Charging pile energy storage connector system and method with adaptive current distribution function

Abstract

The application relates to the technical field of current distribution, and discloses a charging pile energy storage connector system with an adaptive current distribution function and a method, which comprises the following steps: collecting and filtering data of each charging port of a charging pile to obtain a port real-time operation parameter matrix; performing port load power calculation on the port real-time operation parameter matrix to obtain port load power, and calculating load imbalance and charging efficiency according to the port load power; calculating adaptive distribution indexes of each charging port based on the load imbalance and the charging efficiency; calculating optimal current distribution values of each charging port according to the adaptive distribution indexes; and performing PWM signal duty ratio modulation and current dynamic adjustment on power electronic converters of each charging port according to the optimal current distribution values to obtain real-time output currents of each charging port, so that accurate control of a charging process is realized, and the safety and reliability of the charging process are ensured.

Description

Technical Field

[0001] This application relates to the field of current distribution technology, and in particular to a charging pile energy storage connector system and method with adaptive current distribution function. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the construction and operation of charging infrastructure are facing increasing challenges. Traditional charging pile systems generally adopt a fixed current distribution strategy, which cannot dynamically adjust the charging power of each port according to the actual load, resulting in low overall system charging efficiency. Especially in scenarios where multiple vehicles are charging simultaneously, the differences in load characteristics of each charging port can easily lead to uneven distribution of charging resources, affecting the user charging experience.

[0003] Furthermore, existing charging pile systems often neglect the influence of energy storage connector characteristics during current distribution, making it difficult to guarantee power transmission efficiency during actual charging. Since the impedance characteristics of energy storage connectors vary significantly under different operating conditions, failure to accurately consider this factor will affect the stability and reliability of the charging system. Summary of the Invention

[0004] This application provides a charging pile energy storage connector system and method with adaptive current distribution function, thereby realizing precise control of the charging process and ensuring the safety and reliability of the charging process.

[0005] The first aspect of this application provides a control method for a charging pile energy storage connector with adaptive current distribution function, the control method for the charging pile energy storage connector with adaptive current distribution function includes:

[0006] Data is collected and filtered from each charging port of the charging pile to obtain a matrix of real-time operating parameters for the port.

[0007] The port load power is calculated by performing a real-time operating parameter matrix on the port to obtain the port load power, and the load imbalance and charging efficiency are calculated based on the port load power.

[0008] Based on the load imbalance and the charging efficiency, calculate the adaptive allocation index for each charging port;

[0009] The optimal current allocation value for each charging port is calculated based on the adaptive allocation index.

[0010] Based on the optimal current allocation value, the power electronic converter of each charging port is subjected to PWM signal duty cycle modulation and dynamic current adjustment to obtain the real-time output current of each charging port.

[0011] A second aspect of this application provides a charging pile energy storage connector system with adaptive current distribution function, the charging pile energy storage connector system with adaptive current distribution function includes:

[0012] The data acquisition module is used to acquire and filter data from each charging port of the charging pile to obtain a real-time operating parameter matrix of the port.

[0013] The power calculation module is used to calculate the port load power based on the real-time operating parameter matrix of the port, obtain the port load power, and calculate the load imbalance and charging efficiency based on the port load power.

[0014] An index calculation module is used to calculate the adaptive allocation index of each charging port based on the load imbalance and the charging efficiency.

[0015] The current calculation module is used to calculate the optimal current allocation value for each charging port based on the adaptive allocation index.

[0016] The dynamic adjustment module is used to perform PWM signal duty cycle modulation and dynamic current adjustment on the power electronic converter of each charging port according to the optimal current allocation value, so as to obtain the real-time output current of each charging port.

[0017] Compared with existing technologies, this application has the following advantages: By real-time monitoring and filtering of the voltage, current, and power factor of the charging port, an accurate port operating parameter matrix is ​​obtained, improving the stability of system operation. An adaptive allocation index calculation method based on load imbalance and charging efficiency is adopted, combined with corrections based on the characteristics of the energy storage connector, achieving reasonable allocation of charging resources and significantly improving the overall charging efficiency of the system. A genetic algorithm is introduced for multi-objective optimization; through Pareto optimal solution selection, the optimal balance point is found among multiple objectives such as minimizing system losses, maximizing charging efficiency, and maximizing load balance. A PWM modulation method based on a feedforward-feedback composite control strategy is designed, achieving rapid response to load changes through dynamic current adjustment, improving the dynamic performance of the system. Dead-time compensation and misalignment delay control technologies are used to effectively reduce the switching losses of the power electronic converter, improving the energy conversion efficiency of the system. Closed-loop tracking control and real-time output current prediction enable precise control of the charging process, ensuring its safety and reliability. Through the synergistic effect of multi-level optimization and control strategies, efficient operation of the charging pile system under different operating conditions is achieved, improving the utilization rate of charging equipment. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] The structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0020] Figure 1 This is a flowchart illustrating the control method for a charging pile energy storage connector with adaptive current distribution function provided in an embodiment of the present invention.

[0021] Figure 2 This is a schematic block diagram of the structure of a charging pile energy storage connector system with adaptive current distribution function provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0024] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0025] It should also be further understood that the term "and / or" as used in this application specification and the appended claims refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations. See also Figure 1 One embodiment of the charging pile energy storage connector control method with adaptive current distribution function in this application includes:

[0026] Step 100: Collect and filter data from each charging port of the charging pile to obtain the real-time operating parameter matrix of the port;

[0027] It is understood that the executing entity of this application can be a charging pile energy storage connector system with adaptive current distribution function, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0028] Specifically, voltage sensors are installed at each charging port of the charging pile to collect raw voltage data in real time. The raw signals are then filtered. A series of high-pass and low-pass filters are used. The high-pass filter eliminates low-frequency interference, such as DC components or slowly changing trends, while the low-pass filter removes high-frequency noise, retaining useful frequency components to obtain the RMS voltage data. Simultaneously, current sensors are installed at each charging port to collect raw current data. This raw current data is also processed using a series of high-pass and low-pass filters. The high-pass filter removes low-frequency components from the current signal, while the low-pass filter removes high-frequency interference, yielding the RMS current data. The RMS voltage and current data are then input into a power analyzer for processing. The power analyzer multiplies the input voltage and current signals to calculate the active power data of the port and analyzes the phase difference between the voltage and current signals, using the phase difference and signal amplitude to calculate the reactive power data. Active power data characterizes the actual energy consumed by the port, while reactive power data reflects the useless power cycling caused by inductive or capacitive loads in the port. The power factor data of the port is obtained by performing trigonometric function calculations on the active and reactive power data. The power factor is an important parameter for measuring energy utilization efficiency; its value is the ratio of active power to apparent power, calculated using the following formula: Power factor equals active power divided by apparent power, and apparent power equals the product of the effective voltage value and the effective current value. This calculation can intuitively reflect the power characteristics of the charging port. The port effective voltage data, port effective current data, port active power data, port reactive power data, and port power factor data are integrated into a real-time operating parameter matrix for the port using a matrix concatenation method. The matrix construction represents the operating status of different ports in a unified structure, forming the following form: each row corresponds to a charging port, and each column corresponds to a parameter, such as voltage, current, active power, reactive power, and power factor, constructing a multi-dimensional data matrix that comprehensively reflects the operating status of the charging pile.

[0029] Step 200: Calculate the port load power based on the port real-time operating parameter matrix to obtain the port load power, and calculate the load imbalance and charging efficiency based on the port load power.

[0030] Specifically, active power data for each charging port is extracted from the real-time operating parameter matrix. This active power data is then categorized and processed, organizing the active power information of different ports according to time or other specific dimensions to obtain port active power distribution data. This data describes the power allocation of each charging port in the system and reflects the actual operating status of different port loads. The standard deviation and mean of the active power distribution data are calculated to determine the fluctuation range of port power. The standard deviation measures the degree to which the power of each port deviates from the average level, while the mean provides a reference for the overall power level. By calculating these indicators, the fluctuation range of port power is obtained, thereby assessing the stability of the system at different times. Based on this fluctuation range, the active power distribution data is filtered, i.e., outliers or power data exceeding reasonable ranges are removed to improve the accuracy of the analysis. The filtered data is then averaged to obtain the load power of the port, reflecting the actual load level borne by each charging port. Maximum and minimum values ​​of the port load power are retrieved. By finding the maximum and minimum values ​​of the port load power, the differences in load distribution within the system can be intuitively judged. This difference directly affects the system's load balance. The load imbalance is calculated based on the difference between the maximum and minimum port power, combined with the sum of the port load power. Dividing the difference between the maximum and minimum port power by the sum of the port load power and then multiplying by 100% yields the load imbalance, reflecting the uniformity of the system's load distribution. A smaller value indicates a more uniform load and smoother system operation. Simultaneously, the output active power data and input apparent power data in the port's real-time operating parameter matrix are processed to calculate the instantaneous charging efficiency of each charging port. Instantaneous charging efficiency is calculated as the ratio of output active power to input apparent power, reflecting the energy conversion efficiency of each port at a given moment. Since instantaneous charging efficiency is affected by short-term system fluctuations, an exponentially weighted average is used. This exponentially weighted average, by assigning higher weights to recent data and gradually decreasing the weights to earlier data, more accurately reflects the dynamic trend of the port's average charging efficiency. Finally, a weighted sum is calculated based on the port's average charging efficiency and the proportion of each port's load power in the total system power to obtain the overall system charging efficiency. The weighted summation process can comprehensively consider the impact of each port on the overall power distribution. The charging efficiency of each port is weighted according to its importance and then summed to ensure that the calculation of system efficiency is global and representative.

[0031] Step 300: Calculate the adaptive allocation index of each charging port based on the load imbalance and charging efficiency;

[0032] It should be noted that the load imbalance score is obtained by multiplying it by the first weighting coefficient to adjust its influence in the scoring. The charging efficiency score is generated by multiplying the charging efficiency by the second weighting coefficient. The power factor score is generated by multiplying the power factor data in the port's real-time operating parameter matrix by the third weighting coefficient and performing a weighted calculation. The system operating status score is obtained by adding the load balance score, charging efficiency score, and power factor score. The standardized load rate is obtained by dividing the port load power by the rated load of each charging port, eliminating the absolute value difference of the port load power and making the load conditions of different ports comparable. Simultaneously, the relative efficiency value is obtained by dividing the charging efficiency by the system's highest charging efficiency, reflecting the deviation of the current port's efficiency level from the system's optimal state. To optimize the allocation index, the power factor score is divided by the target power factor to generate a correction coefficient, reflecting the degree of deviation of the actual power factor from the ideal target, used to correct the final allocation result. Based on the above calculation results, the standardized load rate, relative efficiency value, and correction coefficient are weighted by preset weighting coefficients to obtain the initial allocation index. Real-time acquisition of input and output voltage and current data from the charging pile energy storage connector is performed to calculate the connector's power transmission efficiency. The power transmission efficiency is combined with the initial allocation index, and a correction algorithm is used to dynamically adjust the initial allocation index to obtain an adaptive allocation index that considers the characteristics of the energy storage connector.

[0033] Step 400: Calculate the optimal current allocation value for each charging port based on the adaptive allocation index;

[0034] Specifically, the standardized load rate, relative efficiency value, and correction coefficient of each charging port are used to calculate the target allocation ratio, forming the initial allocation weight for each charging port. The weight calculation reflects the allocation priority of the port under the current load conditions. Based on the standardized indicators of the port's own operating state, the initial estimate of current allocation is both comparable and reflects port differences. After completing the calculation of the initial allocation weight, multiple independent subpopulations are generated through replication. Each subpopulation contains several chromosome individuals, and the code of each chromosome represents the current allocation value of each charging port. After the population is generated, the fitness of the chromosome individuals in each subpopulation is evaluated to measure the merits of the current allocation scheme corresponding to each individual. The fitness evaluation is based on three objectives: minimizing system loss, maximizing charging efficiency, and maximizing load balance. By comprehensively considering the impact of these indicators on system performance, the evaluation results ensure that the evaluation results can comprehensively reflect the merits of the current allocation scheme. In this process, the performance of each chromosome individual on multiple objectives is quantified through mathematical models. For example, energy utilization efficiency is characterized by calculating the total power loss of the system, and the stability of the system is described by the load balance index. After fitness evaluation, a tournament selection strategy is used to screen for high-quality individuals from each subpopulation, generating parent individuals. The tournament selection strategy randomly selects several individuals from the subpopulations and compares their fitness values, choosing the best-performing individual as the parent. This selection mechanism helps improve the overall quality of the population while avoiding premature convergence. Simulated binary crossover and polynomial mutation operations are performed on the parent individuals to generate offspring. Simulated binary crossover mimics gene exchange in the genetic process, generating new chromosomal individuals among the parents, thus increasing population diversity. Polynomial mutation applies small random perturbations to certain genes on the chromosomes, improving the population's fitness and exploratory capabilities. To promote information exchange between subpopulations, migration operations are performed on the offspring every certain number of generations, using a circular topology to exchange optimal individuals between adjacent subpopulations. Migration operations transfer high-quality individuals from one subpopulation to neighboring subpopulations, allowing the entire population evolution process to fully utilize the optimal solutions of each subpopulation, thereby improving the efficiency and quality of global optimization. After the migration operation is completed, current and power constraints are applied to the individuals in the migrated population for verification. Current constraints ensure that the allocated current at each charging port does not exceed its rated value, thus protecting the safety of the hardware. Power constraints ensure that the total allocated power of the system does not exceed the maximum output power of the energy storage device, guaranteeing the overall stability of the system. After passing the constraint verification, non-dominated sorting and congestion distance calculations are performed on the feasible solution set to identify the solution set on the Pareto optimal front. Non-dominated sorting compares the multi-objective dominance relationships between individuals, selecting the set of individuals not dominated by other solutions. These solutions achieve a balance in terms of system losses, charging efficiency, and load balancing.Crowding distance calculation is used to evaluate the distribution of solutions on the Pareto front, thereby guiding the population to evolve towards a more uniform and diversified direction. The solution set on the Pareto optimal front is weighted and scored based on the system loss, charging efficiency, and load balancing degree of each individual, and the solution with the best overall performance is selected as the final optimal current allocation value. The weighted summation process can take into account the actual operating requirements of the system, prioritizing the importance of different objectives by adjusting the weight coefficients, thereby achieving a dynamic balance between efficiency, stability, and safety, ensuring that the current allocation scheme not only meets technical constraints but also maximizes system performance.

[0035] Step 500: Based on the optimal current allocation value, perform PWM signal duty cycle modulation and dynamic current adjustment on the power electronic converter of each charging port to obtain the real-time output current of each charging port.

[0036] Specifically, the optimal current allocation value is input into the controller of each charging port to set the target output value for each port. These target values ​​reflect the current allocation required by each port under the current operating conditions of the charging pile system. Real-time data is collected from the current sensors of the charging ports to obtain the real-time output current value of each port. By calculating the difference between the real-time current value and the aforementioned port control target value, the current tracking error value for each port is obtained, reflecting the deviation between the actual output current of the current port and the target value. The current tracking error value is input into the PI (Proportional-Integral) controller, and a feedback control output value is generated through proportional-integral calculation. The role of the PI controller is to adjust the proportional and integral coefficients to correct the current output in real time, making it quickly follow the changes in the target value, while minimizing steady-state error. During this process, the rate of change of the port control target value is analyzed to assess the dynamic characteristics of the load and predict the load's response behavior. The predicted load dynamic response can reflect the possible current adjustment needs of the system when the target value changes. Based on the predicted load dynamic response value, feedforward compensation calculation is performed to generate the feedforward control output value. Feedforward control pre-adjusts the control signal based on the trend of target value changes, thereby reducing system response time and fluctuations during the adjustment process. Combining feedback and feedforward control achieves precise and rapid control of the output current while improving the system's dynamic performance and stability. The feedback control output value and the feedforward control output value are weighted and superimposed to generate a PWM modulation control signal. This weighted superposition process integrates the error correction capability of feedback control and the dynamic compensation effect of feedforward control, ensuring that the control signal simultaneously meets the requirements of steady-state accuracy and dynamic response. Carrier modulation calculations are performed on the PWM modulation control signal to generate the IGBT (Insulated Gate Bipolar Transistor) switching waveform for the power electronic converter. Carrier modulation employs methods such as sinusoidal pulse width modulation (SPWM) or space vector pulse width modulation (SVPWM), generating a suitable switching signal by comparing the control signal with the carrier wave. This driving waveform directly determines the switching state of the power electronic converter, thus affecting the waveform and amplitude of the output current. The generated IGBT switching waveform is input into the power electronic converter's drive circuit to complete real-time switching control of the switching transistors. The drive circuit controls the IGBT to turn on and off, thereby achieving DC-to-AC or DC-to-DC power conversion. It also adjusts the output current based on the duty cycle of the PWM signal to obtain the real-time output current of each charging port.

[0037] Data is extracted from the PWM modulation control signal to generate a sampling sequence of control signals. This sampling sequence captures the dynamic characteristics of the PWM signal through uniform sampling, preserving its amplitude variation information. To ensure the standardization and compatibility of subsequent calculations, the sampling sequence is normalized to generate a normalized modulation wave. A frequency multiplication operation is performed on the triangular carrier signal to obtain a high-frequency carrier signal. This frequency increase enhances the system's modulation accuracy, ensuring the output current more accurately follows the target value changes. The normalized modulation wave and the high-frequency carrier signal are then synchronized to generate a synchronous carrier modulation signal. This phase synchronization ensures a good match between the modulation wave and the carrier, reducing the impact of phase errors on the final output waveform. Based on the synchronous carrier modulation signal, a feedforward-feedback composite control strategy is applied to dynamically calculate the duty cycle, generating a duty cycle modulation waveform. The feedforward control section directly adjusts the duty cycle based on changes in the input target, improving the system's dynamic response speed, while the feedback control section compensates for the shortcomings of the feedforward control through real-time error correction, thus ensuring output accuracy. Voltage clamping and overmodulation suppression are applied to the duty cycle modulation waveform to effectively control its amplitude range and prevent unreasonable modulation overshoot, thus protecting the safe operation of the power electronic converter. Based on this, dead-time compensation is applied to the optimized modulation waveform to eliminate the influence of dead time on the output waveform during IGBT switching, resulting in a compensated PWM drive signal. This compensated PWM drive signal is then input to the opto-isolation module of the driver for electrical isolation, generating an isolation drive voltage. Opto-isolation ensures electrical isolation between the control circuit and the power circuit, enhancing the system's anti-interference capability and safety. The isolation drive voltage is further processed by staggered delay control to generate a staggered drive signal, preventing overlap of parallel switches during turn-on and turn-off, thus avoiding short-circuit risks. After gate drive voltage clamping and overcurrent protection operations on the staggered drive signal, the IGBT switching drive waveform is generated. Precise control of the drive voltage amplitude and timing ensures the high efficiency and reliability of IGBT switching operation. The generated drive waveform, through the power electronic converter drive circuit, ultimately controls the current change at the output terminal. Based on the transfer function model of the duty cycle of the IGBT switching waveform and the output current of the charging port, dynamic response analysis is performed on the system to calculate the predicted value of the output current. The predicted output current reflects the output trend under the current control state through real-time calculation, providing a basis for closed-loop control. By performing closed-loop tracking control on the predicted output current value and correcting deviations, precise control of the real-time output current of each charging port is ultimately achieved.

[0038] In this embodiment, by real-time monitoring and filtering of the voltage, current, and power factor of the charging port, an accurate port operating parameter matrix is ​​obtained, improving the stability of system operation. An adaptive allocation index calculation method based on load imbalance and charging efficiency is adopted, and corrected by combining the characteristics of the energy storage connector, achieving reasonable allocation of charging resources and significantly improving the overall charging efficiency of the system. A genetic algorithm is introduced for multi-objective optimization, and through the screening of Pareto optimal solutions, the optimal balance point is found among multiple objectives such as minimizing system losses, maximizing charging efficiency, and maximizing load balance. A PWM modulation method based on a feedforward-feedback composite control strategy is designed, achieving rapid response to load changes through dynamic current adjustment, improving the dynamic performance of the system. Dead-time compensation and misalignment delay control technologies are used to effectively reduce the switching losses of the power electronic converter and improve the energy conversion efficiency of the system. Through closed-loop tracking control and real-time output current prediction, precise control of the charging process is achieved, ensuring the safety and reliability of the charging process. Through the synergistic effect of multi-level optimization and control strategies, efficient operation of the charging pile system under different operating conditions is achieved, improving the utilization rate of the charging equipment.

[0039] In one specific embodiment, the process of performing step 100 may specifically include the following steps:

[0040] Data is collected from the voltage sensors installed at each charging port in the charging pile to obtain the raw port voltage data. The raw port voltage data is then processed by a series of high-pass and low-pass filters to obtain the effective value data of the port voltage.

[0041] Data is collected from the current sensors installed at each charging port in the charging pile to obtain the raw port current data. The raw port current data is then processed by a series of high-pass and low-pass filters to obtain the effective value data of the port current.

[0042] Input the port voltage RMS data and port current RMS data into the power analyzer for product operation and phase difference calculation to obtain port active power data and port reactive power data;

[0043] Trigonometric function calculations are performed on the active power data and reactive power data at the port to obtain the port power factor data.

[0044] The port voltage RMS data, port current RMS data, port active power data, port reactive power data, and port power factor data are matrix-stitched to obtain the port real-time operating parameter matrix.

[0045] Specifically, high-precision voltage sensors are installed at each charging port of the charging station to collect raw voltage data for each port. Let's assume the raw voltage data for a certain port is V. raw(t), which is a continuous function of voltage over time. For V raw (t) Perform cascaded high-pass and low-pass filtering. The purpose of high-pass filtering is to remove low-frequency interference. Let the transfer function of the high-pass filter be H. high (f), whose frequency response satisfies when f>f c H high (f) = 1, where f c It is the cutoff frequency; otherwise, H high (f) = 0. After high-pass filtering, the signal becomes:

[0046] V high (t)=F -1 {H high (f)·F{V raw (t)}};

[0047] Where F and F -1 These represent the Fourier transform and inverse Fourier transform, respectively. A low-pass filter is used to eliminate high-frequency noise. Let the transfer function of the low-pass filter be H. low (f), satisfying when f <f c H low (f) = 1, otherwise H low (f) = 0. The signal after low-pass filtering is:

[0048] V low (t)=F -1 {H low (f)·F{V high (t)}};

[0049] After high-pass and low-pass filtering in series, the effective voltage value of the port is obtained, denoted as V. eff The result is obtained by calculating the average square formula:

[0050]

[0051] Where T is the period of the signal. Similar to voltage signal processing, the raw data I collected by the current sensor at the port... raw (t) Perform high-pass and low-pass filtering in series to obtain the high-pass filtered signal I. high (t) and the low-pass filtered signal I low (t), and finally the effective value of the current I is calculated. eff :

[0052]

[0053] The obtained V eff and I effIn the input power analyzer, the active and reactive power data at the port are obtained by calculating their product and phase difference. Active power P is defined as:

[0054] P = V eff ·I eff ·cosφ;

[0055] Where φ is the phase difference between voltage and current, describing their time delay relationship. Reactive power Q is defined as:

[0056] Q = V eff ·I eff ·sinφ;

[0057] The P and Q obtained from the above calculations are used to solve for the power factor cosφ, and the formula is as follows:

[0058]

[0059] To construct the port's real-time operating parameter matrix, the effective voltage value V eff , current effective value I eff The active power P, reactive power Q, and power factor cosφ are integrated. The final operating parameter matrix is ​​expressed as follows:

[0060]

[0061] Where n is the number of charging ports.

[0062] In one specific embodiment, the process of performing step 200 may specifically include the following steps:

[0063] The active power data of the ports in the real-time operating parameter matrix of the ports are extracted and classified to obtain the active power distribution data of each charging port. The standard deviation and mean of the active power distribution data are calculated to obtain the port power fluctuation range.

[0064] Based on the port power fluctuation range, the active power distribution data is filtered and the average value is calculated to obtain the port load power. The maximum and minimum values ​​of the port load power are then retrieved to obtain the system's maximum port power and minimum port power.

[0065] The load imbalance is obtained by dividing the difference between the system's maximum port power and the system's minimum port power by the sum of the port load power and multiplying by 100%.

[0066] The instantaneous charging efficiency of each charging port is obtained by performing a division operation on the output active power data and input apparent power data in the port real-time operating parameter matrix.

[0067] The instantaneous charging efficiency is calculated by performing an exponential weighted average to obtain the port average charging efficiency. The charging efficiency is then calculated by weighting and summing the port average charging efficiency with the proportion of each port load power in the total system power.

[0068] Specifically, the active power data for each port is extracted from the real-time operating parameter matrix. Let the real-time operating parameter matrix of the ports be M. 参数 The i-th row represents the operating data of the i-th port, including the voltage V. eff,i Current I eff,i Active power P i Reactive power Q i and power factor cosφ i Active power data from all ports were extracted and categorized by time series to form an active power distribution dataset {P}. i,t}, where P i,t Let represent the active power at port i at time t. The standard deviation and mean of the active power distribution data for each port are calculated to measure the range of power fluctuations. The standard deviation describes the dispersion of the power data, while the mean represents the overall power level. The standard deviation σ of port i is... i and mean μ i Calculated using the following formula:

[0069]

[0070] Where T is the total number of sampling time points, P i,t This is the power value at a certain moment. σ is calculated... i and μ i This yields the power fluctuation range of the port, describing the dynamic characteristics of the port power. The active power distribution data is then filtered based on the power fluctuation range to remove outliers or noise. This can be achieved by setting a filtering threshold, for example, limiting the power value to [μ]. i -2σ i ,μ i +2σ i Within the range specified, only power data within this range is retained for subsequent calculations. The load power P of the port is calculated by averaging the filtered power data. load,i :

[0071]

[0072] Where N i P is the number of valid data points remaining after filtering. i,t,filtered This represents the filtered power data. The load power {P} for all ports. load,i The maximum and minimum values ​​are retrieved to obtain the system's maximum port power P. maxand system minimum port power P min Based on this, the load imbalance of the system is calculated, defined as the ratio of the difference between the maximum port power and the minimum port power to the total load power. The formula for the load imbalance δ is:

[0073]

[0074] Where n is the number of ports. A smaller load imbalance value indicates a more uniform load distribution in the system. After completing the load analysis, the charging efficiency is calculated. The output active power data P from the port real-time operating parameter matrix is ​​used. i and input apparent power data S i =V eff,i ·I eff,i Perform a division operation to obtain the instantaneous charging efficiency η of each port. i,t :

[0075]

[0076] Instantaneous charging efficiency varies over time. To obtain a more stable efficiency value, an exponentially weighted average is calculated. The average charging efficiency of the i-th port is... Calculated using the following recursive formula:

[0077]

[0078] Where α is a weighting coefficient, ranging from 0 < α < 1, representing the weight of recent data. The overall charging efficiency η of the system is calculated by weighting and summing the average charging efficiency of the port with the proportion of its load power in the total system power. sys :

[0079]

[0080] In one specific embodiment, the process of performing step 300 may specifically include the following steps:

[0081] The load imbalance is multiplied by the first weighting coefficient and weighted to obtain the load balance score, and the charging efficiency is multiplied by the second weighting coefficient and weighted to obtain the charging efficiency score.

[0082] The power factor score is obtained by multiplying the power factor data in the real-time operating parameter matrix of the port by a third weighting coefficient and performing a weighted calculation.

[0083] The load balance score, charging efficiency score, and power factor score are added together to obtain the system operating status score.

[0084] The standardized load rate is obtained by dividing the port load power by the rated load of each charging port, and the relative efficiency is obtained by dividing the charging efficiency by the system's highest charging efficiency.

[0085] The power factor score is divided by the target power factor to obtain the correction coefficient. The initial allocation index is obtained by weighting the standardized load rate, relative efficiency value and correction coefficient with the preset weight coefficient.

[0086] The input and output voltage and current data of the energy storage connector of the charging pile are collected to obtain the power transmission efficiency of the energy storage connector. Based on the power transmission efficiency, the initial allocation index is corrected and calculated to obtain an adaptive allocation index that takes into account the characteristics of the energy storage connector.

[0087] Specifically, the load imbalance of the system is multiplied by a first weighting coefficient and then weighted to obtain the load balance score. The load imbalance δ is an important indicator describing the evenness of load distribution across the system's ports, and its formula is:

[0088]

[0089] Where P max and P min These represent the system's maximum and minimum port load power, P. load,i Let represent the load power of the i-th port, and n be the total number of ports. The load balance score S is obtained by multiplying the load imbalance by the first weighting coefficient w1. balance :

[0090] S balance =w1·(1-δ);

[0091] Where 1-δ is used to convert to load balancing degree, the closer the value is to 1, the more even the distribution. The charging efficiency is multiplied by a second weighting coefficient and weighted to obtain the charging efficiency score. Charging efficiency η sys The energy conversion efficiency of a system is defined as follows:

[0092]

[0093] Where η i The charging efficiency score S is obtained by multiplying the charging efficiency of the i-th port by the weight w2. efficiency :

[0094] S e ffi c i ency =w2·η sys ;

[0095] The power factor score is calculated by multiplying the power factor data in the real-time operating parameter matrix of the port by a third weighting coefficient. Power factor cosφ i The power factor score S represents the energy utilization efficiency of the i-th port. By weighting the efficiency, the power factor performance of the entire system is quantified. power_factor The calculation formula is:

[0096]

[0097] Where w3 is the third weighting coefficient. The load balancing score S calculated above... balance Charging efficiency rating S efficiency and power factor score S power_facton Add them together to get the system operating status score S. system :

[0098]

[0099] To optimize allocation decisions, the standardized load factor, relative efficiency value, and power factor correction factor are calculated. The standardized load factor λ... i This represents the ratio of the load power of each port to its rated load.

[0100]

[0101] Where P rated,i Let be the rated load power of the i-th port. The relative efficiency value ρ. i This represents the port charging efficiency relative to the system's highest charging efficiency, η. max The ratio:

[0102]

[0103] Power factor correction factor γ i This is used to correct the difference between the actual power factor and the target power factor cosφ. target The deviation between them is calculated using the following formula:

[0104]

[0105] Standardized load rate λ i Relative efficiency value ρ i and correction factor γ i Each with a preset weighting coefficient w λ ,w ρ ,w γ We perform a weighted calculation to obtain the initial allocation index I. i :

[0106] I i =w λ ·λ i+w ρ ·ρ i +w γ ·γ i ;

[0107] After obtaining the initial allocation index, it is corrected based on the power transfer efficiency of the energy storage connector. This is achieved by collecting the voltage V at the input and output terminals of the energy storage connector. in V out and current I in ,I out Calculate the power transmission efficiency η connector :

[0108]

[0109] Corrected adaptive allocation index The calculation formula is:

[0110]

[0111] In one specific embodiment, the process of performing step 400 may specifically include the following steps:

[0112] The target allocation ratio is calculated for the standardized load rate, relative efficiency value and correction coefficient of each charging port to obtain the initial allocation weight of each charging port.

[0113] The initial weight allocation is replicated to construct N independent subpopulations, each subpopulation containing M chromosome individuals, and each chromosome individual encodes the current allocation value of each charging port.

[0114] The fitness of chromosome individuals in each subpopulation is evaluated to obtain individual fitness values. The fitness evaluation is constructed based on the system loss minimization index, the charging efficiency maximization index, and the load balance maximization index.

[0115] Based on the tournament selection strategy, high-quality individuals are selected from the chromosome individuals of each subpopulation to obtain parent individuals. Simulated binary crossover and polynomial mutation operations are then performed on the parent individuals to obtain the offspring population.

[0116] Every P generations, a migration operation is performed on the offspring population to obtain the migrated population. The migration operation uses a circular topology to exchange the best individuals between adjacent offspring populations.

[0117] The individuals in the migrated population are verified by current and power constraints to obtain a feasible solution set. The current constraint ensures that the current at each port does not exceed the rated value, and the power constraint ensures that the total power of the system does not exceed the maximum output power of the energy storage device.

[0118] The feasible solution set is sorted by non-dominated order and the congestion distance is calculated to obtain the solution set on the Pareto optimal front. The system loss, charging efficiency and load balance of each individual in the solution set on the Pareto optimal front are weighted and scored to obtain the optimal current allocation value.

[0119] Specifically, the target allocation ratio is calculated for each charging port based on its standardized load rate, relative efficiency value, and correction coefficient to obtain the initial allocation weight. Assume the standardized load rate of the i-th port is... Where P load,i P represents the current load power of the port. rated,i This is the rated load power of the port; the relative efficiency value is... Where η i For port charging efficiency, η max This represents the system's highest charging efficiency; the correction factor is... Where cosφ i For the port power factor, cosφ target The target power factor. Initial weights w. i The calculation formula is:

[0120] w i =w λ ·λ i +w ρ ·ρ i +w γ ·γ i ;

[0121] Where w λ w ρ and w γ These are the weighting factors for the standardized load rate, relative efficiency value, and correction coefficient, respectively. The initial weighting is used to generate the population, and N independent subpopulations are constructed through replication. Each subpopulation contains M chromosome individuals. The chromosome individuals are encoded with the current allocation values ​​{I} for all charging ports. i}, where each I i This represents the allocated current value at the i-th port. The purpose of population construction is to perform evolutionary optimization using a genetic algorithm based on diverse individuals. The fitness of chromosome individuals in each subpopulation is evaluated to measure the merits of each allocation scheme. The fitness evaluation is based on three optimization objectives: minimizing system loss, maximizing charging efficiency, and maximizing load balancing. The objective function for minimizing system loss is expressed as:

[0122]

[0123] Where R i Let be the equivalent resistance of the i-th port. The objective function for maximizing charging efficiency is:

[0124]

[0125] The objective function for maximizing load balancing is calculated by using the load uniformity index δ. balance accomplish:

[0126]

[0127] The overall evaluation of the fitness function adopts a weighted summation method:

[0128] f fitness =w loss ·f loss +w efficiency ·f efficiency +w balance ·δ balance ;

[0129] Where w loss w efriciency and w balance These are the weighting coefficients for each optimization objective. After fitness evaluation, a tournament selection strategy is used to select high-quality individuals from the offspring population to serve as parents. Tournament selection involves randomly selecting several individuals, comparing their fitness values, and choosing the individual with the best fitness to become a parent. Simulated binary crossover and polynomial mutation operations are then performed on the parent individuals to generate the offspring population. The crossover operation simulates gene recombination in the genetic process, and the crossover probability P is controlled. c Information exchange between individuals is achieved; polynomial mutation enhances population diversity by introducing small random variations, preventing the algorithm from getting trapped in local optima. Every P generations, a migration operation is performed on the offspring population, exchanging the best individuals between adjacent offspring populations through a circular topology. The migration operation accelerates the convergence of the global optimum through the crossover and propagation of the best individuals. After the migration is completed, current and power constraints are applied to the individuals in the population for verification. The current constraint ensures that each port is allocated a current I. i Not exceeding its rated value I rated,i :

[0130] I i ≤I rated,i ;

[0131] Power constraints ensure that the total allocated power does not exceed the maximum output power P of the energy storage device. max :

[0132]

[0133] After constraint verification, the feasible solution set is sorted by non-dominated order and congestion distance is calculated to select the solution set on the Pareto optimal front. Non-dominated ordering finds solutions that are not dominated by other solutions on any objective by comparing their relative merits. Congestion distance is used to evaluate the uniformity of solution distribution on the Pareto front, encouraging solution diversity. A weighted summation score is applied to the solution set on the Pareto optimal front to comprehensively evaluate the system loss, charging efficiency, and load balancing of the solutions, and the solution with the best fitness is selected as the final optimal current allocation value. For example, assuming there are three ports, after optimization, three solutions {I} are obtained on the Pareto front. 1,j ,I 2,j ,I 3,j}, with corresponding fitness values ​​of {f fitness,1 ,f fitness,2 ,f fitness,3 The optimal solution is selected by comparing fitness values. As the final allocation scheme.

[0134] In one specific embodiment, the process of performing step 500 may specifically include the following steps:

[0135] The optimal current allocation value is input into the controller of each charging port to set the target value, thus obtaining the port control target value;

[0136] The difference between the real-time current value collected by the current sensor of each charging port and the target value of the port control is calculated to obtain the current tracking error value;

[0137] The current tracking error value is input into the PI controller for proportional-integral calculation to obtain the feedback control output value. The load characteristics are analyzed based on the rate of change of the port control target value to obtain the load dynamic response prediction value.

[0138] The feedforward compensation calculation is performed on the predicted value of the load dynamic response to obtain the feedforward control output value;

[0139] The PWM modulation control signal is obtained by weighted superposition of the feedback control output value and the feedforward control output value.

[0140] Carrier modulation calculation is performed on the PWM modulation control signal to obtain the IGBT switching transistor drive waveform of the power electronic converter. The IGBT switching transistor drive waveform is then input into the drive circuit of the power electronic converter for switching control to obtain the real-time output current of each charging port.

[0141] Specifically, the optimal current allocation value {I} opt,i The input is sent to the controller of each charging port to set the target output value I of the port. target,i For each port i, the target value I target,iThe current output at each charging port directly reflects the current that should be output and is a key reference quantity in the control process. The optimal allocation scheme is translated into specific control indicators to guide real-time current adjustment. The output current I of each charging port is collected in real time using a current sensor. actual,i (t), and the corresponding target value I target,i Perform interpolation to calculate the current tracking error value ΔI. i (t):

[0142] ΔI i (t)=I target,i -I actual,i (t);

[0143] Where ΔI i (t) represents the magnitude of the error at port i at time t, reflecting the deviation between the actual output current and the target value. The error value ΔI i (t) is input to the proportional-integral (Pl) controller, where proportional-integral calculations are performed to generate the feedback control output value U. feedback,i (t). The core formula of the PI controller is:

[0144]

[0145] Where K p It is the proportional gain, K i It is integral gain. This represents the integral term of the accumulated error. The proportional term is used for rapid response to instantaneous changes in error, while the integral term eliminates steady-state error, ensuring that the output current accurately tracks the target value. Simultaneously, based on the target value I... target,i rate of change dI target,i / dt, analyze the dynamic characteristics of the load to obtain the predicted dynamic response value U of the load. predict,i (t). This predicted value is used to describe the system current response behavior caused by changes in the target value, and its calculation formula is as follows:

[0146]

[0147] Where K f It is feedforward gain. This represents the instantaneous rate of change of the target value. The predicted value U... predict,i (t) Input the feedforward controller to perform feedforward compensation calculation and generate the feedforward control output value U. feedforward,i (t). Feedforward control aims to adjust the output signal in advance according to the dynamic changes of the target value, thereby improving the system's rapid response capability. The feedback control output value U... feedback,i (t) and feedforward control output value U feedforward,i (t) Perform weighted superposition operation to generate PWM modulation control signal U PWM,i (t):

[0148] U PWM,i (t)=w d ·U feedback,i (t)+w ff ·U feedforward,i (t);

[0149] Where w f and w ff These are the weighting factors for feedback control and feedforward control, determined through system debugging to achieve a balance between response speed and control accuracy. The generated PWM modulation control signal U... PWM,i (t) Carrier modulation calculations are performed to obtain the IGBT switching waveform of the power electronic converter. Carrier modulation employs either sinusoidal pulse width modulation (SPWM) or space vector pulse width modulation (SVPWM). Taking SPWM as an example, the modulated signal is compared with a high-frequency triangular carrier wave to generate the switching signal:

[0150]

[0151] Among them U carrier (t) is the carrier signal, S IGBT,i (t) is a binary signal representing the IGBT's on (1) and off (0) states. The generated IGBT drive waveform S... IGBT,i (t) The input power electronic converter's drive circuit controls the on and off states of the switching transistors, thereby regulating the output current. Output current I output,i (t) and target value I target,i Dynamic matching completes the entire current control process.

[0152] In one specific embodiment, the process of performing carrier modulation calculation on the PWM modulation control signal to obtain the IGBT switching transistor drive waveform of the power electronic converter, and inputting the IGBT switching transistor drive waveform into the drive circuit of the power electronic converter for switching control to obtain the real-time output current of each charging port can specifically include the following steps:

[0153] Data extraction is performed on the PWM modulation control signal to obtain the control signal sampling sequence, and the amplitude of the control signal sampling sequence is normalized to obtain the normalized modulation wave.

[0154] The frequency of the triangular carrier signal is multiplied to obtain a high-frequency carrier signal, and the phase synchronization of the normalized modulated wave and the high-frequency carrier signal is calculated to obtain a synchronous carrier modulated signal.

[0155] The duty cycle of the synchronous carrier modulation signal is dynamically calculated based on the feedforward-feedback composite control strategy to obtain the duty cycle modulation waveform.

[0156] Voltage clamping and overmodulation suppression are applied to the duty cycle modulation waveform to obtain an optimized modulation waveform. Dead time compensation is then applied to the optimized modulation waveform to obtain the compensated PWM drive signal.

[0157] The compensated PWM drive signal is input to the opto-isolation module of the driver for electrical isolation to obtain the isolation drive voltage. The isolation drive voltage is then subjected to misalignment delay control to obtain the misalignment drive signal.

[0158] By clamping the gate drive voltage and protecting it from overcurrent, the IGBT switch drive waveform is obtained.

[0159] Dynamic response analysis is performed based on the transfer function model of the duty cycle of the IGBT switching waveform and the output current of the charging port to obtain the predicted value of the output current. Closed-loop tracking control is then performed on the predicted value of the output current to obtain the real-time output current of each charging port.

[0160] Specifically, data extraction is performed on the PWM modulation control signal to obtain the signal sampling sequence. Let the PWM control signal be U. PWM (t), and discretely sample it to obtain the sampling sequence {U PWM,n} where n represents the discrete time step. The sampling sequence describes the discrete changes of the PWM signal over time and retains the signal's amplitude information. To facilitate subsequent processing, the sampling sequence is normalized to obtain the normalized modulated wave. The normalization formula is:

[0161]

[0162] Among them U max and U min These represent the maximum and minimum values ​​of the sampled sequence, respectively. The normalized signal range is limited to [0,1], facilitating matching and modulation with the carrier signal. A high-frequency carrier signal is generated for modulation. A triangular wave is chosen as the carrier signal, with an initial frequency of f. carrier To improve modulation accuracy, the frequency is multiplied to obtain a high-frequency carrier signal. Assuming the frequency multiplication factor is k, then the frequency of the high-frequency carrier is f. carrier,high =k·f carrier The expression for a high-frequency carrier signal is:

[0163]

[0164] Where T carrier =1 / f carrier,high It is the period of the high-frequency carrier. This indicates a floor operation. It normalizes the modulated wave. With high-frequency carrier signal Phase synchronization calculations are performed to obtain the synchronization carrier modulation signal. Phase synchronization ensures that the modulation operations of both carriers are consistent in time and amplitude, thereby reducing modulation errors. Based on the synchronous carrier modulation signal, a feedforward-feedback composite control strategy is used to dynamically calculate the duty cycle of the signal, generating a duty cycle modulation waveform. The feedforward control section predicts the output based on the dynamic rate of change of the modulated signal, while the feedback control section uses error correction to fix the signal deviation. The dynamic calculation formula for the duty cycle is:

[0165]

[0166] in It is the real-time error of the modulated signal, K p and K f These represent the proportional gain and the feedforward gain, respectively. To ensure the safety and stability of the modulated waveform, voltage clamping and overmodulation suppression are applied to the duty cycle modulated waveform to obtain an optimized modulated waveform. Voltage clamping limits the waveform amplitude to the range [0,1], while overmodulation suppression avoids overload of the drive signal by adjusting the duty cycle dynamic range. Dead time compensation is applied to the optimized waveform to generate a compensated PWM drive signal. The expression for dead time compensation is:

[0167]

[0168] Where ΔT dead This is the dead time setting. The compensated PWM drive signal is input to the driver's opto-isolation module for electrical isolation, generating an isolation drive voltage. The purpose of opto-isolation is to provide electrical isolation between control circuits and power circuits, thereby improving the system's anti-interference capability and safety. Based on this, staggered delay control is applied to the isolation drive voltage to generate a staggered drive signal. The misalignment delay is set according to specific current requirements to ensure that the operation of different channels does not interfere with each other. Gate drive voltage clamping and overcurrent protection are applied to the misalignment drive signal to generate the IGBT switching transistor drive waveform S. IGBT (t). Gate clamping ensures the amplitude of the drive signal remains within the safe operating range of the IGBT, while overcurrent protection prevents overload damage by monitoring the current. Dynamic response analysis is used to determine the duty cycle of the IGBT switching waveform. Transfer function model of charging port output current Combined, predict the output current.

[0169]

[0170] Among them and -1These represent the Laplace transform and its inverse transform, respectively. Closed-loop tracking control is applied to the predicted output current, and the output current is ensured by dynamically adjusting the PWM signal. Exactly match target value I target .

[0171] The control method for the charging pile energy storage connector with adaptive current distribution function further includes: designing a reverse peak voltage suppression for the solid-state relay drive circuit of the charging pile energy storage connector, wherein a suppression unit composed of a diode and a Zener diode is connected in parallel at the output terminal of the solid-state relay to obtain a drive circuit resistant to voltage surges; integrating an optocoupler isolation module to the output terminal of the solid-state relay to obtain a drive module with feedback signal isolation function, and sampling the output signal of the drive module to obtain a drive state feedback signal; measuring the rise and fall times of the drive state feedback signal to obtain drive circuit reset time data, and optimizing the parameters of the suppression unit based on the reset time data to obtain optimized drive circuit parameters; setting a Hall effect current sensing module at the output terminal of the charging pile energy storage connector to obtain the original current sampling signal, and performing operational amplification processing on the original current sampling signal. A high-precision current measurement signal is obtained; dynamic response characteristics of the high-precision current measurement signal are analyzed to obtain dynamic error data of the current measurement, and the gain parameters of the operational amplifier are calibrated according to the dynamic error data of the current measurement to obtain the compensated measurement circuit parameters; the high-precision current measurement signal is resampled and amplified based on the compensated measurement circuit parameters to obtain the calibrated current measurement value; the calibrated current measurement value is compared and analyzed with the preset standard current value to obtain static measurement accuracy data, and the current measurement circuit is corrected and compensated according to the static measurement accuracy data to obtain the final current measurement result; piecewise linear fitting is performed on the final current measurement result to obtain the current measurement characteristic curve, and the current measurement characteristic curve is used as a feedback calibration signal input to the closed-loop control system of the real-time output current of each charging port to obtain the calibrated real-time output current of each charging port.

[0172] In this embodiment, obtaining the real-time output current of each charging port after calibration further includes: extracting time-series features from the historical operating data of each charging port to obtain a multi-dimensional time-series feature matrix containing voltage, current, power, and charging efficiency; normalizing the multi-dimensional time-series feature matrix to obtain standardized feature data; inputting the standardized feature data into the first-layer threshold cyclic unit for time-series dependency analysis to obtain the time-series correlation vector of the port operating state; mapping the time-series correlation vector using an adaptive threshold function to obtain filtered state features; calculating the cyclic connection weights of the filtered state features to obtain a state transition probability matrix; constructing a Markov chain model for port load prediction based on the state transition probability matrix to obtain initial prediction results; inputting the initial prediction results and actual operating data into the second-layer threshold cyclic unit for error analysis to obtain a prediction deviation sequence; and performing autocorrelation on the prediction deviation sequence. The system performs a performance analysis to obtain an error correction factor. Based on this factor, the weight parameters of the two-layer threshold recurrent network are updated online to obtain optimized network parameters. The load changes at the charging ports are then re-predicted based on these optimized parameters, yielding a corrected load prediction result. This corrected load prediction result is input into a neural network surrogate model for parallel training, resulting in a probability distribution model of the port load characteristics. Confidence interval analysis is then performed on this model to obtain an uncertainty assessment result for the load prediction. Based on this uncertainty assessment result, the charging control strategy is dynamically optimized to obtain a control parameter set considering the prediction uncertainty. This set is then input into an adaptive PI controller to obtain a compensated control command. Fuzzy rule reasoning is performed on the compensated control command to obtain a fuzzy control gain matrix, which is then superimposed as a correction factor onto the PWM modulation control signals of each charging port.

[0173] The control method for the charging pile energy storage connector with adaptive current distribution function in the embodiments of this application has been described above. The charging pile energy storage connector system 10 with adaptive current distribution function in the embodiments of this application is described below. Please refer to... Figure 2 One embodiment of the charging pile energy storage connector system 10 with adaptive current distribution function in this application includes:

[0174] The data acquisition module 11 is used to acquire and filter data from each charging port of the charging pile to obtain a real-time operating parameter matrix of the port.

[0175] The power calculation module 12 is used to calculate the port load power from the real-time operating parameter matrix of the port, obtain the port load power, and calculate the load imbalance and charging efficiency based on the port load power.

[0176] The index calculation module 13 is used to calculate the adaptive allocation index of each charging port based on the load imbalance and charging efficiency.

[0177] The current calculation module 14 is used to calculate the optimal current allocation value for each charging port based on the adaptive allocation index.

[0178] The dynamic adjustment module 15 is used to perform PWM signal duty cycle modulation and dynamic current adjustment on the power electronic converter of each charging port according to the optimal current distribution value, so as to obtain the real-time output current of each charging port.

[0179] Through the collaborative efforts of the aforementioned components, and by real-time monitoring and filtering of the voltage, current, and power factor of the charging ports, an accurate port operating parameter matrix is ​​obtained, improving the stability of system operation. An adaptive allocation index calculation method based on load imbalance and charging efficiency, combined with corrections based on the characteristics of the energy storage connector, achieves rational allocation of charging resources, significantly improving the overall charging efficiency of the system. A genetic algorithm is introduced for multi-objective optimization; by screening for Pareto optimal solutions, the optimal balance point is found among multiple objectives such as minimizing system losses, maximizing charging efficiency, and maximizing load balance. A PWM modulation method based on a feedforward-feedback composite control strategy is designed, achieving rapid response to load changes through dynamic current adjustment, improving the dynamic performance of the system. Dead-time compensation and misalignment delay control technologies effectively reduce the switching losses of the power electronic converter, improving the system's energy conversion efficiency. Closed-loop tracking control and real-time output current prediction enable precise control of the charging process, ensuring its safety and reliability. Through the synergistic effect of multi-level optimization and control strategies, the charging pile system achieves efficient operation under different working conditions, improving the utilization rate of charging equipment.

[0180] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0181] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0182] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A control method for a charging pile energy storage connector with adaptive current distribution function, characterized in that, The method includes: Data acquisition and filtering are performed on each charging port of the charging pile to obtain a real-time operating parameter matrix for the port. Specifically, this includes: acquiring data from voltage sensors installed at each charging port of the charging pile to obtain raw port voltage data, and performing series high-pass and low-pass filtering on the raw port voltage data to obtain RMS port voltage data; acquiring data from current sensors installed at each charging port of the charging pile to obtain raw port current data, and performing series high-pass and low-pass filtering on the raw port current data to obtain RMS port current data; inputting the RMS port voltage data and the RMS port current data into a power analyzer for product operation and phase difference calculation to obtain port active power data and port reactive power data; performing trigonometric function calculation on the port active power data and the port reactive power data to obtain port power factor data; and performing matrix concatenation on the RMS port voltage data, the RMS port current data, the port active power data, the port reactive power data, and the port power factor data to obtain a real-time operating parameter matrix for the port. The process involves calculating port load power from the real-time operating parameter matrix of the ports, and then calculating load imbalance and charging efficiency based on the port load power. Specifically, this includes: extracting and classifying the active power data of the ports in the real-time operating parameter matrix to obtain the active power distribution data for each charging port; calculating the standard deviation and mean of the active power distribution data to obtain the port power fluctuation range; filtering and calculating the average value of the active power distribution data based on the port power fluctuation range to obtain the port load power; and then retrieving the maximum value and the minimum value of the port load power. The minimum and maximum port power of the system are obtained through minimum value retrieval. The load imbalance is obtained by dividing the difference between the maximum and minimum port power by the sum of the port load power and multiplying by 100%. The instantaneous charging efficiency of each charging port is obtained by dividing the output active power data and input apparent power data in the real-time operating parameter matrix of the port. The instantaneous charging efficiency is calculated by exponential weighted averaging to obtain the average port charging efficiency. The charging efficiency is then obtained by weighted summation based on the average port charging efficiency and the proportion of each port load power in the total system power. Based on the load imbalance and the charging efficiency, an adaptive allocation index for each charging port is calculated. Specifically, this includes: multiplying the load imbalance by a first weighting coefficient and performing a weighted calculation to obtain a load balance score; multiplying the charging efficiency by a second weighting coefficient and performing a weighted calculation to obtain a charging efficiency score; multiplying the power factor data in the port's real-time operating parameter matrix by a third weighting coefficient and performing a weighted calculation to obtain a power factor score; adding the load balance score, the charging efficiency score, and the power factor score to obtain a system operating status score; and comparing the port load power with the rated load of each charging port. The system performs a division operation to obtain a standardized load rate, and then divides the charging efficiency with the system's highest charging efficiency to obtain a relative efficiency value. It then divides the power factor score with the target power factor to obtain a correction coefficient, and performs a weighted calculation based on the standardized load rate, the relative efficiency value, and the correction coefficient with preset weighting coefficients to obtain an initial allocation index. Finally, it collects input and output voltage and current data from the charging pile's energy storage connector to obtain the connector's power transmission efficiency, and then corrects the initial allocation index based on this efficiency to obtain an adaptive allocation index that considers the connector's characteristics. The optimal current allocation value for each charging port is calculated based on the adaptive allocation index. Based on the optimal current allocation value, the power electronic converter of each charging port is subjected to PWM signal duty cycle modulation and dynamic current adjustment to obtain the real-time output current of each charging port.

2. The control method for a charging pile energy storage connector with adaptive current distribution function according to claim 1, characterized in that, The step of calculating the optimal current allocation value for each charging port based on the adaptive allocation index includes: The target allocation ratio is calculated for the standardized load rate, relative efficiency value and correction coefficient of each charging port to obtain the initial allocation weight of each charging port. The initial allocation weights are replicated to construct N independent subpopulations, each subpopulation containing M chromosome individuals, and each chromosome individual encodes the current allocation value of each charging port. The fitness of chromosome individuals in each subpopulation is evaluated to obtain individual fitness values, wherein the fitness evaluation is constructed based on the system loss minimization index, the charging efficiency maximization index, and the load balance maximization index. Based on the tournament selection strategy, high-quality individuals are selected from the chromosome individuals of each subpopulation to obtain parent individuals. Then, simulated binary crossover and polynomial mutation operations are performed on the parent individuals to obtain the offspring population. Every P generations, a migration operation is performed on the offspring population to obtain the migrated population. The migration operation uses a circular topology to exchange the best individuals between adjacent offspring populations. The individuals in the migrated population are verified by current constraints and power constraints to obtain a feasible solution set, wherein the current constraint ensures that the current at each port does not exceed the rated value, and the power constraint ensures that the total power of the system does not exceed the maximum output power of the energy storage device. The feasible solution set is sorted by non-dominated order and congestion distance is calculated to obtain the solution set on the Pareto optimal front. The optimal current allocation value is obtained by weighted summing and scoring based on the system loss, charging efficiency and load balance of each individual in the solution set on the Pareto optimal front.

3. The control method for a charging pile energy storage connector with adaptive current distribution function according to claim 1, characterized in that, The step of performing PWM signal duty cycle modulation and dynamic current adjustment on the power electronic converter of each charging port according to the optimal current allocation value to obtain the real-time output current of each charging port includes: The optimal current allocation value is input into the controller of each charging port to set the target value, thus obtaining the port control target value; The difference between the real-time current value collected by the current sensor of each charging port and the target value of the port control is calculated to obtain the current tracking error value; The current tracking error value is input into the PI controller for proportional-integral calculation to obtain the feedback control output value. The load characteristics are analyzed based on the rate of change of the port control target value to obtain the load dynamic response prediction value. The feedforward compensation calculation is performed on the predicted load dynamic response value to obtain the feedforward control output value; The feedback control output value and the feedforward control output value are weighted and superimposed to obtain the PWM modulation control signal; The PWM modulation control signal is subjected to carrier modulation calculation to obtain the IGBT switching transistor drive waveform of the power electronic converter. The IGBT switching transistor drive waveform is then input into the drive circuit of the power electronic converter for switching control to obtain the real-time output current of each charging port.

4. The control method for a charging pile energy storage connector with adaptive current distribution function according to claim 3, characterized in that, The process involves performing carrier modulation calculations on the PWM modulation control signal to obtain the IGBT switching transistor drive waveform of the power electronic converter, and then inputting the IGBT switching transistor drive waveform into the drive circuit of the power electronic converter for switching control to obtain the real-time output current of each charging port, including: Data extraction is performed on the PWM modulation control signal to obtain a control signal sampling sequence, and the amplitude of the control signal sampling sequence is normalized to obtain a normalized modulation wave. The frequency of the triangular carrier signal is multiplied to obtain a high-frequency carrier signal, and the phase synchronization of the normalized modulated wave and the high-frequency carrier signal is calculated to obtain a synchronous carrier modulated signal. The duty cycle of the synchronous carrier modulation signal is dynamically calculated based on the feedforward-feedback composite control strategy to obtain the duty cycle modulation waveform. Voltage clamping and overmodulation suppression are applied to the duty cycle modulation waveform to obtain an optimized modulation waveform. Dead time compensation is then applied to the optimized modulation waveform to obtain a compensated PWM drive signal. The compensated PWM drive signal is electrically isolated by the opto-isolation module of the driver to obtain an isolation drive voltage, and the isolation drive voltage is subjected to misalignment delay control to obtain a misalignment drive signal. The misaligned drive signal is clamped by the gate drive voltage and protected against overcurrent to obtain the IGBT switch drive waveform; Dynamic response analysis is performed based on the transfer function model of the duty cycle of the IGBT switching waveform and the output current of the charging port to obtain the predicted value of the output current. Then, closed-loop tracking control is performed on the predicted value of the output current to obtain the real-time output current of each charging port.

5. A charging pile energy storage connector system with adaptive current distribution function, characterized in that, The system is used to execute the charging pile energy storage connector control method with adaptive current distribution function as described in any one of claims 1-4, the system comprising: The data acquisition module is used to acquire and filter data from each charging port of the charging pile to obtain a real-time operating parameter matrix of the port. The power calculation module is used to calculate the port load power based on the real-time operating parameter matrix of the port, obtain the port load power, and calculate the load imbalance and charging efficiency based on the port load power. An index calculation module is used to calculate the adaptive allocation index of each charging port based on the load imbalance and the charging efficiency. The current calculation module is used to calculate the optimal current allocation value for each charging port based on the adaptive allocation index. The dynamic adjustment module is used to perform PWM signal duty cycle modulation and dynamic current adjustment on the power electronic converter of each charging port according to the optimal current allocation value, so as to obtain the real-time output current of each charging port.

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