A power supply and distribution intelligent control method and system for a charging pile

Abstract

This application discloses a power transmission and distribution intelligent control method and system for charging piles, comprising: first, acquiring the target power value of each charging port; second, performing an initial calculation using a proportional-integral-derivative (PID) algorithm to determine an initial dynamic power allocation scheme; simultaneously, employing a composite filtering technique including Kalman filtering and least mean square (LMS) adaptive filtering to process the input grid voltage to obtain stable input power; then, allocating power according to the initial scheme and monitoring the actual output power of each port in real time; finally, for the deviation between the actual output and the target, performing closed-loop feedback adjustment again using the PID algorithm to accurately and dynamically optimize the power allocation. This application, through a dual-closed-loop PID control strategy and composite adaptive filtering technology, effectively solves the problems of inaccurate dynamic power allocation and grid voltage fluctuations, significantly improving the control accuracy, operational stability, and charging efficiency of charging piles.

Description

Technical Field

[0001] This invention relates to the field of power electronics and automatic control technology, and in particular to a method and system for intelligent power transmission and distribution control of charging piles. Background Technology

[0002] As a core infrastructure for the development of the new energy vehicle industry, the intelligence and efficiency of electric vehicle charging stations directly impact user experience and grid stability. With the rapid growth in the number of electric vehicles, charging stations need to support simultaneous charging of multiple vehicles while ensuring efficient, stable, and low-energy power distribution. This places higher demands on intelligent power transmission and distribution control technology. Currently, charging station systems have significant shortcomings in multi-port output and input stability management. Many solutions rely on static configuration for power distribution, making it difficult to adapt to the dynamic charging needs of different vehicle models. This results in insufficient or excessive power at some ports, affecting charging efficiency. Furthermore, frequent grid voltage fluctuations and the lack of real-time monitoring and adjustment capabilities in existing systems make them prone to increased energy loss and reduced overall operating efficiency due to poor input power quality.

[0003] In scenarios supporting simultaneous charging of multiple electric vehicles, the core challenge lies in achieving independent power regulation at each port and dynamic stability of the input voltage. Dynamic power allocation is the primary technical bottleneck. Due to the significant differences in charging needs among different vehicle models—for example, a high-power electric vehicle requiring fast charging while a low-power model requires stable, low-speed charging—if the system cannot accurately match the output power of each port, it will lead to power waste or prolonged charging time. Furthermore, this problem amplifies the impact of input voltage fluctuations. When multiple ports are under high load, unstable grid voltage can cause power quality degradation or even damage to equipment. For instance, during peak hours, if a charging station is charging multiple vehicles simultaneously and the grid voltage suddenly drops, and the system cannot adjust the input voltage in time, it may lead to charging interruptions or reduced efficiency.

[0004] Therefore, how to achieve independent power regulation of multiple ports through embedded controllers and dynamic allocation algorithms, while combining adaptive filtering and voltage regulation technology to ensure stable input voltage, has become a key issue in intelligent power transmission and distribution control systems. Summary of the Invention

[0005] The purpose of this application is to provide a power transmission and distribution intelligent control method and system for charging piles, aiming to solve the technical problems mentioned in the background art, such as inaccurate dynamic power distribution and low charging efficiency caused by unstable input voltage.

[0006] To achieve the above objectives, this application provides the following technical solution:

[0007] On the one hand, this application provides a power transmission and distribution intelligent control method for charging piles, the method comprising:

[0008] Step S1: Obtain real-time charging demand data from multiple ports of the charging pile, parse the electric vehicle charging protocol connected to each port, extract the power demand parameters, and obtain the target power value for each port.

[0009] Step S2: Based on the target power value, the proportional-integral-derivative (PID) algorithm is used for the first time to calculate the power allocation ratio of each port. The proportional term is used to quickly respond to the target power, the integral term is used to correct the long-term steady-state error, and the derivative term is used to predict the power demand change trend and suppress oscillations, thereby determining the initial dynamic power allocation scheme.

[0010] Step S3: Real-time monitoring and processing of the input grid voltage is performed using composite adaptive filtering technology to obtain a stable input voltage value; the composite adaptive filtering technology includes: using the Kalman filter algorithm to preprocess the original voltage data to filter out Gaussian noise, and then using the least mean square (LMS) adaptive filtering algorithm to process the denoised data to track and suppress harmonics and frequency drift;

[0011] Step S4: If the grid voltage fluctuation is detected to exceed the preset threshold, the response parameters of the input voltage regulator module are adjusted through the embedded controller to obtain a stable power output;

[0012] Step S5: According to the initial dynamic power allocation scheme, the stable power is allocated to each port to obtain the actual output power of each port;

[0013] Step S6: Based on the deviation between the actual output power and the target power value of each port, the PID algorithm is used again to readjust the power allocation ratio, determine the optimized power allocation scheme, and form a closed-loop feedback control.

[0014] Step S7: Obtain the optimized power allocation scheme, and adjust the output power of each port in real time through the embedded controller to achieve high charging efficiency;

[0015] Step S8: If the charging efficiency of any port is detected to be lower than the preset efficiency threshold, repeat steps S6 and S7 until the final power distribution and voltage stabilization scheme is obtained.

[0016] Furthermore, in step S2, the input of the PID algorithm is the difference between the target power value of each port and the current total available power, and the output is the power adjustment amount that should be allocated to each port.

[0017] Furthermore, in step S3, the LMS adaptive filtering algorithm achieves adaptive processing by iteratively updating the filter weights w(n+1)=w(n) + μ·e(n)·x(n), where w(n) is the current weight, μ is the step size factor, e(n) is the error between the expected output and the actual output, and x(n) is the input signal.

[0018] Furthermore, in step S6, the input of the PID algorithm is the real-time deviation between the actual output power of each port and the target power value. Through iterative optimization, the deviation is reduced to less than a preset convergence threshold.

[0019] On the other hand, this application also provides a power transmission and distribution intelligent control system for charging piles, the system comprising:

[0020] The charging demand acquisition module is used to acquire real-time charging demand data from multiple ports of the charging pile, parse the electric vehicle charging protocols connected to each port, extract power demand parameters, and obtain the target power value of each port.

[0021] The power allocation calculation module is used to calculate the power allocation ratio of each port based on the target power value using a proportional-integral-derivative (PID) algorithm. The proportional term is used to quickly respond to the target power, the integral term is used to correct long-term steady-state errors, and the derivative term is used to predict the power demand change trend and suppress oscillations, thereby determining the initial dynamic power allocation scheme. For the deviation between the actual output power of each port and the target power value, the PID algorithm is used again to readjust the power allocation ratio, determine the optimized power allocation scheme, and form a closed-loop feedback control.

[0022] The voltage monitoring and processing module is used to monitor and process the input grid voltage in real time using composite adaptive filtering technology to obtain a stable input voltage value. The composite adaptive filtering technology includes: using a Kalman filter algorithm to preprocess the original voltage data to filter out Gaussian noise, and then using a least mean square (LMS) adaptive filtering algorithm to process the denoised data to track and suppress harmonics and frequency drift.

[0023] The voltage regulation parameter adjustment module is used to monitor the grid voltage fluctuations and adjust the response parameters of the input voltage regulation module through the embedded controller to obtain a stable power output.

[0024] The power distribution execution module is used to distribute the stable power to each port according to the initial dynamic power distribution scheme, so as to obtain the actual output power of each port;

[0025] The real-time control module is used to obtain the optimized power distribution scheme and adjust the output power of each port in real time through the embedded controller to achieve high charging efficiency.

[0026] The efficiency optimization module is used to monitor the charging efficiency of each port. When the charging efficiency of any port is lower than a preset threshold, the power allocation calculation module is triggered to re-execute the closed-loop feedback control until the final power allocation and voltage stabilization scheme is obtained.

[0027] This invention discloses a dynamic power allocation and voltage stability control method based on a multi-port charging pile. Addressing the problems of uneven power distribution across multiple ports and low charging efficiency caused by grid voltage fluctuations during electric vehicle charging, the method uses an embedded controller to extract power demand parameters from the charging protocols of each electric vehicle in real time, calculates the target power value, and dynamically adjusts the power allocation ratio of each port using a proportional-integral-derivative (PI-DE) algorithm to generate an optimized allocation scheme. Simultaneously, adaptive filtering technology monitors the grid voltage in real time, adjusting the voltage regulator module parameters when the voltage exceeds a preset threshold to ensure stable power output. For deviations between the actual output power and the target power, this invention further optimizes the allocation ratio using a PI-DE algorithm, combining real-time monitoring of charging status and voltage fluctuation data to dynamically regulate the output power, ultimately achieving high charging efficiency and a stable power supply.

[0028] This invention introduces a dual-closed-loop PID control strategy, enabling not only rapid initial power allocation based on the target power but also real-time, iterative correction based on the deviation between the actual output and the target. The synergistic effect of P, I, and D ensures both rapid dynamic response and stable static performance, significantly improving the accuracy of power allocation. The use of Kalman-LMS composite adaptive filtering technology, compared to single filtering methods, more effectively handles complex noise (Gaussian noise and harmonics) present in the grid voltage. By integrating dynamic power allocation and voltage stability control, this invention significantly improves the charging efficiency and system stability of multi-port charging piles, adapting to complex grid environments and diverse charging needs. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0030] Figure 1 This is an overall flowchart of a power transmission and distribution intelligent control method for charging piles provided in this application.

[0031] Figure 2 This is a functional module architecture diagram of a power transmission and distribution intelligent control system for charging piles provided in this application.

[0032] Figure 3 This is a detailed flowchart of power allocation based on the PID algorithm in the embodiments of this application.

[0033] Figure 4 This is a detailed flowchart of the power grid voltage processing based on composite adaptive filtering technology in the embodiments of this application.

[0034] Figure 5 This is a comparison chart of the effect of the method in this application and the traditional filtering method on the power grid voltage processing.

[0035] Figure 6 This is a schematic diagram showing the dynamic adjustment of the power of a certain charging port over time under PID closed-loop control in the method of this application. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0037] As a key infrastructure of the new energy vehicle industry, the intelligence and efficiency of electric vehicle charging stations directly impact user experience and grid stability. With the rapid growth in the number of electric vehicles, multi-port charging stations supporting simultaneous charging of multiple vehicles face significant challenges in power distribution and input stability management. Currently, the power transmission and distribution control technology for multi-port charging stations has the following main shortcomings:

[0038] 1. Regarding power allocation, many existing solutions rely on static configuration or simple polling mechanisms, which are difficult to adapt to the dynamic charging needs of different vehicle models. This often results in insufficient or excessive power at some ports, affecting overall charging efficiency. Although some improved solutions introduce dynamic adjustment, their control algorithms are relatively simple, with limited response speed and allocation accuracy, making it impossible to achieve optimal power scheduling under varying load conditions.

[0039] 2. Regarding input power quality management, grid voltage fluctuations are a common problem affecting charging stability and safety. Most existing systems employ passive threshold protection mechanisms, meaning they only implement derating or shutdown measures after detecting voltage exceeding limits. This response mode is lagging, and frequent voltage fluctuations or sags can easily lead to charging interruptions. Furthermore, the inability to effectively filter harmonic noise can increase equipment losses and reduce overall system operating efficiency.

[0040] Therefore, how to design a control algorithm that can accurately and quickly respond to and optimize dynamic power distribution, and combine it with advanced filtering technology to actively stabilize the input voltage, has become a key technical problem that urgently needs to be solved in the field of intelligent power transmission and distribution control.

[0041] Example 1

[0042] To address the aforementioned technical problems, this embodiment details the specific execution flow of a power transmission and distribution intelligent control method for charging piles. (Refer to...) Figure 1The method mainly includes the following steps:

[0043] Step S101: Obtain real-time charging demand data from multiple ports of the charging pile and obtain the target power value for each port.

[0044] When an electric vehicle connects to a charging station, the system's embedded main controller establishes communication with the vehicle's Battery Management System (BMS) via a charging communication interface (e.g., conforming to IEC 61851 or GB / T 27930 standards). By parsing BMS messages, the system can obtain key charging demand parameters, such as requested charging voltage, requested charging current, current battery state of charge (SOC), and total battery capacity. The target power value P_target can be determined by the product of the requested voltage and the requested current. Furthermore, the system assigns a priority to each port according to preset priority rules. For example, vehicles with an SOC below 20% or vehicles where the user selects 'urgent' mode can be set as high priority. These target power values ​​and priority information form the basis for subsequent power allocation.

[0045] Preferably, the preset priority rule maps ports of different priorities to a set of weight coefficients, Priority_Weight(i). For example, the weight of a high-priority port is 1.2, that of a normal-priority port is 1.0, and that of a low-priority port is 0.8. When calculating the initial power allocation, this weight coefficient will be used as a multiplier in the calculation, thereby tilting power towards high-priority ports.

[0046] Preferably, real-time charging data is obtained from the electric vehicle charging protocol via an embedded controller, the protocol content is parsed, and the power demand parameters of each port are extracted to obtain an initial power demand dataset. Protocol parsing technology is then used to process the initial power demand dataset, identify the charging protocol format of each port, extract the target power parameters, and determine the standardized power demand value.

[0047] Step S102: Based on the target power value, the initial dynamic power allocation scheme is calculated for the first time using the proportional-integral-derivative PID algorithm.

[0048] A proportional-integral-derivative (PI-DI) algorithm is used to obtain the target power value and calculate the initial power allocation ratio. If the target power value changes, the power allocation ratio is adjusted using the proportional algorithm to obtain the initial allocation result. Based on the initial allocation result, an integral algorithm is applied to correct the accumulated error and obtain the optimized power allocation ratio. If the optimized power allocation ratio deviates from a preset threshold, a derivative algorithm is used to predict the power change trend and obtain real-time adjustment parameters. By adjusting the parameters in real time, the port power control commands are updated to determine the dynamic power allocation scheme. Based on the dynamic power allocation scheme, the power output of each port is adjusted to obtain the actual power allocation result.

[0049] Specifically, after obtaining the target power values ​​P_target(i) for all ports, the system needs to perform an initial allocation based on the total available power P_total of the charging station. This step uses a PID controller to dynamically calculate the power ratio that should be allocated to each port.

[0050] Definition of deviation e(t): The deviation here can be defined as the difference between the total target power and the total available power, i.e., e(t) = ΣP_target(i) - P_total. If e(t)>0, it means that demand exceeds supply and needs to be reduced proportionally.

[0051] PID control output u(t): The PID controller calculates an overall power adjustment coefficient u(t) based on the deviation e(t).

[0052] Allocation ratio calculation: The initial allocated power P_initial(i) for each port is P_target(i) multiplied by an adjustment factor. This adjustment factor combines the port priority and the PID output u(t). For example, .

[0053] In this way, the PID algorithm ensures that when total power is limited, it can quickly and smoothly determine an initial allocation scheme that satisfies both constraints and priorities.

[0054] Step S103: The input grid voltage is monitored and processed in real time using composite adaptive filtering technology to obtain a stable input voltage value. This step is crucial for ensuring power quality; the specific process is detailed below. Figure 4 The system acquires input voltage signals from the power grid using sensors, and converts these analog signals to digital signals using analog-to-digital conversion (ADC) technology to obtain raw voltage data. If the raw voltage data contains noise, a Kalman filter algorithm is used to preprocess the data, resulting in denoised voltage data. Based on the denoised voltage data, an adaptive filtering algorithm is used to adjust the filtering parameters, obtaining optimized filtering coefficients. If the optimized filtering coefficients meet a preset convergence condition, the denoised voltage data is further filtered to obtain smoothed voltage data. Using the smoothed voltage data, a mean filtering algorithm is used to calculate the stable voltage value, resulting in stable voltage data. Based on the stable voltage data, if voltage fluctuations exceed a preset threshold, the filtering parameters are dynamically adjusted and the data is reprocessed to obtain adjusted stable voltage data. Finally, the adjusted stable voltage data is used to generate the final stable voltage value, determining the stability of the output voltage.

[0055] This step is crucial for ensuring power quality; it is an example, and the specific process can be found by referring to [reference needed]. Figure 4 .

[0056] 1. ADC Sampling: The system acquires the analog voltage signal of the input power grid in real time through a high-precision analog-to-digital converter (ADC, e.g., 16-bit, 10kHz sampling rate) and converts it into a digital sequence x(n). This sequence typically contains the fundamental frequency, Gaussian white noise, and harmonic distortion caused by nonlinear loads.

[0057] 2. Kalman Filtering: The digital sequence first enters a Kalman filter. A Kalman filter is a highly efficient recursive filter that can make optimal estimates of the next state of a dynamic system with uncertainty. In this scenario, it is used for preliminary denoising of the voltage signal, effectively filtering out Gaussian-distributed random noise and outputting a smoother voltage sequence x'(n).

[0058] 3. LMS Adaptive Filtering: x'(n) is then fed into the LMS adaptive filter. Unlike fixed-coefficient filters, the LMS filter automatically adjusts its internal filter coefficients (weights) based on the statistical characteristics of the input signal to minimize the error between the output signal and a desired signal (here, an ideal 50Hz sine wave). The filter weights w(0) are initialized to 0, and the step factor μ = 0.01. The LMS algorithm calculates the weights by iteratively updating w(n+1) = w(n) + μ·e(n)·x'(n), where e(n) = d(n) - y(n), d(n) is the desired output (ideal 50Hz sine wave), y(n) is the filter output, and x'(n) is the input signal. In each iteration, the error e(n) is calculated, and the weights are adjusted according to gradient descent to make the output approximate the ideal sine wave. To verify the filtering effect, the spectrum of the output signal is analyzed, and y(n) is decomposed using Fast Fourier Transform (FFT) to observe whether the harmonic components are attenuated. Its core is the weight update equation: w(n+1) = w(n) + μ·e(n)·x'(n). This property enables it to:

[0059] Tracking frequency drift: When the mains frequency shifts slightly from the standard 50Hz (e.g., to 49.8Hz), the LMS filter can adaptively adjust to maintain optimal filtering performance.

[0060] Harmonic suppression: It can effectively filter out harmonic components of specific frequencies such as the 3rd, 5th, and 7th harmonics, and output a voltage signal with a waveform that is closer to an ideal sine wave.

[0061] For example, if the amplitude of the 5th harmonic in the initial signal is 10V, after filtering it drops to 0.5V, and the total harmonic distortion (THD) decreases from 10% to below 1%, indicating that the filtering is effective. Next, the filtered signal y(n) is converted to an effective value using the formula Vrms=sqrt(1 / N·∑[y(n)]^2). Assuming N=200, a stable output of approximately 220.0V±0.2V is obtained. To ensure real-time performance, the algorithm runs on an embedded DSP (such as the TI C2000 series), with each iteration taking approximately 50μs, meeting the 10kHz sampling requirement. If the grid frequency drifts (e.g., to 49.8Hz), the adaptive filter adjusts the weights by tracking frequency changes to maintain output stability. Finally, the stable voltage value is transmitted to the subsequent control system via a digital interface for grid protection or load regulation, ensuring system reliability.

[0062] Step S104: If the grid voltage fluctuation exceeds the preset threshold, the response parameters of the input voltage regulator module are adjusted by the embedded controller to obtain a stable power output.

[0063] Voltage data from the power grid is acquired by a voltage sensor to determine if the voltage value exceeds a preset threshold, thus obtaining a fluctuation state. If the fluctuation state indicates that the voltage exceeds the preset threshold, the embedded controller calculates the necessary adjustment parameters for the voltage regulator module and obtains a control signal. The response parameters of the voltage regulator module are adjusted according to the control signal to obtain a stable power output. The voltage value of the power output is collected in real-time by a monitoring circuit to determine if the output remains within a preset range, thus obtaining a stable state. If the stable state indicates that the power output deviates from the preset range, an adaptive control algorithm is used to recalculate the adjustment parameters, obtaining an updated control signal. The voltage regulator module is adjusted again according to the updated control signal to obtain an optimized power output. The optimized power output data is stored by a data recording module to determine the stability of the system operation and obtain the operating status.

[0064] For example, after composite filtering, the system obtains a very stable and pure effective voltage value (Vrms). The system compares this Vrms with preset upper and lower threshold values ​​(e.g., 220V ± 7%). If Vrms exceeds the limit, it indicates that there is a real and continuous voltage deviation in the power grid that is not caused by noise. At this time, the embedded controller immediately calculates the adjustment parameters and sends a command to the active or passive voltage regulator module at the input (such as an automatic voltage regulator AVR) to adjust its response parameters (such as transformer taps or the duty cycle of the power electronic converter), thereby stabilizing the DC bus voltage supplied to the back-end power module at the rated value.

[0065] Steps S105 to S107: Closed-loop feedback PID power regulation. This is the second layer of closed-loop control to ensure accurate power output.

[0066] The system controls the power modules of each port to output according to the initial allocation scheme P_initial(i) (step S105).

[0067] 1. Obtain power demand data for each port. Collect load information through a real-time monitoring system to determine the power demand for each port. Based on the power demand data, use a preset allocation priority rule to determine the priority order of each port, obtaining a priority ranking result. If a high-priority port exists in the priority ranking result, allocate stable power to the high-priority port according to the dynamic power allocation scheme, determining the output power of the high-priority port. Based on the output power of the high-priority port, obtain the remaining power data, use a power balancing algorithm to determine the power allocation ratio for the remaining ports, obtaining the allocated power for each remaining port. During power allocation, if the load on a port exceeds a preset threshold, recalculate the allocated power for that port through a power adjustment mechanism to determine the adjusted output power.

[0068] 2. At the same time, the system uses current and voltage sensors to measure the actual output power P_actual(i) of each port in real time.

[0069] Secondary PID tuning, step S106: Based on the deviation between the actual output power and the target power value, a proportional-integral-derivative algorithm is used to readjust the power allocation ratio, determining the optimized power allocation scheme: For each port, the system activates an independent PID controller. At this time, the deviation e_i(t) = P_target(i) - P_actual(i). The goal of this PID controller is to make this deviation approach zero. (Refer to...) Figure 3 The specific process is as follows:

[0070] The proportional (P) term Kp * e_i(t) provides the most important control function. When the deviation is large, the output of the P term is also large, causing the actual power to quickly approach the target power.

[0071] The integral (I) term Ki * ∫e_i(t)dt is used to eliminate steady-state error. If, due to the inherent characteristics of the system, P_actual is always slightly lower than P_target, the P term may not be able to completely eliminate this small deviation. The I term will accumulate this deviation and continuously generate a correction until the deviation is zero.

[0072] The differential (D) term Kd * de_i(t) / dt serves both a "predictive" and "damping" function. It adjusts the output based on the rate of change of the deviation. If the deviation is decreasing rapidly, the D term generates a counterforce to prevent the power from "overshooting," thereby suppressing oscillations and stabilizing the system more quickly.

[0073] For example, to address the deviation between the actual output power and the target power value, a proportional-integral-derivative (PID) algorithm is used to readjust the power allocation ratio. The optimized power allocation scheme can be implemented through the following specific method. Assume that in a charging pile system, the target total power is 1000kW, the actual output power is 950kW, and the deviation is 50kW. First, real-time data is collected. The actual output power is obtained through a power sensor and compared with the target power to calculate the deviation e(t) = 1000 - 950 = 50kW. The PID algorithm formula is u(t) = Kp·e(t) + Ki·∫e(t)dt + Kd·de(t) / dt, where Kp, Ki, and Kd are the proportional, integral, and derivative coefficients, respectively. Let Kp = 0.5, Ki = 0.1, and Kd = 0.05. The proportional term is calculated as Kp·e(t) = 0.5 × 50 = 25kW, which is used to process the current deviation and quickly adjust the power allocation. The integral term uses historical deviation data, assuming the deviations for the previous 5 seconds are 50kW, 48kW, 52kW, 50kW, and 50kW respectively, with a sampling period of 1 second. ∫e(t)dt is approximately (50+48+52+50+50)=250kW·s, and the integral term is Ki·∫e(t)dt=0.1×250=25kW, eliminating steady-state error. The differential term uses the deviation change rate, assuming the deviation at the previous moment is 48kW, de(t) / dt=(50-48) / 1=2kW / s, and the differential term is Kd·de(t) / dt=0.05×2=0.1kW, predicting the deviation trend and suppressing oscillations. Combining the three terms, the control quantity u(t)=25+25+0.1=50.1kW, indicating that an additional 50.1kW of power allocation is needed. Assume the system contains three power generation units with an initial power allocation ratio of 4:3:3, namely 400kW, 300kW, and 300kW. After optimization, the ratio is adjusted, with the increased allocation being 50.1×0.4=20.04kW, 50.1×0.3=15.03kW, and 50.1×0.3=15.03kW, respectively. The new power is 420.04kW, 315.03kW, and 315.03kW. The new total power is verified to be 420.04+315.03+315.03=1050.1kW, with a deviation of 1050.1-1000=50.1kW. The system then enters the next round of PID iteration until the deviation is less than a set threshold (e.g., 5kW). Through real-time data acquisition, PID calculation, proportional allocation, and iterative optimization, a closed-loop control is formed to ensure accurate power allocation and stable system operation.

[0074] In this embodiment, the PID parameters can also be tuned offline using the Ziegler-Nichols step response method through simulation or experimentation to obtain better initial performance. In a more preferred embodiment, the system can also integrate a parameter self-tuning module, which uses online methods such as fuzzy control or neural networks to adaptively adjust Kp, Ki, and Kd according to real-time operating conditions (such as changes in load characteristics) to achieve optimal control performance under all operating conditions.

[0075] 4. Obtain the optimized power distribution scheme, and use the embedded controller to adjust the output power of each port in real time to achieve high charging efficiency.

[0076] Specifically, the charging status data of each port is acquired. Real-time current and voltage information is extracted from the charging device via an embedded controller to obtain the port charging status. Based on the port charging status, a preset power allocation model is used to analyze the power demand of each port and determine an initial power allocation scheme. If the initial power allocation scheme causes the charging efficiency of a certain port to fall below a preset threshold, the output power of that port is dynamically adjusted via the embedded controller to obtain an optimized power allocation scheme. Based on the optimized power allocation scheme, controller commands are generated and sent to each port via the embedded controller to obtain real-time controlled output power. The charging status of each port after real-time adjustment is acquired, and the trend of charging efficiency changes is analyzed to obtain efficiency evaluation data. Based on the efficiency evaluation data, if the charging efficiency of a certain port is still below the preset threshold, a support vector machine algorithm is used to iteratively optimize the power allocation scheme to obtain a further optimized power allocation scheme. Through system feedback, the execution effect of the optimized power allocation scheme is obtained, and the final controller commands are generated to achieve high charging efficiency.

[0077] For example, the output u_i(t) of the PID controller is used to finely adjust the control signal (such as PWM duty cycle) of the power module at that port to achieve real-time, closed-loop control of the output power (step S107).

[0078] Steps S108-S109: Monitor whether the charging efficiency is lower than the threshold. If the charging efficiency is lower than the preset threshold, the proportional-integral-derivative algorithm is re-executed through the embedded controller to obtain the final power distribution and voltage stabilization scheme.

[0079] Preferably, charging status data and grid voltage data at each port are collected in real time using sensors to obtain a raw dataset. Voltage fluctuation characteristics and charging status characteristics are extracted from the raw dataset to determine fluctuation feature values ​​and status parameters. If the fluctuation feature value exceeds a preset threshold, a linear regression algorithm is used to predict the voltage change trend, obtaining a prediction result. Based on the prediction result, the power allocation ratio of each port is calculated to determine an optimized power allocation scheme. The optimized power allocation scheme is used to adjust the power output of each port, obtaining adjusted charging status data. A voltage regulation control algorithm is used to correct the grid voltage in real time to obtain a stable voltage output value. The port management strategy is updated based on the stable voltage output value to determine the final charging efficiency and system stability.

[0080] If the charging efficiency is lower than a preset threshold, the current charging efficiency data is acquired through real-time monitoring to determine the efficiency deviation. Based on the efficiency deviation, a proportional-integral-derivative (PI-DE) algorithm is executed using an embedded controller to obtain a power allocation scheme. The control signal is adjusted using this power allocation scheme to obtain voltage stability parameters. If the voltage stability parameters exceed the safe range, the PI-DE parameters are recalculated using the embedded controller to determine a corrected control signal. Based on the corrected control signal, system feedback data is acquired to assess the stability of the power allocation. If the system feedback data indicates instability, the control signal is optimized using a Kalman filter algorithm to obtain the final power allocation scheme. The charging equipment output is adjusted using the final power allocation scheme to determine the improvement in charging efficiency.

[0081] For example, the system calculates the real-time charging efficiency of each port based on the actual output power and the actual charging power fed back by the vehicle's BMS. If the efficiency of a certain port (e.g., 83.3%) is found to be consistently lower than a preset threshold (e.g., 85%), the system determines that the current operating point may not be optimal. At this time, it will trigger a global PID parameter retuning or re-execute step S106 to attempt to find a more efficient operating point by fine-tuning the power allocation, thereby achieving the final energy optimization.

[0082] Example 2

[0083] This embodiment describes the supporting entity of the method in Embodiment 1—a smart power transmission and distribution control system for charging piles. (Refer to...) Figure 2 The system hardware consists of an embedded main controller, a power module, and sensors, and the software can be divided into the following functional modules:

[0084] Charging demand acquisition module: Acquires real-time charging demand data from multiple ports of the charging pile, parses the electric vehicle charging protocols connected to each port, extracts power demand parameters, and obtains the target power value for each port; for example, it is responsible for communicating with the vehicle BMS, parsing the protocol, and extracting the target power value.

[0085] Voltage monitoring and processing module: This module uses a composite adaptive filtering technique to monitor and process the input grid voltage in real time, obtaining a stable input voltage value. The composite adaptive filtering technique includes: preprocessing the raw voltage data using a Kalman filter algorithm to remove Gaussian noise, and then processing the denoised data using a Least Mean Square (LMS) adaptive filtering algorithm to track and suppress harmonics and frequency drift. Its core is a DSP or FPGA unit that runs the composite adaptive filtering algorithm. It receives the raw voltage data from the ADC, processes it through Kalman filtering and LMS adaptive filtering, and outputs a stable Vrms value.

[0086] Power Allocation Calculation Module: The core module for running the PID algorithm. It receives the target power and actual power of all ports, performs the initial allocation calculation in S102 and the closed-loop feedback adjustment calculation in S106, and outputs the target power or allocation ratio for each port.

[0087] Voltage regulation parameter adjustment module: If the detected grid voltage fluctuation exceeds a preset threshold, the embedded controller adjusts the response parameters of the input voltage regulation module to obtain a stable power output. For example, it receives the Vrms value from the voltage monitoring and processing module, determines whether it exceeds the limit, and sends an adjustment command to the hardware voltage regulation circuit.

[0088] Power distribution execution module: Based on the initial dynamic power distribution scheme, the stable power is distributed to each port to obtain the actual output power of each port.

[0089] Real-time control module: This module acquires the optimized power allocation scheme and uses an embedded controller to adjust the output power of each port in real time to achieve high charging efficiency. Located in the lowest driver layer of the embedded main controller, it receives fine-tuning instructions from the PID controllers of each port, generates high-frequency PWM signals, and precisely controls the switching state of the power module.

[0090] Efficiency optimization module: This module monitors the charging efficiency of each port. When the charging efficiency of any port falls below a preset threshold, it triggers the power allocation calculation module to re-execute closed-loop feedback control until a final power allocation and voltage stabilization scheme is obtained. It can be a background monitoring process that calculates real-time efficiency and triggers the power allocation calculation module for further optimization based on preset rules.

[0091] Preferably, when the efficiency optimization module determines that the current operating point is not optimal, it will trigger the power allocation calculation module. In one implementation, the triggered action is to adjust the target power value of one or more port PID controllers in small steps (for example, increasing the power of port A by 0.5kW and decreasing the power of port B by 0.5kW while keeping the total power unchanged), and observe the change in the overall efficiency after the adjustment. The system will then automatically seek the optimal point for global or local efficiency using algorithms such as hill climbing or particle swarm optimization.

[0092] Example 3

[0093] The technical solution of this application is applied to the following scenario: a 220V power grid containing noise and 5th harmonics supplies power to a charging station with a total power of 100kW. Two vehicles, A and B, are charging simultaneously at the station. Vehicle A has high priority and a target power of 70kW; vehicle B has low priority and a target power of 50kW. The total target power of 120kW exceeds the total available power of 100kW.

[0094] The workflow of this application:

[0095] 1. Voltage Stabilization: The "Voltage Monitoring and Processing Module" is activated, and the composite adaptive filter begins operation. The Kalman filter first filters out random noise, and then the LMS adaptive filter rapidly iteratively adjusts its weights based on its error compared to the ideal 50Hz sine wave, significantly attenuating the 5th harmonic component within tens of milliseconds and outputting a stable 220V voltage.

[0096] Figure 5 This is a waveform diagram comparing the effects of the composite adaptive filtering method described in this application and traditional filtering methods on grid voltage processing. For example... Figure 5 As shown, the horizontal axis represents time (in milliseconds), and the vertical axis represents voltage amplitude (in volts). Curve (a) is the original grid voltage, which includes random Gaussian noise (manifested as high-frequency spikes in the waveform) and significant 5th harmonic distortion (manifested as periodic fluctuations in the waveform), resulting in a high total harmonic distortion (THD). Curve (b) is the voltage after using a traditional mean filter. It can be seen that although it filters out some high-frequency noise, making the waveform relatively smooth, its ability to suppress the 5th harmonic is limited, and the periodic fluctuations in the waveform are still obvious. Curve (c) is the voltage after processing with a Kalman filter in the first stage of this application. It can be seen that Gaussian noise has been significantly suppressed. Curve (d) is the output voltage after the final Kalman-LMS composite adaptive filter in this application. This curve is very smooth and basically eliminates the periodic fluctuations caused by harmonics, very close to an ideal sine wave, and its THD is controlled at an extremely low level (below <1%). This comparison chart intuitively demonstrates the significant advantages of the composite filtering technology proposed in this application compared with existing technologies in suppressing complex noise and improving power quality.

[0097] 2. Initial Power Allocation: The "Power Allocation Calculation Module" performs the initial PID allocation. The total deviation e(t) = 120 - 100 = 20kW. The PID controller outputs a reduction factor. Because car A has a higher priority, its reduction ratio is smaller; car B has a lower priority, its reduction ratio is larger. The final possible allocation is: car A receives 65kW, and car B receives 35kW.

[0098] 3. Closed-loop power control: The "real-time control module" begins to perform independent PID closed-loop control on the two ports.

[0099] Port A: Target power 65kW, initial actual output may be 60kW. The PID controller detects a deviation of 5kW e_A(t). The P term immediately increases the output significantly, the I term begins to accumulate the deviation, and the D term monitors the rate of change. After several iterations, P_actual(A) quickly stabilizes at 65kW.

[0100] Port B: Similarly, its output power also quickly stabilizes at 35kW.

[0101] 4. Dynamic adjustment: At this time, the BMS information of vehicle A is updated. Due to the increase in battery temperature, its requested power (target power) drops to 50kW.

[0102] The total target power is now 50kW + 50kW = 100kW, which is equal to the total available power.

[0103] The initial allocation PID of the "Power Allocation Calculation Module" detected a deviation e(t) = 100 - 100 = 0, and stopped power reduction.

[0104] New targets have been issued: 50kW for vehicle A and 50kW for vehicle B.

[0105] The two-port closed-loop PID controllers restart, smoothly and precisely adjusting their respective output power to the new target value of 50kW. The entire process is dynamic, fast, and stable. Figure 6 This is a dynamic response curve diagram illustrating the closed-loop PID power control process described in this application. For example... Figure 6As shown, the horizontal axis represents time (in minutes), and the vertical axis represents power (in kilowatts). The dashed line represents the target power value of a charging port, and the solid line represents the actual output power of that port controlled by the method of this application. In the initial stage, the target power is 20kW. The actual power, after rapid adjustment by the PID controller, rises from 0kW in a very short time and quickly stabilizes at 20kW after a slight overshoot, achieving precise tracking of the target value. At the 5th minute, a step change in the target power is simulated, with the target power suddenly dropping from 20kW to 15kW. As can be seen from the figure, the PID controller of this application can respond to this change immediately, the actual output power drops rapidly, and after a brief and smooth adjustment process, it stabilizes again precisely at the new target value of 15kW. The entire process is characterized by rapid response, small overshoot, and no sustained oscillation, fully demonstrating the high speed, stability, and accuracy of the closed-loop PID control strategy of this application in dynamic power regulation.

[0106] Through this scenario, the method of this application demonstrates its comprehensive ability to stabilize power quality under complex power grid conditions and achieve accurate, fast, and stable power distribution under dynamic and multi-constrained load demands.

[0107] In summary, this application, by introducing a dual-closed-loop PID control strategy, can not only perform rapid initial allocation based on the target power, but also make real-time, iterative corrections based on the deviation between the actual output and the target. The synergistic effect of P, I, and D ensures that the system has both fast dynamic response and stable static performance, significantly improving the accuracy of power allocation.

[0108] Secondly, this application employs a Kalman-LMS composite adaptive filtering technique, which, compared to single filtering methods, can more effectively handle complex noise (Gaussian noise and harmonics) present in the grid voltage. In particular, the adaptive characteristics of the LMS algorithm enable the system to actively track and suppress changes in the grid environment, outputting a purer and more stable voltage, thereby ensuring the continuity and efficiency of the charging process.

[0109] Finally, this application uses charging efficiency as the ultimate optimization target, triggering a readjustment of the PID control to form a top-level closed loop oriented towards efficiency. This ensures that the system not only achieves stability in terms of power and voltage, but also optimizes the final energy conversion efficiency, resulting in higher economic efficiency and energy-saving effects.

[0110] 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 power transmission and distribution intelligent control method for charging piles, characterized in that, The method includes: Obtain the target power value of each charging port and the total available power of the charging pile; Based on the difference between the sum of the target power of all charging ports and the total available power of the charging pile, the overall power adjustment coefficient is calculated for the first time using the proportional-integral-derivative PID algorithm. Combined with the target power value of each charging port and the preset priority weight of each charging port, the power allocation ratio of each charging port is calculated to determine the initial dynamic power allocation scheme. The raw voltage data of the input grid voltage is collected. First, the raw voltage data is preprocessed using the Kalman filter algorithm to filter out Gaussian noise. Then, the data after Kalman filtering is processed using the Least Mean Square (LMS) adaptive filter algorithm to track and suppress harmonics and frequency drift, so as to obtain a stable input voltage value. According to the initial dynamic power allocation scheme, the stable electrical energy generated based on the stable input voltage value is allocated to each charging port to obtain the actual output power of each charging port; For each charging port, an independent PID controller is instantiated, and the real-time deviation between the target power value and the actual output power of the charging port is used as the input of the corresponding independent PID controller. The power allocation ratio of the charging port is readjusted to form a port-level closed-loop feedback control, and the optimized power allocation scheme is determined and executed. The charging efficiency of each charging port is monitored. When the charging efficiency of any charging port is lower than a preset threshold, the port-level closed-loop feedback control is triggered to be re-executed.

2. The method according to claim 1, characterized in that, The steps for processing the Kalman-filtered data using the Least Mean Square (LMS) adaptive filtering algorithm include: Adaptive processing is achieved by iteratively updating the filter weights w(n+1)=w(n)+μ·e(n)·x'(n), where w(n) is the current weight, μ is the step size factor, e(n) is the error between the expected output and the actual output, and x'(n) is the input signal.

3. The method according to claim 1, characterized in that, In the step of instantiating an independent PID controller for each charging port, the independent PID controller iteratively optimizes the power allocation ratio of the charging port until the real-time deviation between the target power value and the actual output power of the charging port is less than a preset convergence threshold.

4. A power transmission and distribution intelligent control system for charging piles, used to implement the method described in any one of claims 1-3, characterized in that, The system includes: An embedded controller; A voltage monitoring and processing module is configured to collect raw voltage data of the input grid voltage. First, the raw voltage data is preprocessed using a Kalman filter algorithm to filter out Gaussian noise. Then, the data after Kalman filtering is processed using a least mean square (LMS) adaptive filter algorithm to track and suppress harmonics and frequency drift, thereby obtaining a stable input voltage value. A power distribution calculation module, configured to execute a proportional-integral-derivative PID algorithm under the control of the embedded controller, is used for: a) Based on the difference between the total target power of all charging ports and the total available power of the charging pile, the PID algorithm is used for the first time to calculate the overall power adjustment coefficient. Combined with the target power value of each charging port and the preset priority weight of each charging port, the power allocation ratio of each charging port is calculated to determine the initial dynamic power allocation scheme. b) For each charging port, instantiate an independent PID controller and use the real-time deviation between the target power value and the actual output power of the charging port as the input of the corresponding independent PID controller to readjust the power allocation ratio of the charging port, form a port-level closed-loop feedback control, and determine the optimized power allocation scheme. A power execution and regulation module is used to distribute and regulate the stable electrical energy generated based on the stable input voltage value to each charging port according to the scheme determined by the power distribution calculation module. An efficiency optimization module is configured to monitor the charging efficiency of each charging port. When the charging efficiency of any charging port is lower than a preset threshold, the power allocation calculation module is triggered to re-execute the port-level closed-loop feedback control.

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