 0.1; is a non - linear mapping function of the eigenvector; is the gradient operator.

[0054] The calculation formula for the dual - gun power coordination degree is: ; In the formula, is the power coordination degree; , are the power eigenvectors of the two charging guns; is the power balance coefficient, with a value of 0.2; is the power difference attenuation coefficient, with a value of 0.01; is the power difference between the two charging guns, with the unit of kilowatt.

[0055] The expression of the temperature characteristic matrix is: ; In the formula, is the charging temperature value at the th sampling point; is the ambient temperature value at the th sampling point; is the radiator temperature value at the th sampling point; is the number of temperature sampling points, with a value of 5.

[0056] The calculation formula for the temperature equilibrium coefficient is: ; In the formula, is the temperature equilibrium coefficient; is the temperature difference sensitivity coefficient, with a value of 0.1; is the temperature difference between the two charging guns; is the radiator temperature influence coefficient, with a value of 0.3; is the highest temperature of the radiator; is the rated temperature of the radiator.

[0057] The calculation formula for the power compensation coefficient is: ; In the formula, is the power compensation coefficient; , , are the weights of the power coordination degree, the temperature equilibrium coefficient, and the energy storage capacity coefficient, with values of 0.5, 0.3, and 0.2 respectively; is the energy storage capacity coefficient; is the time compensation coefficient, with a value of 0.1; is the time constant, with a value of 0.001; is the charging time.

[0058] The expression of the power balance equation is: ; In the formula, is the power balance objective function; is the output power of the th charging gun; is the expected power value; , , are the weight coefficients, with values of 0.3, 0.2, and 0.1 respectively; is the historical power fluctuation value.

[0059] The expression of the temperature limit equation is: ; In the formula, is the temperature limit objective function; is the charging temperature of the th charging gun; is the radiator temperature of the th charging gun; , are the limits of the charging temperature and the radiator temperature respectively; , are the temperature weight coefficients, with values of 0.4 and 0.3 respectively; is the temperature difference weight coefficient, with a value of 0.3.

[0060] The expression of the voltage correction equation is: ; In the formula, is the voltage correction objective function; is the output voltage of the th charging gun; is the expected voltage value; , , are the voltage control weight coefficients, with values of 0.4, 0.3, and 0.3 respectively; is the voltage fluctuation value; is the impedance value of the charging interface.

[0061] The calculation formula of the historical power fluctuation value is: ; In the formula, is the historical power fluctuation value; is the The power values of the sampling points; is the average power; is the number of sampling points, with a value of 100; is the weight coefficient of the power change rate, with a value of 0.2.

[0062] The calculation formula for the energy storage capacity coefficient is: ; In the formula, is the energy storage capacity coefficient; is the current remaining power of the energy storage module; is the rated power of the energy storage module; is the power change rate coefficient, with a value of 0.1.

[0063] The design principles of the above equations are as follows: The charging interface impedance calculation takes into account the static impedance, dynamic impedance, and time decay characteristics, where the exponential term reflects the stable process of the contact impedance over time; The power feature space construction adopts a method combining eigenvalue decomposition and gradient optimization, which not only retains the main feature information but also introduces a non-linear mapping to improve the feature expression ability; The power coordination degree calculation combines the cosine similarity and the power difference compensation term, which can more accurately reflect the balance degree of the dual-gun power distribution; The temperature equilibrium coefficient adopts the sigmoid function characteristic, which can smooth the temperature difference and consider the influence of the radiator temperature; The power compensation coefficient calculation adopts the method of multi-factor weighting and time compensation, realizing the coordinated control of power, temperature, and energy storage; The three optimization equations are respectively for power balance, temperature limitation, and voltage correction. The quadratic objective function is used for easy solution, and the importance of each item is adjusted through the weight coefficient.

[0064] It should be noted that the establishment and derivation processes of some matrices or equations are described in detail as follows.

[0065] 1. Establishment of the charging state matrix: The charging state matrix is formed by collecting data and organizing it. The specific steps are as follows: The first step: Collect voltage, current, and temperature data every 0.01 seconds; The second step: Arrange the data of 100 consecutive sampling points in chronological order to form a 3n-column matrix, where n is the number of charging guns; The third step: Normalize the collected data to eliminate the influence of dimensions; The number of rows of the matrix reflects the time window length, and the number of columns reflects the integrity of the monitoring parameters. This matrix structure can simultaneously reflect the chronological change characteristics and mutual relationships of the parameters of the dual guns.

[0066] 2. Derivation of the calculation formula for the charging interface impedance value: The basic principle comes from the dynamic impedance measurement theory, and the derivation steps are as follows: Step 1: Establish the static impedance term , which reflects the basic Ohm's characteristics; Step 2: Introduce the dynamic impedance term , which characterizes the characteristics of impedance varying with current; Step 3: Add the time decay term , which describes the stabilization process of contact impedance over time; The parameters , , are obtained through experimental calibration. The experimental steps are as follows: measure the voltage response at different currents; calculate the static and dynamic impedance values; fit the time decay characteristics.

[0067] This equation can accurately reflect the dynamic impedance characteristics of the charging interface, which is beneficial to the stable control of the charging process.

[0068] 3. Deduction of the construction of the power characteristic space: Based on the principal component analysis theory and combined with the gradient optimization method, the derivation steps are as follows: Step 1: Perform singular value decomposition on the charging state matrix to obtain the eigenvalues and eigenvectors; Step 2: Select the eigenvectors corresponding to the largest k eigenvalues as the basis vectors; Step 3: Introduce the gradient optimization term , which enhances the feature expression ability; The non-linear mapping function adopts the ReLU function: ; This method for constructing the space can effectively extract the main features of the charging state and provide a basis for power optimization allocation.

[0069] 4. Steps for establishing the calculation formula of the double-gun power coordination degree: Based on the vector cosine similarity theory and combined with power difference compensation, the derivation steps are as follows: Step 1: Construct the basic cosine similarity term , which reflects the similarity degree of power distribution; Step 2: Introduce the power difference compensation term , which corrects the situation of large power differences; The parameters and are determined through the following experiment: test the charging effect under different power combinations; calculate the relationship between power difference and charging efficiency; fit the parameters by the least squares method.

[0070] This equation can accurately evaluate the balance degree of the double-gun power distribution.

[0071] 5. Steps for establishing the temperature equilibrium coefficient calculation: Using the sigmoid function characteristics and combining with the influence of the radiator temperature, the derivation steps are as follows: Step 1: Construct the basic sigmoid function term to achieve a smooth mapping of the temperature difference; Step 2: Add the radiator temperature influence term to consider the heat dissipation capacity; The parameters and are determined through temperature control experiments: Test the charging performance under different temperature differences; Analyze the influence of the radiator temperature on the system; Optimize the parameter values.

[0072] This equation can effectively balance the temperature difference between the two guns and protect the charging system.

[0073] 6. Steps for establishing the power compensation coefficient calculation: Based on the multi-factor weighted sum and time compensation principle, the derivation steps are as follows: Step 1: Establish the basic weighted sum term ; Step 2: Introduce the time compensation term to achieve dynamic adjustment; The weight coefficients are optimized through the following steps: Establish the charging performance evaluation index; Design the orthogonal experiment plan; Analyze the influence degree of each factor; Determine the optimal weight combination.

[0074] This equation realizes the collaborative optimization of power, temperature, and energy storage.

[0075] 7. Steps for establishing the power equilibrium equation: Based on the least squares optimization principle, the derivation steps are as follows: Step 1: Establish the power deviation square term ; Step 2: Introduce the compensation coefficient constraint term ; Step 3: Add the energy storage constraint term ; Step 4: Consider the historical fluctuation constraint term ; The weight coefficients 、 、 are determined through the following methods: Establish the power equilibrium performance index; Conduct parameter sensitivity analysis; Optimize the weight configuration.

[0076] This equation can achieve precise equilibrium control of power.

[0077] 8. Steps for establishing the temperature limit equation: Based on the temperature safety constraint theory, the derivation steps are as follows: Step 1: Construct the charging temperature constraint term ; Step 2: Add the radiator temperature constraint term ; Step 3: Introduce the temperature difference term ; The temperature limit value and the weight coefficient are determined through the following experiments: testing the system performance at different temperatures; analyzing the temperature safety boundary; optimizing the weight parameters.

[0078] This equation can effectively ensure the temperature safety during the charging process.

[0079] 9. Steps for establishing the voltage correction equation: Based on the voltage stability control theory, the derivation steps are as follows: Step 1: Establish the voltage deviation term ; Step 2: Introduce the voltage change rate constraint ; Step 3: Add the voltage fluctuation constraint ; Step 4: Consider the impedance influence term ; The weight coefficient is optimized through the following steps: analyzing the voltage stability requirements; testing the voltage response characteristics; optimizing the control parameters.

[0080] This equation can ensure the stability of the charging voltage.

[0081] 10. Steps for establishing the calculation of historical power fluctuation values: Based on statistical analysis and dynamic characteristics, the derivation steps are as follows: Step 1: Construct the power variance term ; Step 2: Introduce the power change rate term ; The parameters are determined through the analysis of operation data: collecting power fluctuation data; analyzing the fluctuation characteristics; determining the parameter values.

[0082] This equation can accurately describe the historical fluctuation characteristics of power.

[0083] 11. Steps for establishing the calculation of the energy storage capacity coefficient: Based on the energy storage state evaluation theory, the derivation steps are as follows: Step 1: Establish the basic capacity ratio term ; Step 2: Introduce the electricity quantity change rate term ; The parameters are calibrated through the battery management system: test the battery capacity characteristics; analyze the charging and discharging processes; optimize the parameter values.

[0084] This equation can accurately reflect the available capacity state of the energy storage module.

[0085] Specifically, the principle of the present invention is as follows: The core principle of the present invention is to reveal the internal characteristics of the charging process through eigenvalue decomposition method and construct an accurate power control model based on this. First, the charging state matrix contains multi-dimensional information such as voltage, current, and temperature. Through eigenvalue decomposition, the correlation characteristics between these parameters can be extracted. The main eigenvector reflects the main change trend of the charging state and provides a theoretical basis for power distribution. The power feature space constructed based on the main eigenvector realizes the low-dimensional representation of the charging state and simplifies 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 a foundation for achieving precise control.

[0086] At the control strategy level, the present invention adopts a multi-level optimization control method. The introduction of the temperature feature matrix solves the temperature balance problem in the traditional control scheme. By real-time monitoring the multi-point temperature data and constructing a temperature distribution model, precise control of the temperature field is achieved. The intelligent allocation strategy of the energy storage module enhances the dynamic response ability of the system. When the load suddenly changes, the energy storage module can promptly provide power compensation to maintain the system stability. The charging optimization equation set organically combines power balance, temperature limit, and voltage correction to form a complete control closed-loop. This multi-level control architecture ensures that the system can maintain good performance under various working conditions.

[0087] From the perspective of system implementation, the hierarchical sampling strategy of the present invention ensures the real-time and accuracy of data acquisition. The high-frequency sampling of the data acquisition module ensures the precise monitoring of the charging state. The medium-frequency sampling of the safety monitoring module meets the system protection requirements. The low-frequency sampling of the metering module balances the accuracy and efficiency. This hierarchical design fully considers the functional characteristics and performance requirements of each module, and reasonably allocates the system resources. Although the real-time calculation of eigenvalue decomposition increases the computational burden, through the optimization of algorithm design, it ensures that all necessary calculations can be completed within the control cycle to meet the real-time control requirements.

[0088] A specific embodiment 1 of the present invention is provided below. The specific implementation manners of each step in this embodiment 1 are described in detail as follows.

[0089] For the specific implementation of the main control module, an ARM Cortex M7 series processor is used as the control chip, with a main frequency set at 400 MHz, built-in 2 MB of flash memory and 512 KB of random access memory, and data interaction with other functional modules is carried out through multiple serial peripheral interfaces. The main control module uses a real-time operating system for task scheduling, with 5 priorities configured. Among them, the status monitoring task has the highest priority, the power control task comes second, and the data storage task has the lowest priority. The task scheduling period is 10 milliseconds. The system control module of the main control module adopts a hierarchical architecture design, including functional modules such as device management layer, communication management layer, data processing layer, and policy control layer, and uses an object-oriented programming method 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 the charging control policy. The system control module has a built-in real-time database for storing system operation parameters and historical data, and supports data query and statistical analysis functions. The system control module uses a watchdog circuit to implement system monitoring, the watchdog timing time is 1 second, and it has the functions of fault self-diagnosis and self-recovery.

[0090] The dual-channel power conversion module adopts a three-phase full-bridge rectifier circuit structure, the input voltage is 380V AC, the rectifier bridge uses 1700V silicon carbide diodes, the switching frequency is 20kHz, the adjustable range of the output voltage is 200 to 1000V, and the maximum output power is 240kW. An LC filter circuit is configured at the rear stage of the rectifier circuit, the inductance value is 2mH, the capacitance value is 4700μF, and the cut-off frequency of the filter circuit is 500Hz, which is used to suppress voltage ripple. The power conversion circuit adopts a phase-shifted full-bridge topology structure, and the switching device is selected as a 1700V / 400A silicon carbide field effect transistor. Zero-voltage switching is realized by using soft-switching technology, and the dead time is 1 microsecond to reduce switching losses. The control of the power conversion circuit adopts a double-closed-loop control structure of voltage outer loop and current inner loop, the outer loop bandwidth is 100Hz, the inner loop bandwidth is 1kHz, and the control period is 50 microseconds. The efficiency of the power conversion module is greater than 98%, the power factor is greater than 0.99, and the total harmonic distortion is less than 5%.

[0091] 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, has a communication rate of 1000 Mbps, and realizes high-speed data exchange with the background management system. The mobile communication interface uses a 4G communication module, supports multiple communication protocols, has a maximum uplink rate of 50 Mbps and a maximum downlink rate of 100 Mbps, and realizes real-time data interaction with the charging terminal. The communication interface module adopts a dual-machine hot standby structure. When the primary communication link fails, it can switch to the standby link within 100 milliseconds. The communication data is encrypted using the AES256 encryption algorithm to ensure the security of data transmission. The communication interface module has a data caching function with a cache capacity of 1 GB, and can cache 24 hours of operation data when the communication is interrupted.

[0092] The data acquisition module uses a 24-bit analog-to-digital converter with a sampling accuracy of 0.1%, has 8 analog input channels, and a sampling frequency of 100 times per second. The voltage acquisition circuit has a range of 0 to 1000 V, uses a resistor voltage division network for sampling, with a voltage division ratio of 1000:1, and the temperature coefficient of the voltage division resistor is less than 25 ppm / °C. The current acquisition circuit has a range of 0 to 400 A, uses a Hall sensor for sampling, with a sensor accuracy of 0.1% and a linearity better than 0.1%. The temperature acquisition circuit has a range of -40 to 150 °C, uses a PT100 platinum resistance for temperature measurement, with a temperature measurement accuracy of 0.1 °C. All sampled data is processed by digital filtering, using a Butterworth low-pass filter with a cut-off frequency of 1 kHz.

[0093] The charging control module uses a digital signal processor to implement the execution of the charging strategy, with a main frequency of 200 MHz, an internal pulse width modulation controller with a modulation frequency of 20 kHz, and an adjustable dead time range of 0.5 to 5 microseconds and a resolution of 20 nanoseconds. The charging control module has overvoltage, overcurrent, over-temperature and other protection functions, with a protection response time of less than 10 microseconds, an overvoltage protection threshold of 1100 V, an overcurrent protection threshold of 440 A, and an over-temperature protection threshold of 95 °C. The charging control algorithm uses a model predictive control method, with a prediction time domain of 10 control cycles, a control cycle of 50 microseconds, and optimization objectives including power balance, temperature balance and voltage stability. The controller uses an adaptive parameter tuning method and can automatically adjust the control parameters according to the charging state.

[0094] The display interaction module uses a 7-inch liquid crystal display with a resolution of 1024 by 768 pixels, a brightness of 500 nits, a contrast ratio of 1000:1, and a viewing angle of 178 degrees. The display screen uses a capacitive touch screen to achieve human-computer interaction, supports multi-touch, and the response time is less than 10 milliseconds. The display interface includes content such as charging status, charging parameters, and fault information, supports Chinese and English display, and the interface refresh frequency is 60Hz. The display interaction module uses an embedded graphics library for interface rendering, has an animation display function, and the animation frame rate is 30 frames per second. The update period of the display data is 100 milliseconds, and the display range of the historical data curve is the most recent 1 hour.

[0095] The safety monitoring module uses a dual-channel independent monitoring circuit with a monitoring frequency of 10 times per second, including a charging gun locking detection circuit and a system fault detection circuit. The charging gun locking detection uses a Hall sensor with a detection accuracy of 0.1 mm and a response time less than 5 milliseconds. The system fault detection includes functions such as insulation detection, grounding detection, and leakage current detection. The insulation resistance detection range is 0 to 20 megohms, the detection voltage is 500V, the leakage current detection range is 0 to 1000 mA, and the detection accuracy is 1 mA. The safety monitoring module has a self-check function, performs a comprehensive self-check every time it starts up, and performs a regular self-check every hour during operation. The monitoring data is stored in a real-time database with a storage period of 3 months.

[0096] The energy storage module uses a lithium iron phosphate battery pack with a rated capacity of 30 kWh, a nominal voltage of 650V, a working voltage range of 600 to 800V, and a maximum charge and discharge power of 60 kW. The battery management system uses a hierarchical management structure to achieve battery state monitoring and protection functions. The monitoring parameters include battery voltage, current, temperature, internal resistance, etc. The equalization charging uses an active equalization method with an equalization current of 2 amperes and an equalization accuracy of 10 millivolts. The cycle life of the battery pack is greater than 3000 times, the calendar life is greater than 8 years, and the working temperature range is -20 to 60 degrees Celsius. The energy storage module has a battery state evaluation function, uses a state estimation algorithm based on Kalman filtering, and can accurately evaluate the remaining capacity and health status of the battery.

[0097] The metering module uses a 0.5S-class electric energy metering chip with a measurement accuracy better than 0.5%, a sampling frequency of 1 time per second, and can achieve the metering of electric energy parameters such as forward active electric energy, reverse active electric 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 combined error of power calculation is less than 0.5%. The metering data is recorded every 15 minutes, and the data storage uses a ferroelectric memory with power-off protection, and the storage capacity is 5 years of metering data. The metering module has a real-time billing function, supports tiered electricity prices and peak-valley electricity prices, and the billing accuracy is 0.01 yuan.

[0098] The power management module adopts a multi-channel DC conversion circuit. The input voltage range is 180 to 264V, and the output voltages include multiple DC power supplies such as 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 coefficient is less than 50mV, the load regulation rate is less than 0.5%, and the power conversion efficiency is greater than 95%. The power management module has protection functions such as overvoltage, overcurrent, and short circuit. The overvoltage protection point is set to 120% of the rated output voltage, the overcurrent protection point is set to 110% of the rated output current, and the short circuit protection adopts a self-recovery method. The power startup sequence control uses a sequence controller to ensure that each power supply starts and shuts down in the correct order, and the startup sequence interval is 100 milliseconds.

[0099] The specific implementation manners of the steps to be executed by the system control module are described in detail as follows.

[0100] The specific implementation manner of step S01 is to collect double-gun charging data through the data acquisition module at a sampling frequency of 100 times per second. First, digital filtering is performed on the collected data. A 4th-order Butterworth low-pass filter with a cut-off frequency of 1kHz is used, and the filtered data will be used as the basic data for subsequent processing. Then, the charging interface impedance value is calculated. The calculation formula for the charging interface impedance value is: , where the parameters , , are the dynamic impedance coefficient, the initial impedance correction coefficient, and the time decay coefficient respectively, and their value ranges are 0.1 to 0.3, 0.05 to 0.15, and 0.01 respectively. Then, a charging state matrix is constructed. The expression of the charging state matrix is: , where takes the value of 100, takes the value of 2, and the elements in the matrix are normalized. The normalization uses the maximum-minimum method. Finally, the historical power fluctuation value is calculated. The calculation formula for the historical power fluctuation value is: , where takes the value of 100, takes the value of 0.2. The main purpose of this step is to obtain the real-time operation state data of the charging system.

[0101] The specific implementation manner of step S02 is to first perform singular value decomposition on the charging state matrix. The decomposition formula is: , and the QR decomposition algorithm is used for the decomposition. The number of iterations is set to 100 times, and the convergence threshold is 0.001. Then, the main eigenvectors are extracted. The eigenvalue threshold is set to 0.1, and only the eigenvectors corresponding to the eigenvalues greater than the threshold are retained. Then, a power feature space is constructed based on the main eigenvectors. The space construction formula is: , where takes the value of 3, The value is 0.1, and the ReLU function is used as the non - linear mapping function. Finally, the dual - gun power coordination degree is calculated and the power distribution coefficient is generated. The formula for calculating the power coordination degree is: , where The value is 0.2, The value is 0.01. The main purpose of this step is to extract the key features of the charging state and achieve the optimal distribution of the charging power.

[0102] The specific implementation of step S03 is to first use the safety monitoring module to collect temperature - related data at a sampling frequency of 10 times per second. The collected data includes the charging temperature value, the ambient temperature value, and the radiator temperature value. The collected data is processed by median filtering, and the length of the filtering window is 5 sampling points. Then, a temperature feature matrix is constructed. The expression of the temperature feature matrix is: , where The value is 5. Finally, the dual - gun temperature equilibrium coefficient is calculated. The formula for calculating the temperature equilibrium coefficient is: , where The value is 0.1, The value is 0.3. The main purpose of this step is to achieve temperature monitoring and equilibrium control of the charging system.

[0103] The specific implementation of step S04 is to first obtain the energy storage capacity coefficient. The formula for calculating the energy storage capacity coefficient is: , where The value is 0.1. Then, based on the power distribution coefficient, the temperature equilibrium coefficient, and the energy storage capacity coefficient, the power compensation coefficient is calculated. The formula for calculating the power compensation coefficient is: , where , , The values are 0.5, 0.3, and 0.2 respectively, The value is 0.1, The value is 0.001. The main purpose of this step is to comprehensively consider factors such as power distribution, temperature equilibrium, and energy storage capacity, and calculate reasonable power compensation parameters.

[0104] The specific implementation of step S05 is to correct the power compensation coefficient using the charging optimization equation set. First, the power equilibrium equation is used for optimization calculation. The expression of the power equilibrium equation is: , where , , The values are 0.3, 0.2, and 0.1 respectively. The gradient - descent method is used for solving, the iteration step size is 0.01, the maximum number of iterations is 100 times, and the convergence threshold is 0.001. Then, the temperature constraint equation is used for temperature constraint. The expression of the temperature constraint equation is: , where , , take values of 0.4, 0.3, and 0.3 respectively, and the temperature limit is 90 degrees Celsius, is 75 degrees Celsius. Finally, voltage stability control is performed using the voltage correction equation, and the expression of the voltage correction equation is: , where in the formula , , take values of 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.

[0105] The specific implementation of step S06 is to first convert the power compensation coefficient into a power regulation command. The conversion uses a look-up table method with a look-up accuracy of 0.01, and the look-up data is obtained through offline optimization. Then, the target values of the charging voltage and charging current are calculated according to the power regulation command. The calculation uses a proportional-integral control algorithm with a proportional coefficient of 0.8 and an integral time constant of 0.1 second, and the control period is 50 microseconds. Next, a control signal is output through a pulse width modulation controller. The frequency of the control signal is 20 kHz, the dead time is 1 microsecond, and the minimum duty cycle is 0.05. Finally, power regulation is performed. The power regulation uses a soft start strategy, and the power change rate is limited within 20% per second. At the same time, the voltage of the charging interface is monitored. When the voltage fluctuation exceeds 5%, voltage feed-forward control is started with a feed-forward coefficient of 0.5. The main purpose of this step is to execute the power balance control strategy.

[0106] The specific implementation of step S07 is to first process the monitored data using the Kalman filter algorithm. The state equation uses a second-order model, the standard deviation of the observation noise is 0.01, the standard deviation of the process noise is 0.001, and the filtering period is 10 milliseconds. Then, it is judged whether the monitored parameters exceed the preset thresholds. The voltage fluctuation threshold is 3% of the rated value, the current fluctuation threshold is 5% of the rated value, and the temperature fluctuation threshold is 5 degrees Celsius. When the parameters exceed the thresholds, the charging optimization control process is triggered to be executed again. When re-executing, a warm start strategy is used, and the previous optimization result is used as the initial value. At the same time, the system operation data is recorded, and the data storage uses a circular buffer structure with a buffer size of 1 hour's worth of data. The main purpose of this step is to ensure the stability and reliability of the charging process.

[0107] The specific implementation of step S08 is to first determine the charging task completion status based on the metering data output by the metering module. The judgment conditions include: the charging power reaches the set value, the charging duration reaches the maximum limit, the user actively ends the charging, and the system detects a charging fault. Then, the charging end process is executed. First, the charging power is reduced, and a soft stop strategy is adopted, with the power reduction rate controlled within 10% per second. Next, the charging contactor is disconnected, and the charging circuit voltage is detected. The voltage detection duration is 1 second, and the detection interval is 10 milliseconds. Then, the charging gun unlocking program is executed, and the unlocking signal duration is 100 milliseconds. Finally, the charging record data is saved, and the charging settlement information is displayed. The data is saved using a ferroelectric memory with power-off protection, and the storage capacity is for 5 years of charging records. The main purpose of this step is to achieve a safe end to the charging process and cost settlement.

[0108] To better understand and implement the present invention, the following provides an embodiment 2 of a specific application scenario of the present invention: During the development of a new generation of charging piles by a certain R & D team, in response to problems such as uneven power distribution and unstable temperature control existing in traditional dual-gun charging piles, a dual-gun charging control scheme based on feature decomposition and multi-objective optimization was developed. This scheme was first tested in a laboratory environment and then demonstrated in an actual charging station. During the laboratory test phase, the R & D team used a prototype with a rated power of 240 kW for testing. The test environment temperature was 25 degrees Celsius, and the relative humidity was 65%. During the test, loads of 100 kW and 140 kW were respectively used to simulate the charging requirements of two different vehicle models, and the data acquisition period was 10 minutes. The key parameters during the charging process are shown in Table 1: Table 1 Key parameter table for dual-gun charging process

[0109] Based on the collected data, the system calculates the impedance value of the charging interface. The calculation formula is: , where the dynamic impedance coefficient takes a value of 0.2, the initial impedance correction coefficient takes a value of 0.1, and the time decay coefficient takes a value of 0.01. The calculation results show that the impedance values of the two charging guns fluctuate within a reasonable range, effectively ensuring charging safety.

[0110] The statistical table of the state parameters during the system operation is shown in Table 2: Table 2 Statistical table of system operation state parameters

[0111] Figure 2(System Operating Status Parameter Distribution Diagram): It uses grouped bar charts to show the minimum, average, and maximum value distributions of four key status parameters (power coordination degree, temperature balance coefficient, energy storage capacity coefficient, power compensation coefficient). A charging status matrix is constructed based on the collected data. Through eigenvalue decomposition, the main eigenvectors are extracted, and the eigenvalue distribution is shown in Table 3 as follows: Table 3 Eigenvalue Distribution Table of Charging Status Matrix

[0112] Figure 3 (Eigenvalue Distribution and Contribution Rate Analysis Diagram): It shows the eigenvalue size distribution and cumulative contribution rate of the charging status matrix. Bar charts are used to represent the eigenvalue sizes, and line charts are used to show the cumulative contribution rates, with a dual-axis design. The distribution of 5 eigenvalues is shown in the figure, clearly showing the contribution of the main eigenvalues. The eigenvectors corresponding to the first 3 eigenvalues are selected to construct the power feature space. The construction formula of the power feature space is: , where the gradient weight coefficient takes the value of 0.1. Based on the constructed power feature space, the dual-gun power coordination degree is calculated. The calculation formula is: , where the power balance coefficient takes the value of 0.2, and the power difference attenuation coefficient takes the value of 0.01.

[0113] The temperature control effect during the charging process is shown in Table 4 as follows: Table 4 Comparison Table of Temperature Control Effects

[0114] Figure 4 (Temperature Change Trend Diagram): It shows the temperature change trends of the dual guns and the radiator during the charging process. Smooth curves are used to show the temperature change process over time, including scatter markers at the actual sampling points. The figure contains three curves: the temperature of charging gun 1, the temperature of charging gun 2, and the temperature of the radiator, clearly showing the temperature change trends and temperature differences. The system uses a charging optimization equation set to correct the power compensation coefficient. The change of the objective function value during the optimization process is shown in Table 5 as follows: Table 5 Change Table of Optimization Objective Function Value

[0115] Figure 5(Optimization of the objective function convergence process): It shows the variation trends of the three objective function values in the charging optimization equation set with the number of iterations. The convergence processes of the power balance objective value, temperature limit objective value, and voltage correction objective value are presented using smooth curves, including scatter markers for the actual iteration points. After 3 months of laboratory testing and 1 month of on-site demonstration operation, the charging pile system demonstrated excellent performance. Compared with the traditional dual-gun charging control scheme, the present invention has the following advantages: The traditional scheme mainly adopts a simple power distribution strategy, only considering the balance of charging power, without fully considering temperature balance and system stability, and uses fixed control parameters with poor adaptability. In contrast, the present invention extracts the main features of the charging state through eigenvalue decomposition, constructs a power feature space to achieve intelligent power distribution, and simultaneously uses a temperature feature matrix for temperature balance control, realizing the coordinated control of power, temperature, and voltage through the charging optimization equation set. Test data show that the power distribution balance of the present invention has increased by 25%, the temperature control accuracy has increased by 35%, the system operation stability has increased by 40%, and the charging efficiency has been improved by 15%, significantly enhancing the overall performance of the charging pile. In addition, the present invention adopts a hierarchical sampling frequency strategy, with a sampling frequency of 100 times per second for the data acquisition module, 10 times per second for the safety monitoring module, and 1 time per second for the metering module, reasonably allocating system resources and improving control accuracy and system reliability.

[0116] It should be noted that the detailed explanations of the variables involved in the present invention are shown in Table 6 below.

[0117] Table 6 Variable Explanation Table

[0118] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention.

Claims

1. An integrated DC charging pile with two guns in one machine, characterized in that, It 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 is built-in with a control chip. The main control module is electrically connected to each functional module. The control chip is provided with 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 eigenvector to achieve optimal power distribution for two charging guns, performs temperature balance control using the temperature feature matrix, and corrects the power compensation coefficient through a charging optimization equation set. The dual-channel power conversion module is used to convert alternating current into direct current and perform power regulation.

2. The integrated DC charging pile with dual guns in one machine according to claim 1, characterized in that, The system control module is used to perform the following steps: Use the data acquisition module to collect the charging voltage value, charging current value, charging temperature value, and input voltage fluctuation value of the two charging guns, calculate the charging interface impedance value based on the charging voltage value and the charging current value, construct a charging state matrix, and analyze the charging state matrix to calculate the historical power fluctuation value; Perform eigenvalue decomposition on the charging state matrix, extract the main eigenvector, construct a power feature space based on the main eigenvector, calculate the power coordination degree of the two charging guns, and generate a power distribution coefficient; Use the safety monitoring module to collect data on the charging temperature value, ambient temperature value, and radiator temperature value, construct a temperature feature matrix, and calculate the temperature balance coefficient of the two charging guns; Based on the power distribution coefficient and the temperature balance coefficient, combined with the energy storage capacity coefficient output by the energy storage module, calculate the power compensation coefficient of the two charging guns.

3. The integrated dual-gun DC charging pile according to claim 2, wherein, The system control module also performs the following steps: Use a charging optimization equation set to correct the power compensation coefficient. The charging optimization equation set includes a power balance equation, a temperature limit equation, and a voltage correction equation; According to the corrected power compensation coefficient, adjust the output power value of the two charging guns through the charging control module to achieve balanced control of two-gun charging; Real-time monitor the charging state of the two charging guns. When the fluctuations of the charging voltage value, charging current value, and charging temperature value exceed the preset threshold, re-execute the power optimization control; Based on the charging power value, charging cost value, and cumulative charging duration value output by the metering module, judge the completion status of the charging task and control the end of the charging process.

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

5. The integrated one-machine two-gun DC charging pile according to claim 4, wherein The impedance value of the charging interface is the ratio of the charging voltage value to the charging current value; the historical power fluctuation value is the amplitude of the sequential change of the charging power; the two-gun power coordination degree is the quantification value of the balance degree of the two-gun power distribution; the main eigenvector is the main eigencomponent obtained by the eigenvalue decomposition of the charging state matrix; the power feature space is the power distribution optimization space constructed based on the main eigenvector.

6. The integrated one-machine two-gun DC charging pile according to claim 5, wherein, The input parameters of the power balance equation include the power distribution coefficient, the power compensation coefficient, the energy storage capacity coefficient, the cumulative value of the charging duration, and the historical power fluctuation value, and the output parameter is the power adjustment coefficient.

7. The integrated one-machine two-gun DC charging pile according to claim 6, characterized in that, The input parameters of the temperature limit equation include the temperature balance 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.

8. The integrated DC charging pile with dual guns in one machine according to claim 7, characterized in that, The input parameters of the voltage correction equation include the charging voltage value, the power adjustment coefficient, the temperature correction coefficient, the input voltage fluctuation value, and the impedance value of the charging interface, and the output parameter is the voltage correction coefficient.

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

10. The integrated one-machine two-gun DC charging pile according to claim 1, characterized in that, The communication interface module is used to realize the data interaction with the background management system and the charging terminal. The data acquisition module is used to collect the 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 interaction module is used to display the charging state and charging parameters and receive user input. The safety monitoring module is used to monitor the connection state of the charging gun and system faults. The energy storage module is used to temporarily store electrical energy and provide a backup power supply. The metering module is used to measure the charging power and charging cost. The power management module is used to distribute the system power and perform electrical energy scheduling. Among them, 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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