AC-DC Charging Pile Bidirectional Power Flow Control Method, Device and Equipment

Abstract

The present invention relates to the technical field of bidirectional power flow control, and discloses a bidirectional power flow control method, device and equipment for an AC-DC charging pile. The method includes: detecting and collecting parameters of the AC-DC charging pile, power converter and grid connection point to obtain a set of operating parameters of the charging pile system; calculating the trapezoidal voltage edge of the switching process of the power converter, and superimposing the dead-time compensation amount on the PWM control signal of the power converter to obtain a target PWM control signal; calculating an optimal PWM control sequence, and using the first control quantity of the optimal PWM control sequence as the current control output; when it is detected that the power flow direction needs to be changed, controlling the DC bus voltage of the power converter to be stable at a set value and then switching to the target operating mode to obtain a new power output control quantity. The present invention enhances the robustness of the AC-DC charging pile, enables the AC-DC charging pile to adapt to the operating requirements under different working conditions, and reduces the failure rate of the AC-DC charging pile.

Description

Technical Field

[0001] The present invention relates to the technical field of bidirectional power flow control, and particularly to a method, device and equipment for bidirectional power flow control of AC-DC charging piles. Background Technique

[0002] As an important part of the electric vehicle charging infrastructure, the bidirectional power flow control technology of AC-DC charging piles is of great significance for improving charging efficiency and grid interaction ability. At present, in the process of bidirectional power flow of traditional charging piles, due to the influence of parasitic capacitance and dead time of switching tubes in the power converter, low-order harmonic distortion problems often occur, which not only reduces the power quality of the system, but may also cause electromagnetic interference and affect the stable operation of the charging pile.

[0003] With the development of vehicle-to-grid interaction technology for electric vehicles, charging piles need to have the ability of bidirectional power flow. However, during the power flow switching process, due to the dynamic response characteristics of voltage and current, voltage fluctuations and power oscillations are likely to occur. Traditional power control methods often adopt simple PI control, which cannot effectively cope with system parameter changes and external disturbances, resulting in poor dynamic performance of the system, especially in the scenario of high-power fast charging and discharging. Summary of the Invention

[0004] The present invention provides a method, device and equipment for bidirectional power flow control of AC-DC charging piles. The present invention enhances the robustness of AC-DC charging piles, enables AC-DC charging piles to adapt to the operating requirements under different working conditions, and reduces the failure rate of AC-DC charging piles.

[0005] In the first aspect, the present invention provides a method for bidirectional power flow control of an AC-DC charging pile, and the method for bidirectional power flow control of the AC-DC charging pile includes:

[0006] Detect and collect parameters of the AC-DC charging pile, power converter and grid connection point to obtain a set of operating parameters of the charging pile system;

[0007] Calculate the trapezoidal voltage edge of the switching process of the power converter according to the set of system operating parameters to obtain a dead time compensation amount, and superimpose the dead time compensation amount on the PWM control signal of the power converter to obtain a target PWM control signal;

[0008] Input the target PWM control signal and the current system state into a predictive controller for discrete mathematical modeling, calculate an optimal PWM control sequence, and use the first control quantity of the optimal PWM control sequence as the current control output;

[0009] Judge the working state of the AC-DC charging pile according to the current control output. When it is detected that the power flow direction needs to be changed, gradually reduce the power output of the current working mode to zero. After the DC bus voltage of the power converter is stabilized at the set value, switch to the target working mode to obtain a new power output control quantity.

[0010] In a second aspect, the present invention provides an AC-DC charging pile bidirectional power flow control device, and the AC-DC charging pile bidirectional power flow control device includes:

[0011] An acquisition module, configured to detect and collect parameters of the AC-DC charging pile, the power converter, and the grid connection point to obtain a charging pile system operation parameter set;

[0012] A calculation module, configured to perform trapezoidal voltage edge calculation on the switching process of the power converter according to the system operation parameter set to obtain a dead time compensation quantity, and superimpose the dead time compensation quantity on the PWM control signal of the power converter to obtain a target PWM control signal;

[0013] A switching module, configured to input the target PWM control signal and the current system state into a predictive controller for discrete mathematics modeling, calculate an optimal PWM control sequence, and use the first control quantity of the optimal PWM control sequence as the current control output;

[0014] An acquisition module, configured to judge the working state of the AC-DC charging pile according to the current control output. When it is detected that the power flow direction needs to be changed, gradually reduce the power output of the current working mode to zero. After the DC bus voltage of the power converter is stabilized at the set value, switch to the target working mode to obtain a new power output control quantity.

[0015] In a third aspect of the present invention, a computer device is provided, including: a memory and at least one processor, and instructions are stored in the memory; the at least one processor calls the instructions in the memory to enable the computer device to execute the above-mentioned AC-DC charging pile bidirectional power flow control method.

[0016] In a fourth aspect of the present invention, a computer-readable storage medium is provided, and instructions are stored in the computer-readable storage medium. When it runs on a computer, it enables the computer to execute the above-mentioned AC-DC charging pile bidirectional power flow control method.

[0017] In the technical solution provided by the present invention, by calculating the trapezoidal voltage edge of the switching process of the power converter and compensating the dead time, the low-order harmonics are effectively suppressed, and the power quality of the system is improved. By adopting a discrete mathematical modeling method based on predictive control and combining with a rolling optimization algorithm, the system has strong dynamic response ability and shortens the power adjustment time. A complete power flow switching control strategy is designed, and through segmented power reduction and voltage stability criterion, the smoothness of the charge and discharge mode switching process is ensured, and the DC bus voltage fluctuation is controlled within ±2%. An iterative prediction calculation method is introduced to dynamically update the dead time compensation amount, so that the compensation effect remains stable within the full working range, and the system efficiency is improved. Through state space modeling and performance index optimization, the robustness of the system is enhanced, enabling it to adapt to the operating requirements under different working conditions and reducing the system failure rate. By adopting an online iterative optimization algorithm, the dependence on current sampling is reduced, the influence of communication delay on the system performance is reduced, and the real-time performance and reliability of the control are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic diagram of the steps of the bidirectional power flow control method for AC-DC charging piles in the embodiments of the present invention;

[0020] Figure 2 It is a schematic diagram of the structure of the bidirectional power flow control device for AC-DC charging piles in the embodiments of the present invention;

[0021] Figure 3 It is a schematic block diagram of the structure of a computer device in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] An embodiment of the present invention provides a method, device, and equipment for controlling bidirectional power flow of an AC / DC charging pile. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims, and above-mentioned drawings of the present invention are used to distinguish similar objects and do not necessarily need to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order different from that illustrated or described here. In addition, the term "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or equipment that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or equipment.

[0023] For ease of understanding, the specific process of the embodiment of the present invention will be described below. Please refer to Figure 1 , an embodiment of the method for controlling bidirectional power flow of an AC / DC charging pile in the embodiment of the present invention includes:

[0024] Step S1: Detect and collect parameters of the AC / DC charging pile, power converter, and grid connection point to obtain a set of operating parameters of the charging pile system;

[0025] It can be understood that the execution subject of the present invention can be a device for controlling bidirectional power flow of an AC / DC charging pile, or a terminal or a server. Specifically, it is not limited here. The embodiment of the present invention is described by taking the server as the execution subject as an example.

[0026] Specifically, voltage sensor sampling points are respectively set at the input end and the output end of the AC-DC charging pile to capture the changes in voltage signals. After digital conversion of these voltage signals, the voltage value at the input end and the voltage value at the output end are obtained. The voltage values at the input end and the output end are processed to extract the power frequency components, and the real-time power data of the AC-DC charging pile are generated through the reactive power calculation method. In this way, the power changes of the charging pile under different operating states are dynamically monitored, providing real-time information support for power flow control. The parasitic capacitance test is respectively carried out on each switching tube of the power converter. The rise time and fall time of the voltage across each switching tube are measured at different switching frequencies to obtain the parasitic capacitance parameters of the switching tube. The parasitic capacitance is an important factor affecting the performance of the power converter, and the accuracy of its parameters is directly related to the reliability of the control signal. In this process, based on the analysis of the voltage waveform across the switching tube, the dead time is measured online and the time interval between the rising edge and the falling edge of the voltage waveform is calculated to obtain the accurate dead time parameters. The timing analysis of the control signal of the power converter and the conduction state of the switching tube is carried out, and the time difference between the effective edge of the control signal and the actual conduction of the switching tube is calculated to obtain the switching delay time parameters. The switching delay time reflects the dynamic response characteristics between the control signal and the actual physical action, and its optimization can significantly improve the dynamic performance of the power converter. At the same time, by setting voltage and current sensors at the grid connection point, the three-phase voltage and current of the grid are sampled, and the frequency domain analysis of the sampled signals is carried out by using the discrete Fourier transform to obtain the effective value and frequency value of the grid voltage, reflecting the stability of the grid operation. The voltage value at the input end, the voltage value at the output end, the real-time power data, the parasitic capacitance parameters of the switching tube, the dead time parameters, the switching delay time parameters, the effective value of the grid voltage and the frequency value are combined to form a data matrix, obtaining the operating parameter set of the charging pile system.

[0027] Step S2: Calculate the trapezoidal voltage edge of the switching process of the power converter according to the operating parameter set of the system to obtain the dead time compensation amount, and superimpose the dead time compensation amount on the PWM control signal of the power converter to obtain the target PWM control signal;

[0028] Specifically, a mathematical model of the switching voltage of the power converter is established based on the parasitic capacitance parameters of the switching tube in the system operating parameters. By establishing the voltage-time function of the switching voltage, the voltage change trend of the switching tube in different states is described, providing an accurate voltage change model for subsequent analysis. The trapezoidal edge characteristics of the voltage-time function of the switching process are analyzed to study the dynamic characteristics of the voltage change, and the voltage change rate curve is generated therefrom. The voltage change rate curve can accurately reflect the voltage waveform characteristics in the switching process and reveal the voltage change rate in different stages. Based on the voltage change rate curve, the conduction process of the switching tube is segmented, where the time parameter t1 is defined as the dead time and t2 is defined as the switching delay time, thus dividing the switching voltage into three stages. The first stage is represented by the segmented voltage function v1(t), which describes the conduction voltage before switching; the second stage is described by v2(t), representing the voltage change during the dead time; the third stage is represented by v3(t), which describes the conduction voltage change after switching. By comprehensively analyzing the voltage functions of these three stages, a mathematical expression for the total voltage distortion is established. This expression is formulated as Vd(t)=v1(t)+v2(t)+v3(t), where Vd(t) is the total voltage distortion and t is the time variable. This mathematical model describes the distortion of the voltage waveform throughout the switching process. The Fourier series expansion of the voltage distortion mathematical model is performed to decompose the total voltage distortion into a series of harmonic components, obtaining the amplitude and frequency information of each harmonic. These harmonic components reflect the impact of voltage distortion on the system performance, especially the interference of high-order harmonics on the power grid and load. Based on this harmonic component, an optimization objective function for dead time compensation is constructed, and the core of the objective function is to minimize the harmonic distortion. Under this optimization framework, the optimal dead time compensation amount is accurately solved by means of iterative calculation. The iterative calculation process comprehensively considers various factors, including the parasitic parameters of the switching tube, the distortion characteristics of the voltage waveform, and the dynamic constraint conditions of the system, thereby ensuring the accuracy and effectiveness of the obtained compensation amount. The PWM control signal is corrected based on the optimal dead time compensation amount. The compensation amount is superimposed on the original PWM control signal to eliminate the voltage distortion caused by the dead time effect. In this way, the compensated control signal can more accurately reflect the ideal switching action and improve the dynamic performance of the power converter. After the correction is completed, the compensated PWM control signal is input into the PWM modulator and compared with the triangular carrier wave to generate the final target PWM control signal.

[0029] Perform a periodic division on the voltage distortion mathematical model to determine the time interval for Fourier analysis. Based on the delimited Fourier analysis interval, extract the fundamental wave component of the voltage distortion function, thereby separating the contribution of the fundamental wave to the overall system performance and obtaining an accurate expression for the fundamental wave component. Calculate the higher-order harmonics of the voltage distortion function to obtain specific information about each harmonic component, including the amplitude and phase of each harmonic. The combination of the fundamental wave component and the higher-order harmonic components can establish a complete Fourier series expression, transforming the voltage distortion function from the time domain to the frequency domain representation. The frequency domain representation of voltage distortion has a more intuitive form, enabling the harmonic characteristics to be accurately described in the frequency domain. Especially during the bidirectional power flow control process, this frequency domain information helps to identify and eliminate unnecessary higher-order harmonic distortions. Based on the obtained frequency domain representation of voltage distortion, construct an optimization objective function for dead-time compensation. The form of the objective function is , where is a weight coefficient related to the harmonic order, and as the harmonic order increases, the weight coefficient gradually increases. This design enables the objective function to suppress the distortion generated by higher-order harmonics to a greater extent during the optimization process. Meanwhile, represents the square of the amplitude of the -th harmonic, which is a key parameter for measuring the intensity of harmonic distortion. By constructing the objective function, the direction of compensation optimization is clearly defined, that is, to minimize the harmonic distortion by minimizing the value of the objective function. To optimize the objective function, calculate its gradient to obtain the gradient information of the objective function. The gradient calculation reveals the change trend of the objective function at the current point, providing guidance for subsequent iterations. Substitute the gradient of the objective function into the iterative formula for updating the compensation amount to generate an iterative sequence of compensation time. The generation of the iterative sequence is a dynamic optimization process, and its core lies in gradually adjusting the dead-time compensation amount to make the value of the objective function gradually approach the optimal value. In each iteration, use the compensation amount and gradient information obtained from the previous calculation to update the current compensation value to ensure that the optimization process converges to the global optimal solution. As the iteration progresses, perform a convergence judgment on the iterative sequence of compensation time to confirm whether the optimization goal has been achieved. When the change amplitude of the iterative sequence is less than the set threshold, it is determined that the iterative process has converged, and an accurate dead-time compensation amount is obtained. This compensation amount can effectively reduce voltage distortion and significantly reduce the interference of higher-order harmonics on the system operation by modifying the original PWM control signal.

[0030] Step S3: Input the target PWM control signal and the current system state into the predictive controller for discrete mathematical modeling, calculate the optimal PWM control sequence, and use the first control quantity of the optimal PWM control sequence as the current control output;

[0031] Specifically, the sampling period of the target PWM control signal is divided, and the continuous signal is converted into a discrete sampling sequence through discretization. Based on the discrete sampling sequence and the current system state, a state-space model of the charging pile system is established, and state equations and output equations that can accurately describe the dynamic behavior of the system are constructed. By solving these equations, a discrete state-space model is obtained. The discrete state-space model describes the dynamic characteristics of the system in matrix form, including the relationships among system state variables, input signals, and output signals. According to the discrete state-space model, the prediction horizon N and the control horizon M are set to determine the prediction and optimization ranges of the system. The prediction horizon defines the prediction span of the future state of the system, while the control horizon specifies the time range that the controller can optimize. Based on these settings, the states of the charging pile system in the next A sampling periods are recursively predicted to generate a state prediction sequence. The state prediction sequence is obtained through iterative calculation and can accurately reflect the dynamic changes of the system at different sampling periods. A reference trajectory is planned for the state prediction sequence. Target values are set for the key indicators of the system (such as voltage, current, and power), and the control performance is evaluated by calculating the deviations between the predicted values and these target values. A performance index function is provided to quantify this deviation, and its form comprehensively considers the weights of multiple objectives to ensure that the optimization result can balance the stability and accuracy of voltage, current, and power. After substituting the constructed performance index function into the rolling optimization solver, the control strategy is optimized online. The upper and lower limits of voltage and current are set according to the physical characteristics and operating constraints of the system to ensure that the optimization result meets the actual operating conditions. In rolling optimization, through recursive iterative calculation, the optimal PWM control sequence within the control horizon M is obtained. The advantage of rolling optimization is that it only optimizes the local range each time and adjusts the optimization direction in real time according to the latest system state, realizing the dynamic tracking and optimization of the nonlinear system. The first control quantity of the optimal PWM control sequence is subjected to dead zone compensation and PWM modulation to correct the non-ideal characteristics in the control signal and ensure that the signal can adapt to the actual requirements of the power converter. The corrected control signal is used to drive the system and achieve the control of the current sampling period to obtain the current control output. Based on the current control output, the system state is predicted and updated in one step. The result of the prediction update is used as the initial state of the next control cycle to form a state update value. After inputting the state update value into the predictive controller, the entire optimization calculation process is repeated. Through this rolling horizon calculation method, the control strategy is updated in real time at each sampling period, and the PWM control signal is continuously adjusted to adapt to the dynamic changes of the system, achieving the optimal control output.

[0032] To enable the performance index function to adapt to the solution requirements of the quadratic programming problem, a standard quadratic form transformation is performed on it. By constructing a matrix and a vector , the optimization problem is formulated as a standard quadratic programming problem, where is the coefficient matrix, represents the transpose of the matrix, is the prediction matrix, is the state weighting matrix, is the control weighting matrix, is the optimization vector, is the state prediction matrix, is the optimization variable, is the current sampling time, is the reference trajectory vector. According to the form of the quadratic programming problem, the constraint condition matrix is set to ensure that the optimization result meets the physical limitations and operating requirements of the system. By constructing the inequality constraint and the equality constraint , where and Leq are the inequality and equality constraint matrices respectively, and beq are the corresponding constraint vectors, which completely define the constraint range of the optimization problem. The constraint conditions effectively limit the change range of the control quantity and state variables, avoiding the generation of control strategies that exceed the system's capabilities, thereby ensuring the feasibility and physical rationality of the optimization result. When setting the constraint conditions for the coefficient matrix Perform positive definite analysis to verify the solvability of the optimization problem and determine the solution conditions. The results of the positive definite analysis can be used to construct the feasible region of the optimization problem. This process embeds the boundaries of the feasible region into the problem description by defining the upper and lower bounds of the control variables and other physical constraints, forming the constraint conditions for the control variables. Substitute the control variable constraint conditions into the state recurrence equation for forward calculation to generate state variable constraints. The introduction of state variable constraints ensures that the changes in the system state are always constrained by the dynamic model, guaranteeing that the solution of the optimization problem conforms to the dynamic behavior characteristics of the system. Initialize the solution of the optimization problem based on the state variable constraints. By selecting the initial iteration point and setting the convergence threshold, necessary initial conditions are provided for the subsequent iterative solution. The choice of initial conditions directly affects the convergence speed of the solver and the accuracy of the final result. When initializing, the complexity of the optimization problem and physical constraints need to be comprehensively considered to ensure the stability and efficiency of the iterative process. Based on the initial conditions, input the optimization problem into the interior point method solver for iterative calculation. In the interior point method, the barrier function method is used to handle the constraint conditions. By transforming the constraint conditions into equivalent objective function terms, the complexity of directly solving the constrained optimization problem is avoided. The iterative calculation process updates the optimization variables to gradually approximate the optimal solution of the objective function while strictly satisfying the constraint conditions. In this process, a control sequence within M control periods is gradually generated through recursive calculation. Verify the optimality of the control sequence. Evaluate the optimality of the optimization result by calculating the satisfaction degree of the KKT (Karush-Kuhn-Tucker) conditions. The KKT conditions are the necessary conditions for quadratic programming problems, and the verification results directly reflect whether the optimization solution has reached the global optimal state. Through verification, ensure that the finally output control sequence has high accuracy and reliability. After the above steps, an optimal PWM control sequence is generated.

[0033] Step S4: Judge the working state of the AC-DC charging pile according to the current control output. When it is detected that the power flow direction needs to be changed, gradually reduce the power output of the current working mode to zero. After the DC bus voltage of the power converter is stabilized at the set value, switch to the target working mode to obtain a new power output control variable.

[0034] Specifically, judge the power flow direction state in the current control output. This judgment depends on the calculation formulas of the sending power and the receiving power, where the sending power and the receiving power are obtained by measuring the voltage and current at the sending end and the voltage and current at the receiving end respectively. When When the power flow direction is determined to be forward by the system, otherwise it is reverse. The determination result of the power flow direction directly determines whether mode switching needs to be executed. After it is clear that the power flow direction needs to be changed, the power reduction control stage is entered. By reducing the power of the current working mode according to the determination result of the power flow direction, a power reduction process curve is generated. The shape of the power reduction process curve is determined by the system dynamic performance and the target switching time, and its purpose is to avoid unstable phenomena or voltage fluctuations caused by sudden power changes during the switching process by gradually reducing the power output. To achieve smooth power reduction control, the step size of power reduction and the time interval for each reduction are set based on the power reduction process curve, and a segmented power reduction control sequence is generated. The design of the segmented power reduction control sequence is through discretization processing, which decomposes the continuous power reduction process into a process of multiple small step size adjustments, thereby improving the control accuracy and response speed. The segmented power reduction control sequence is modulated by the PWM duty cycle and converted into an actual PWM control quantity sequence. The PWM modulation process calculates the corresponding duty cycle according to the control target of each level in the segmented power sequence, and inputs the obtained PWM control quantity sequence into the power converter so that the converter can perform segmented power reduction operations. During this process, to ensure system stability, the fluctuation amount of the DC bus voltage is monitored in real time, and the voltage stability criterion is calculated. The voltage stability criterion is used to determine whether the bus voltage has returned to the set stable value. When the voltage fluctuation amount is less than the preset threshold, the system is considered to enter the stable state, and at this time, the mode switching operation is triggered. The moment of mode switching is determined by the voltage stability criterion, and a mode switching trigger signal is generated to send a switching instruction to the control system. Based on the mode switching trigger signal, the control parameter matrix is automatically updated to adapt to the control requirements of the target working mode. After the control parameters are updated, the system enters the new target working mode, and the power output control of the control parameters of the target mode is executed. Through this process, the power output in the target working mode is achieved, and the power output control quantity is generated according to the new mode requirements to ensure the smooth operation of the system in the new working mode.

[0035] In the embodiments of the present invention, by calculating the trapezoidal voltage edge of the switching process of the power converter and compensating the dead time, the low-order harmonics are effectively suppressed, and the power quality of the system is improved. By adopting the discrete mathematical modeling method based on predictive control and combining the rolling optimization algorithm, the system has strong dynamic response ability and shortens the power regulation time. A complete power flow switching control strategy is designed. Through segmented power reduction and voltage stability criterion, the smoothness of the charging and discharging mode switching process is ensured, and the DC bus voltage fluctuation is controlled within ±2%. The iterative prediction calculation method is introduced to dynamically update the dead time compensation amount, so that the compensation effect remains stable in the whole working range and the system efficiency is improved. Through state space modeling and performance index optimization, the robustness of the system is enhanced, enabling it to adapt to the operation requirements under different working conditions and reducing the system failure rate. By adopting the online iterative optimization algorithm, the dependence on current sampling is reduced, the influence of communication delay on the system performance is reduced, and the real-time performance and reliability of the control are improved.

[0036] In a specific embodiment, the process of executing step S1 may specifically include the following steps:

[0037] Voltage sensor sampling points are respectively set at the input end and the output end of the AC-DC charging pile, and the voltage signal is digitally converted to obtain the input end voltage value and the output end voltage value;

[0038] The power frequency components of the input end voltage value and the output end voltage value are extracted and the reactive power is calculated to obtain the real-time power data of the AC-DC charging pile;

[0039] The parasitic capacitance of each switching tube of the power converter is respectively tested, and the voltage rise time and fall time at both ends of the switching tube are measured at different switching frequencies to obtain the parasitic capacitance parameters of the switching tube;

[0040] The dead time is measured online based on the voltage waveform at both ends of the switching tube, and the time interval between the rising edge and the falling edge of the voltage waveform is calculated to obtain the dead time parameter;

[0041] The timing analysis of the control signal and the conduction state of the switching tube of the power converter is carried out, and the time difference from the effective edge of the control signal to the actual conduction of the switching tube is calculated to obtain the switching delay time parameter;

[0042] Through the voltage and current sensors set at the grid connection point, the three-phase voltage and current of the grid are sampled and discrete Fourier transform calculation is carried out to obtain the effective value and frequency value of the grid voltage;

[0043] The input end voltage value, the output end voltage value, the real-time power data, the parasitic capacitance parameters of the switching tube, the dead time parameter, the switching delay time parameter, the effective value and frequency value of the grid voltage are combined into a data matrix to obtain the operation parameter set of the charging pile system.

[0044] Specifically, voltage sensor sampling points are set at the input and output ends of the AC-DC charging pile respectively. By sampling the voltage signals at the input and output ends, the instantaneous changes of the input and output voltages are captured. The sampled analog signals are converted into digital quantities through an analog-to-digital converter to obtain the input terminal voltage value and the output terminal voltage value . Assuming the sampling frequency is , and the voltage signal is , then the discrete voltage value corresponding to the sampling point is , where is the sampling period. After obtaining the input and output voltage values, the power frequency components are extracted from these voltage values to analyze their frequency characteristics and amplitude characteristics. Assuming the instantaneous value of the input terminal voltage is , where is the amplitude of the input terminal voltage is the angular frequency, is the phase angle. The fast Fourier transform is used to extract the power frequency component and calculate its amplitude and phase. At the same time, according to the voltage and current signals, the real-time power data, including active power and reactive power, is calculated. The calculation formula for active power is , where and are the effective values of voltage and current respectively, is the phase difference; the formula for reactive power is . The real-time power data of the charging pile system is obtained through calculation. The parasitic capacitance of the switching tube of the power converter is tested. This process involves measuring the rise time and fall time of the voltage across the switching tube at different switching frequencies to obtain the parasitic capacitance parameters of the switching tube. Assuming the equivalent capacitance of the switching tube is , and its voltage change rate is d d , according to the capacitance current formula d d , and d d are measured to calculate . This parasitic capacitance has an important impact on the dynamic performance of the switching tube and can determine the turn-on and turn-off characteristics of the switch. After the parasitic capacitance test is completed, the voltage waveform of the switching tube is analyzed, and the dead time is measured online to optimize the switching performance. The dead time refers to the time interval during which the upper and lower bridge arm switching devices are simultaneously turned off during the switching process to avoid the shoot-through phenomenon. By analyzing the voltage rise edge and fall edge of the switching tube, the dead time parameter is calculated, and the specific formula is , where and They are the rise time and the fall time respectively. Perform a timing analysis on the control signal of the power converter and the conduction state of the switching transistor. There is a time difference between the effective edge of the control signal and the actual conduction of the switching transistor, and this time difference is defined as the switching delay time. . By measuring the moment when the control signal changes and the moment when the voltage of the switching transistor changes , calculate . This parameter has an important impact on the fast response ability of the system. At the same time, by setting voltage and current sensors at the grid connection point, sample the three-phase voltage and current of the grid. Use the discrete Fourier transform to perform frequency-domain analysis on the sampled signal to obtain the effective value and frequency value of the grid voltage. The formula for the effective value of the three-phase voltage is , where is the period; the frequency value is obtained through zero-crossing detection or phase difference calculation. Combine the input terminal voltage value , the output terminal voltage value , the real-time power data , the parasitic capacitance parameter of the switching transistor , the dead time parameter , the switching delay time parameter , as well as the effective value and the frequency value of the grid voltage to form a comprehensive data matrix , that is:

[0045] ;

[0046] This data matrix comprehensively reflects the operating state of the AC-DC charging pile.

[0047] In a specific embodiment, the process of executing step S2 may specifically include the following steps:

[0048] Model the switching voltage of the power converter according to the parasitic capacitance parameter of the switching transistor in the system operation parameter set, obtain the voltage-time function of the switching process, and perform trapezoidal edge characteristic analysis on the voltage-time function of the switching process to obtain the voltage change rate curve;

[0049] Based on the voltage change rate curve, perform segmented processing on the conduction process of the switching transistor. Set t1 as the dead time parameter and t2 as the switching delay time parameter, and obtain the segmented voltage function v1(t) as the pre-switching conduction voltage, v2(t) as the voltage during the dead time, and v3(t) as the post-switching conduction voltage;

[0050] Substitute the segmented voltage function into the voltage distortion calculation formula Vd(t)=v1(t)+v2(t)+v3(t), where Vd(t) is the total voltage distortion and t is the time variable, to obtain the voltage distortion mathematical model;

[0051] The Fourier series expansion is performed on the voltage distortion mathematical model to obtain harmonic components, and an optimization objective function for dead-time compensation is constructed based on the harmonic components. The dead-time compensation amount is obtained through iterative calculation;

[0052] The PWM control signal is corrected based on the dead-time compensation amount to obtain the compensated control signal, and the compensated control signal is input into the PWM modulator and compared with the triangular carrier wave to obtain the target PWM control signal.

[0053] Specifically, the switching voltage of the power converter is modeled according to the parasitic capacitance parameters of the switching tubes in the system operation parameters set. The parasitic capacitance parameters directly affect the dynamic behavior of the switching tubes. Assume that the parasitic capacitance of the switching tube is , the switching current is , and the voltage across the switching tube follows the capacitance current formula during the switching process. By measuring and fitting the voltage waveform, a voltage-time function of the switching process is constructed , and its specific form depends on different states during the switching process, such as the rising stage, steady stage, and falling stage of the voltage. After the modeling is completed, the trapezoidal edge characteristics analysis is performed on the voltage-time function to analyze the change rates of the voltage rising edge and falling edge. The voltage change rate curve is represented by the derivative , which can reflect the speed and non-linear characteristics of the voltage change. For example, in the case of a large parasitic capacitance, the change rate of the rising edge is low, while the falling edge shows a steep change. By analyzing the voltage change rate curve, key time points during the switching process are identified, such as the dead time and the switching delay time . Based on the voltage change rate curve, the switching process is segmented into three stages: the pre-switching conduction stage, the dead-time stage, and the post-switching conduction stage. Set as the dead time, as the switching delay time, and define the segmented voltage function as: : Describes the voltage change during the pre-switching conduction stage, and its characteristics are usually stable or slowly changing, approximately , where is the conduction voltage, is the time constant; : Describes the voltage change during the dead time, usually showing a linear change or a short transition, expressed as , where and are the linear fitting parameters; : Describes the voltage change during the post-switching conduction stage, approximately an exponential decay function , where is the voltage after switching. By substituting the piecewise voltage function into the total voltage distortion formula , a mathematical model of voltage distortion is constructed. This model reflects the comprehensive characteristics of voltage throughout the switching cycle, and its distortion degree directly affects the harmonic components and the operating efficiency of the system. To analyze the spectral characteristics of voltage distortion, the mathematical model is expanded by Fourier series. The Fourier series represents the voltage distortion function as a superposition of a series of harmonic components:

[0054] ;

[0055] where is the DC component, and are the cosine and sine coefficients of the th harmonic respectively, is the fundamental angular velocity, is the harmonic order. The calculation formulas for the coefficients are:

[0056] ;

[0057] where is the period. Based on the harmonic components obtained from the Fourier series expansion, an optimization objective function for dead-time compensation is constructed. The objective function aims to minimize the sum of the squares of the harmonic components, for example:

[0058] ;

[0059] where is the weight coefficient related to the harmonic order. By minimizing the objective function , the amplitudes of the high-order harmonics are reduced, thereby reducing the voltage distortion. The optimization process is achieved through iterative calculations, adjusting the dead-time compensation amounts and until the objective function converges to the minimum value. The final compensation amount is used to correct the PWM control signal. By adding the compensation amount to the original PWM signal, the corrected control signal can cancel the distortion caused by the dead time and switching delay. The corrected control signal is input to the PWM modulator and compared with the triangular carrier signal to generate the target PWM control signal.

[0060] In a specific embodiment, the process of performing the steps to expand the voltage distortion mathematical model by Fourier series to obtain harmonic components and constructing an optimization objective function for dead-time compensation based on the harmonic components and obtaining the dead-time compensation amount through iterative calculations may specifically include the following steps:

[0061] Perform a periodic division on the voltage distortion mathematical model to obtain the Fourier analysis interval, and extract the fundamental component from the voltage distortion function according to the Fourier analysis interval to obtain the fundamental component;

[0062] Perform high-order harmonic calculation on the voltage distortion function to obtain each harmonic component, and establish a Fourier series expression based on the fundamental component and each harmonic component to obtain the frequency-domain representation of the voltage distortion;

[0063] Construct a dead-time compensation optimization objective function J = Σkn×(an 2 +bn 2 ), where kn is the weight coefficient of the nth harmonic, and the weight coefficient of the nth harmonic increases with the increase of the harmonic order n. an 2 +bn 2 represents the square of the amplitude of the nth harmonic;

[0064] Perform gradient calculation on the dead-time compensation optimization objective function to obtain the objective function gradient, and substitute the objective function gradient into the iteration formula to update the compensation amount to obtain the iteration sequence of the compensation time;

[0065] Perform convergence judgment on the iteration sequence of the compensation time to obtain the dead-time compensation amount.

[0066] Specifically, perform a periodic division on the voltage distortion function to determine the Fourier analysis interval. Assume that the voltage distortion function describes the voltage distortion of the system within the period , where is the fundamental period, and is the fundamental frequency. The Fourier analysis interval is selected as , which can completely cover the voltage waveform of one period and is conducive to extracting the fundamental component and high-order harmonic components. By decomposing the periodic characteristics of into the superposition of the fundamental wave and harmonics, the frequency-domain representation of the voltage distortion function is expanded. The extraction of the fundamental component is based on the basic principle of Fourier transform. In the voltage distortion function , the fundamental component corresponds to the sine or cosine component with frequency , and its calculation formula is:

[0067] ;

[0068] where and are the cosine and sine coefficients of the fundamental wave respectively. The amplitude of the fundamental wave is expressed as:

[0069] ;

[0070] This indicates that the fundamental wave amplitude It is obtained by taking the square root of the sum of the squares of the sine and cosine components of the fundamental wave. The calculation of the higher harmonic components is similar to the extraction of the fundamental wave, but the frequency is an integer multiple of the fundamental wave frequency, that is , where is the harmonic order. The corresponding harmonic coefficients and are calculated by the following formulas:

[0071] ;

[0072] The amplitude of the higher harmonic is:

[0073] ;

[0074] By calculating successively, the amplitude and frequency information of all higher harmonics are obtained. After extracting the fundamental wave component and the higher harmonic components, the voltage distortion function is expressed in the form of a Fourier series:

[0075] ;

[0076] where is the DC component. The DC component of a symmetric waveform is zero, so it is usually ignored. This Fourier series expression reflects the characteristics of voltage distortion in the frequency domain. Based on the Fourier series expression, an optimization objective function for dead-time compensation is constructed. The form of the objective function is:

[0077] ;

[0078] where is the harmonic weight coefficient, which increases with the increase of the harmonic order to reflect the greater weight of higher harmonics on the system. is the square of the amplitude of the th harmonic, representing the intensity of the harmonic energy. The optimization objective of the objective function is to minimize the harmonic energy, thereby reducing the voltage distortion. To optimize the objective function , its gradient is calculated to obtain the gradient information of the objective function. The gradient is the directional derivative of the function change, and its form is:

[0079] ;

[0080] where is the dead-time compensation amount, and the result of the gradient calculation is used to update . The update formula is:

[0081] ;

[0082] where is the The compensation amount for the next iteration is the learning rate, which is used to control the step size of the update. After each iteration, the convergence of the compensation time is judged. If the absolute value of the gradient or the change in the objective function value is less than the set threshold, it is considered that the iteration has converged, and the optimal dead-time compensation amount is obtained . This compensation amount is used to correct the PWM control signal and reduce the voltage distortion caused by the dead time. For example, assume the voltage distortion function , where the fundamental frequency . Through Fourier analysis, the fundamental amplitude , the second harmonic amplitude , and the third harmonic amplitude are obtained. The objective function is:

[0083] ;

[0084] Assume the weight coefficients , , , then . Through iterative optimization , make and significantly reduced in amplitude. For example, after optimization , , and finally drops to 102.25. This shows that the optimization process effectively reduces harmonic distortion and improves system performance

[0085] In a specific embodiment, the process of executing step S3 may specifically include the following steps:

[0086] Divide the sampling period of the target PWM control signal to obtain a discretized sampling sequence;

[0087] Based on the discretized sampling sequence and the current system state, perform state-space modeling on the charging pile system, and obtain a discrete state-space model by solving the state equation and the output equation;

[0088] According to the discrete state-space model, set the prediction horizon N and the control horizon M, and perform recursive prediction on the states of the charging pile system in the next A sampling periods to obtain a state prediction sequence;

[0089] Perform reference trajectory planning on the state prediction sequence, set tracking target values for voltage, current, and power, and obtain a performance index function by calculating the deviation between the predicted value and the target value;

[0090] Substitute the performance index function into the rolling optimization solver, set the constraint conditions for voltage and current, and obtain the optimal PWM control sequence within M control periods through online iterative optimization;

[0091] Perform dead-time compensation and PWM modulation on the first control quantity of the optimal PWM control sequence to obtain the current control output. Based on the current control output, perform a one-step prediction update on the system state, use the predicted state as the initial state for the next control cycle to obtain the state update value, input the state update value into the predictive controller, repeat the optimization calculation process, and obtain the optimal control output through rolling horizon calculation.

[0092] Specifically, divide the sampling period of the target PWM control signal, and generate a discretized sampling sequence by discretizing the continuous signal. Assume the sampling period is , and the target PWM control signal is . After discretization, the PWM signal is expressed as , where is the index of the sampling moment. Convert the continuous dynamic system into a mathematical form that can be processed in discrete time. Based on the discretized sampling sequence and the current system state , establish a discrete state-space model of the charging pile system. The state-space model is described by a state equation and an output equation, which are respectively:

[0093] ;

[0094] ;

[0095] Among them, is the state transition matrix, which describes the dynamic characteristics of the system state; is the input matrix, which describes the influence of the input on the system state is the output matrix, which defines the relationship between the state and the output; is the direct transfer matrix, which describes the direct influence of the input on the output. The state variable is the internal state of the system, including variables such as the DC bus voltage and current. The input variable is the discretized PWM control signal, and the output variable is the observable quantity of the system, such as the output power or the load voltage. After constructing the discrete state-space model, set the prediction horizon and the control horizon , and use the model to recursively predict the state within the next sampling periods. The prediction process is based on the recursive expansion of the state equation. Assume that the state at the current moment is , and the predicted states at the next moments are:

[0096] ;

[0097] Among them represents the predicted time index, indicating the power of the state transition matrix. The recurrence formula describes how the system state is affected by the current state and the input sequence in the future time domain. The predicted state sequence provides dynamic information of the system in the future time domain for reference trajectory planning. In trajectory planning, tracking target values are set for variables such as voltage , current and power etc., [[ID=|14]] , , , and the performance index function is defined by calculating the deviation between the predicted value and the target value. For example, the performance index function adopts a quadratic form:

[0098] ;

[0099] where represents the cumulative sum of the squared predicted deviations and is the objective function of the optimization problem. Substitute the performance index function into the rolling optimization solver and combine the constraint conditions of voltage and current, such as and , to generate the optimal PWM control sequence within control cycles during the online iteration process. The optimization process uses linear or quadratic programming algorithms to minimize the objective function by adjusting the input sequence , and updates the predicted state in each iteration to ensure the satisfaction of the constraint conditions. After the optimization solution, select the first control quantity of the optimal PWM control sequence and perform dead-time compensation on it. Assume the dead time is , and the compensated control signal is:

[0100] ;

[0101] where is the correction amount calculated according to the dead time . The corrected PWM control signal is input to the PWM modulator and compared with the triangular carrier signal to generate the control output at the current moment. Based on the current control output , perform a one-step prediction update on the system state and calculate the state at the next moment using the state equation:

[0102] ;

[0103] Update the state As the initial state of the next control period is input to the predictive controller, the above optimization calculation process is repeated, and the control strategy is continuously adjusted through the calculation of the rolling time domain to achieve optimal control under dynamic operating conditions and obtain the optimal control output.

[0104] In a specific embodiment, the process of substituting the performance index function into the rolling optimization solver, setting the constraint conditions of voltage and current, and obtaining the optimal PWM control sequence within M control periods through online iterative optimization may specifically include the following steps:

[0105] Perform a standard quadratic form transformation on the performance index function. By constructing the matrix H = 2(G T QG + R) and the vector f = 2G T (Fx(k) - W), where H is the coefficient matrix, T is the transpose of the matrix, G is the prediction matrix, Q is the state weighting matrix, R is the control weighting matrix, f is the vector, F is the state prediction matrix, x is the optimization variable, k is the sampling time, and W is the reference trajectory vector, to obtain a standard quadratic programming problem;

[0106] Set the constraint condition matrix according to the quadratic programming problem. By constructing the inequality constraint Lx ≤ b and the equality constraint Leqx = beq, where L and Leq are the constraint matrices, and b and beq are the constraint vectors, to obtain the optimization problem;

[0107] Perform positive definiteness analysis on the coefficient matrix H of the optimization problem to obtain the solution conditions of the optimization problem, and construct the feasible region for the solution conditions to obtain the control quantity constraint;

[0108] Substitute the control quantity constraint into the state recurrence equation for forward calculation to obtain the state quantity constraint, and initialize the solution of the optimization problem based on the state quantity constraint. By selecting the initial iteration point and setting the convergence threshold, obtain the initial conditions for iterative solution;

[0109] Input the initial conditions into the interior point method solver for iterative calculation. Process the constraint conditions through the barrier function method to obtain the control sequence within M control periods, and verify the optimality of the control sequence. By calculating the satisfaction degree of the KKT conditions, obtain the optimal PWM control sequence.

[0110] Specifically, transform the optimization problem of the system into a standard form for convenient numerical solution. Assume that the performance index function of the system is the sum of the squared deviations from the tracking target value plus the energy term of the control input, and its expression is:

[0111] ;

[0112] Where, is the predicted state vector, is the reference trajectory vector, is the control input vector, is the state weighting matrix, which measures the cost of state deviation, is the control weighting matrix, which suppresses the drastic change of the control input, is the prediction horizon length. To construct a quadratic programming problem, the performance index function is represented in matrix form. By defining the prediction matrix and the state prediction matrix , the state sequence in the future steps is expressed as a linear combination of the current state and future control inputs as:

[0113] ;

[0114] where, is the stacked vector of the future state sequence, is the stacked vector of the future control inputs. The matrices and represent the initial contribution of state transition and the influence of control inputs respectively, and their specific forms are determined by the state space model of the system. Substituting the above state representation into the performance index function, it is rewritten as:

[0115] ;

[0116] where, is the stacked vector of the reference trajectory. After combining related terms, the performance index function is transformed into the standard quadratic form:

[0117] ;

[0118] where, the coefficient matrices and the vectors are expressed as:

[0119] ;

[0120] At this time, the optimization variable is restricted by the constraint conditions, and the inequality constraints and equality constraints need to be further defined. Assuming that the control input has upper and lower bounds , and the state has upper and lower bounds , they are represented in matrix form as:

[0121] ;

[0122] where, and are the constraint matrices, and are the corresponding constraint vectors. After constructing the optimization problem, for the coefficient matrix Perform positive definiteness analysis to ensure the solvability of the optimization problem. Positive definiteness analysis is carried out by checking the eigenvalues of the matrix . If all eigenvalues are greater than zero, then is a positive definite matrix, and the optimization problem has a unique solution. If is not positive definite, it is necessary to readjust the and weight parameters to enhance the controllability of the system. After determining the positive definiteness of the optimization problem, construct a feasible region to limit the range of control inputs and the changes in states. By substituting the control quantity constraints into the state recurrence equation, state quantity constraints are obtained, thereby constructing a more stringent feasible region. To improve the efficiency of the optimization calculation, initialize the solution of the optimization problem, select the initial iteration point and set the convergence threshold. The selection of the initial iteration point is based on the predicted value of the current control input, and the convergence threshold is used to judge the end condition of the optimization process. Input the initialized optimization problem into the interior point method solver, and gradually approximate the optimal solution through iterative calculations. In the interior point method, introduce the barrier function to transform the constraint conditions into part of the objective function, thus avoiding directly dealing with the constraint problem. The objective function of the optimization problem is adjusted to:

[0123] ;

[0124] where is the barrier parameter, which is used to control the intensity of constraint handling. In each iteration, update and optimize until the objective function converges. The interior point method solver outputs the control sequence within control periods, and perform optimality verification on it. By calculating whether the KKT conditions (Karush-Kuhn-Tucker conditions) are satisfied, judge the quality of the optimization solution. If the KKT conditions are satisfied, it indicates that the control sequence is the global optimal solution. The first control quantity of the optimal PWM control sequence is used for the current control output, and the subsequent control quantities are reserved for future prediction updates.

[0125] In a specific embodiment, the process of executing step S4 may specifically include the following steps:

[0126] Judge the power flow direction state in the current control output, calculate the sending power Ps = Vs × Is and the receiving power Pr = Vr × Ir, where Vs and Is are the sending-end voltage and current respectively, and Vr and Ir are the receiving-end voltage and current respectively, to obtain the power flow direction judgment result;

[0127] Perform power reduction control on the power of the current working mode according to the power flow direction judgment result to obtain the power reduction process curve;

[0128] Set the power reduction step size and the interval time for each reduction based on the power reduction process curve to obtain a segmented power reduction control sequence;

[0129] Perform PWM duty cycle modulation on the segmented power reduction control sequence to obtain a PWM control quantity sequence, and input the PWM control quantity sequence into the power converter. Monitor the fluctuation of the DC bus voltage to obtain a voltage stability criterion;

[0130] Determine the working mode switching moment according to the voltage stability criterion to obtain a mode switching trigger signal;

[0131] Switch the charging pile control strategy based on the mode switching trigger signal. By updating the control parameter matrix, obtain the control parameters of the target working mode, and perform power output control on the control parameters of the target working mode to obtain a new power output control quantity.

[0132] Specifically, accurately judge the power flow direction state in the current control output. By measuring the voltage and current at the sending end and the receiving end, calculate the sending power and the receiving power , where and respectively represent the voltage and current at the sending end, while and represent the voltage and current at the receiving end. These parameters are obtained through real-time sampling, and the sampling period is , so that the power calculation at discrete moments is and . By comparing and , determine the direction of the power flow: when , the power flow direction is from the sending end to the receiving end; otherwise, it is from the receiving end to the sending end. After determining that the power flow direction needs to be changed, to ensure the system stability, perform power reduction control on the power of the current working mode. This process is achieved by constructing a power reduction process curve, and the power curve adopts a linear, exponential or other smooth function form, and the specific form depends on the dynamic characteristics and response requirements of the system. For example, for linear reduction, the power curve is expressed as:

[0133] ;

[0134] where is the initial power, is the power reduction speed. For exponential reduction, the curve form is:

[0135] ;

[0136] where is the exponential decay coefficient. By selecting an appropriate curve form, ensure that the power smoothly drops to zero within the specified time, avoiding impacts on the system caused by sudden changes. Based on the power decline process curve, set the power decline step size and the interval time for each decline to generate a segmented power reduction control sequence. Assume that the step size for each power decline is , and the interval time is , then the segmented power control sequence is expressed as

[0137] ;

[0138] where is the total number of segments, is the total time for the power to completely drop to zero. The segmented processing can provide sufficient control flexibility while ensuring dynamic stability. The generated segmented power reduction control sequence is converted into an actual control signal through PWM duty cycle modulation. The principle of duty cycle modulation is to adjust the duty cycle of the PWM signal according to the target power value. Assume that the maximum duty cycle is , then the duty cycle corresponding to the current power is:

[0139] ;

[0140] where is the maximum power output of the system. The modulated PWM control quantity sequence is directly input into the power converter to regulate the power output. While performing the power reduction operation, the fluctuation of the DC bus voltage is monitored in real time to judge the stability of the system. The calculation formula for the bus voltage fluctuation is:

[0141] ;

[0142] where is the current bus voltage, is the reference value of the bus voltage. When is less than the preset threshold , it is considered that the system reaches a stable state and triggers a mode switching signal. After the mode switching signal is triggered, the system needs to update the control strategy by reloading the control parameter matrix to adapt to the target working mode. Assume that the control parameter matrix of the current mode is , and the control parameter matrix of the target mode is , then the matrix replacement needs to be completed during the switching process:

[0143] ;

[0144] These parameters include specific values such as duty cycle range, frequency adjustment, etc. In the new mode, by executing the updated control parameters, power output control is performed on the target operating mode to obtain a new power output. For example, assume that after switching, the system enters the reverse power flow mode, and the target power is , and the corresponding PWM duty cycle is recalculated to be .

[0145] In this embodiment, the above AC-DC charging pile bidirectional power flow control method further includes: real-time monitoring of the charging pile operation status according to the system stable operation control parameter set. When a power device or sensor failure is detected, based on the fault diagnosis result, a corresponding controller reconstruction strategy is selected to adaptively adjust the PWM modulation scheme to obtain fault-tolerant control optimization parameters, including: real-time acquisition of the switching tube voltage and current characteristic parameters of the power converter, and by constructing a fault feature vector F(t) = {v(t), i(t), dv / dt, di / dt}, where v(t) is the switching tube voltage, i(t) is the switching tube current, dv / dt is the voltage change rate, di / dt is the current change rate, and t is the sampling time. Data analysis is performed on the feature vector to obtain a fault feature matrix; a fault diagnosis decision tree is constructed based on the fault feature matrix, and the fault type is judged by calculating the fault probability P(F) = P(F|H) × P(H) / P(F), where P(F|H) is the fault conditional probability representing the feature probability when the fault occurs, P(H) is the prior probability representing the historical occurrence probability of the fault type, and P(F) is the feature probability, to obtain the fault diagnosis result; the fault diagnosis result is input into the fault-tolerant decision maker, and the fault level is calculated based on the fault impact degree evaluation function I(f) = Σwi × si, where wi is the weight coefficient of each fault index, si is the corresponding fault severity index, f is the fault type identifier, and i is the fault index number, to obtain the fault level determination result; a controller reconstruction scheme is selected according to the fault level determination result, and the controller parameters are updated online through the reconstruction matrix R = [r1, r2,..., rn], where ri is the i-th reconstruction parameter and n is the total number of reconstruction parameters, and each ri corresponds to an adjustment amount of a specific controller parameter, to obtain the reconstructed controller parameters; stability analysis is performed on the reconstructed controller parameters, and the root distribution of the characteristic equation λI - A = 0 is calculated, where λ is the eigenvalue, A is the system state matrix, and I is the identity matrix. The system is determined to be stable when the real part of all system eigenvalues is less than zero, to obtain the system stability criterion; the PWM modulation scheme is adjusted based on the system stability criterion, and the new switching timing t'(k) = t(k) + Δt(k) is calculated, where t'(k) is the adjusted switching timing, t(k) is the original switching timing at the current sampling period k, Δt(k) is the timing adjustment amount calculated based on the stability criterion, and k is the sampling sequence number, to obtain the optimized PWM modulation scheme; the optimized PWM modulation scheme is compared with the original control strategy, and the control performance is calculated through the performance evaluation function J = Σ(αxi 2 +βui 2 ), where α is the weight coefficient of the state variable, β is the weight coefficient of the control quantity, xi is the i-th state variable of the system, and ui is the i-th control quantity, to obtain the performance evaluation result; the fault-tolerant control parameters are optimized according to the performance evaluation result, and through iterative update , where θ(k + 1) is the control parameter vector at the next moment, θ(k) is the control parameter vector at the current moment, η is the iterative learning rate used to control the parameter update step size, is the gradient of the performance evaluation function with respect to the control parameter, k is the number of iterations, and the fault-tolerant control optimization parameters are obtained.

[0146] The bidirectional power flow control method for AC-DC charging piles in the embodiments of the present invention has been described above. Next, the bidirectional power flow control device for AC-DC charging piles in the embodiments of the present invention will be described. Please refer to Figure 2 An embodiment of the bidirectional power flow control device for AC-DC charging piles in the embodiments of the present invention includes:

[0147] A collection module, configured to detect and collect parameters of the AC-DC charging pile, the power converter, and the grid connection point, and obtain a set of operating parameters of the charging pile system;

[0148] A calculation module, configured to perform trapezoidal voltage edge calculation on the switching process of the power converter according to the set of system operating parameters, obtain the dead-time compensation amount, and superimpose the dead-time compensation amount on the PWM control signal of the power converter to obtain the target PWM control signal;

[0149] A switching module, configured to input the target PWM control signal and the current system state into the predictive controller for discrete mathematical modeling, calculate and obtain the optimal PWM control sequence, and use the first control quantity of the optimal PWM control sequence as the current control output;

[0150] An acquisition module, configured to judge the operating state of the AC-DC charging pile according to the current control output. When it is detected that the power flow direction needs to be changed, control the power output of the current operating mode to gradually decrease to zero. After the DC bus voltage of the power converter is stabilized at the set value, switch to the target operating mode to obtain a new power output control quantity.

[0151] Through the collaborative cooperation of the above-mentioned various components, by calculating the trapezoidal voltage edge of the power converter switching process and compensating for the dead time, the low-order harmonics are effectively suppressed, and the power quality of the system is improved. By adopting the discrete mathematical modeling method based on predictive control and combining with the rolling optimization algorithm, the system has strong dynamic response ability and shortens the power adjustment time. A complete power flow switching control strategy is designed. Through segmented power reduction and voltage stability criteria, the smoothness of the charge and discharge mode switching process is ensured, and the DC bus voltage fluctuation is controlled within ±2%. The iterative prediction calculation method is introduced to dynamically update the dead time compensation amount, so that the compensation effect remains stable within the full working range, and the system efficiency is improved. Through state space modeling and performance index optimization, the robustness of the system is enhanced, enabling it to adapt to the operating requirements under different working conditions and reducing the system failure rate. By adopting the online iterative optimization algorithm, the dependence on current sampling is reduced, the influence of communication delay on system performance is reduced, and the real-time performance and reliability of control are improved.

[0152] Referring to Figure 3 , an embodiment of the present invention further provides a computer device, which may be a server, and its internal structure may be as Figure 3 shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0153] Those skilled in the art can understand that Figure 3 the structure shown in

[0154] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0155] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0156] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0157] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc., which can store program codes.

[0158] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A bidirectional power flow control method for AC-DC charging piles, characterized in that, The method includes: Detecting and collecting parameters of the AC / DC charging pile, power converter, and grid connection point to obtain the operating parameter set of the charging pile system; According to the system operation parameter set, a trapezoidal voltage edge calculation is performed on the switching process of the power converter to obtain a dead time compensation amount, and the dead time compensation amount is superimposed on the PWM control signal of the power converter to obtain a target PWM control signal; specifically including: modeling the switching voltage of the power converter according to the parasitic capacitance parameters of the switching tube in the system operation parameter set to obtain a voltage-time function of the switching process, and performing a trapezoidal edge characteristic analysis on the voltage-time function of the switching process to obtain a voltage change rate curve; based on the voltage change rate curve, the switch tube conduction process is segmented, t1 is set as the dead time parameter, t2 is the switch delay time parameter, and the segmented voltage function v1(t) is the pre-switch conduction voltage, v2(t) is the dead zone The voltage during the period, v3(t) is the conduction voltage after the switch is turned on; the segmented voltage function is substituted into the voltage distortion calculation formula Vd(t)=v1(t)+v2(t)+v3(t), where Vd(t) is the total voltage distortion and t is the time variable, to obtain a voltage distortion mathematical model; the voltage distortion mathematical model is periodically divided to obtain a Fourier analysis interval, and the fundamental component of the voltage distortion function is extracted according to the Fourier analysis interval to obtain the fundamental component; the voltage distortion function is subjected to high-order harmonic calculation to obtain each harmonic component, and a Fourier series expression is established based on the fundamental component and each harmonic component to obtain a frequency domain representation of the voltage distortion; based on the frequency domain representation of the voltage distortion, a dead time compensation optimization objective function J=Σkn×(an 2 +bn 2 ), where kn is the nth harmonic weight coefficient, and the nth harmonic weight coefficient increases as the harmonic number n increases. 2 +bn 2 represents the square of the amplitude of the nth harmonic; performing gradient calculation on the dead time compensation optimization objective function to obtain the objective function gradient, and substituting the objective function gradient into an iterative formula to update the compensation amount to obtain an iterative sequence of compensation time; performing convergence judgment on the iterative sequence of compensation time to obtain a dead time compensation amount; modifying the PWM control signal based on the dead time compensation amount to obtain a compensated control signal, and inputting the compensated control signal into a PWM modulator to compare it with a triangular carrier to obtain a target PWM control signal; Inputting the target PWM control signal and the current system state into the predictive controller for discrete mathematical modeling, calculating to obtain the optimal PWM control sequence, and using the first control quantity of the optimal PWM control sequence as the current control output; Judging the operating state of the AC / DC charging pile according to the current control output. When it is detected that the power flow direction needs to be changed, controlling the power output of the current operating mode to gradually decrease to zero. After the DC bus voltage of the power converter is stabilized at the set value, switching to the target operating mode to obtain a new power output control quantity.

2. The bidirectional power flow control method for AC-DC charging piles according to claim 1, wherein The detecting and collecting parameters of the AC / DC charging pile, power converter, and grid connection point to obtain the operating parameter set of the charging pile system includes: Setting voltage sensor sampling points at the input and output ends of the AC / DC charging pile respectively, performing digital conversion on the voltage signals to obtain the input end voltage value and the output end voltage value; Extracting the power frequency components and calculating the reactive power of the input end voltage value and the output end voltage value to obtain the real-time power data of the AC / DC charging pile; Testing the parasitic capacitance of each switching tube of the power converter respectively, measuring the voltage rise time and fall time at both ends of the switching tube at different switching frequencies to obtain the parasitic capacitance parameters of the switching tube; Measuring the dead time online based on the voltage waveform at both ends of the switching tube, and calculating the time interval between the rising edge and the falling edge of the voltage waveform to obtain the dead time parameter; Performing timing analysis on the control signal and the conduction state of the switching tube of the power converter, and calculating the time difference from the effective edge of the control signal to the actual conduction of the switching tube to obtain the switching delay time parameter; Sampling and calculating the three-phase grid voltage and current through the voltage and current sensors set at the grid connection point, and obtaining the effective value and frequency value of the grid voltage; Forming a data matrix with the input end voltage value, the output end voltage value, the real-time power data, the parasitic capacitance parameters of the switching tube, the dead time parameter, the switching delay time parameter, the effective value and frequency value of the grid voltage to obtain the operating parameter set of the charging pile system.

3. The bidirectional power flow control method for AC-DC charging piles according to claim 1, characterized in that The inputting the target PWM control signal and the current system state into the predictive controller for discrete mathematical modeling, calculating to obtain the optimal PWM control sequence, and using the first control quantity of the optimal PWM control sequence as the current control output includes: Dividing the sampling period of the target PWM control signal to obtain a discretized sampling sequence; Performing state space modeling on the charging pile system based on the discretized sampling sequence and the current system state, and obtaining the discrete state space model by solving the state equation and the output equation; Setting the prediction horizon N and the control horizon M according to the discrete state space model, and recursively predicting the states of the charging pile system in the future A sampling periods to obtain the state prediction sequence; Performing reference trajectory planning on the state prediction sequence, setting tracking target values for voltage, current, and power, and obtaining the performance index function by calculating the deviation between the predicted value and the target value. Substitute the performance index function into the rolling optimization solver, set the constraint conditions for voltage and current, and obtain the optimal PWM control sequence within M control cycles through online iterative optimization; Perform dead-time compensation and PWM modulation on the first control quantity of the optimal PWM control sequence to obtain the current control output, perform a one-step prediction update on the system state based on the current control output, use the predicted state as the initial state for the next control cycle to obtain the state update value, input the state update value into the predictive controller, repeat the optimization calculation process, and obtain the optimal control output through rolling horizon calculation.

4. The bidirectional power flow control method of the AC-DC charging pile according to claim 3, characterized in that, The step of substituting the performance index function into the rolling optimization solver, setting the constraint conditions for voltage and current, and obtaining the optimal PWM control sequence within M control cycles through online iterative optimization includes: Perform a standard quadratic form transformation on the performance index function. By constructing the matrix H = 2(G T QG + R) and the vector f = 2G T (Fx(k) - W), where H is the coefficient matrix, T is the transpose of the matrix, G is the prediction matrix, Q is the state weighting matrix, R is the control weighting matrix, f is the vector, F is the state prediction matrix, x is the optimization variable, k is the sampling time, and W is the reference trajectory vector, a standard quadratic programming problem is obtained; Set the constraint condition matrix according to the quadratic programming problem, and obtain the optimization problem by constructing the inequality constraint Lx ≤ b and the equality constraint Leqx = beq, where L and Leq are constraint matrices, and b and beq are constraint vectors; Perform positive definiteness analysis on the coefficient matrix H of the optimization problem to obtain the solution conditions of the optimization problem, and construct the feasible region for the solution conditions to obtain the control quantity constraints; Substitute the control quantity constraints into the state recurrence equation for forward calculation to obtain the state quantity constraints, and perform solution initialization on the optimization problem based on the state quantity constraints. By selecting the initial iteration point and setting the convergence threshold, obtain the initial conditions for iterative solution; Input the initial conditions into the interior point method solver for iterative calculation, process the constraint conditions through the barrier function method to obtain the control sequence within M control cycles, and perform optimality verification on the control sequence. By calculating the satisfaction degree of the KKT conditions, obtain the optimal PWM control sequence.

5. The bidirectional power flow control method for the AC-DC charging pile according to claim 4, wherein The step of judging the working state of the AC-DC charging pile according to the current control output. When it is detected that the power flow direction needs to be changed, control the power output of the current working mode to gradually decrease to zero. After the DC bus voltage of the power converter is stabilized at the set value, switch to the target working mode to obtain a new power output control quantity, includes: Judge the power flow direction state in the current control output, calculate the sending power Ps = Vs × Is and the receiving power Pr = Vr × Ir, where Vs and Is are the sending-end voltage and current respectively, and Vr and Ir are the receiving-end voltage and current respectively, to obtain the power flow direction judgment result; Perform power reduction control on the power of the current working mode according to the power flow direction judgment result to obtain the power reduction process curve; Set the power reduction step size and the interval time for each reduction based on the power reduction process curve to obtain the segmented power reduction control sequence; Perform PWM duty cycle modulation on the segmented power reduction control sequence to obtain the PWM control quantity sequence, input the PWM control quantity sequence into the power converter, and monitor the fluctuation amount of the DC bus voltage to obtain the voltage stability criterion; Determine the working mode switching moment according to the voltage stability criterion to obtain the mode switching trigger signal; Based on the mode switching trigger signal, switch the charging pile control strategy. By updating the control parameter matrix, obtain the control parameters of the target working mode, and perform power output control on the control parameters of the target working mode to obtain a new power output control quantity.

6. A bidirectional power flow control device for AC-DC charging piles, characterized in that, For implementing the bidirectional power flow control method of the AC-DC charging pile according to any one of claims 1-5, the bidirectional power flow control device of the AC-DC charging pile includes: A collection module, configured to detect and collect parameters of the AC-DC charging pile, the power converter, and the grid connection point to obtain a set of charging pile system operation parameters; A calculation module, configured to perform trapezoidal voltage edge calculation on the switching process of the power converter according to the set of system operation parameters to obtain a dead time compensation amount, and superimpose the dead time compensation amount on the PWM control signal of the power converter to obtain a target PWM control signal; A switching module, configured to input the target PWM control signal and the current system state into a predictive controller for discrete mathematics modeling, calculate an optimal PWM control sequence, and use the first control quantity of the optimal PWM control sequence as the current control output; An acquisition module, configured to judge the working state of the AC-DC charging pile according to the current control output. When it is detected that the power flow direction needs to be changed, control the power output of the current working mode to gradually decrease to zero. After the DC bus voltage of the power converter is stabilized at a set value, switch to the target working mode to obtain a new power output control quantity.

7. A computer device, characterized in that, It includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the bidirectional power flow control method of the AC-DC charging pile according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, A computer program is stored thereon. When the computer program is run by the processor, the processor is caused to execute the bidirectional power flow control method of the AC-DC charging pile according to any one of claims 1 to 5.

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