DC charging pile voltage stability optimization method and system

Abstract

The invention relates to the technical field of direct current charging piles, and particularly discloses a direct current charging pile voltage stability optimization method and system, and the method comprises the steps: collecting multi-dimensional working condition information, and carrying out the Kalman and moving average combination filtering noise reduction; dividing working conditions into four classes through a decision tree model, presetting a differential voltage stability threshold and performing real-time judgment; based on a fuzzy control rule, dynamically adjusting PID parameters in combination with working conditions; dual optimization is realized through a switchable filtering and impedance matching unit; and constructing a closed loop, iteratively adjusting parameters every 10ms, and recording an optimal combination to form a database. According to the method, the working condition is accurately identified, through self-adaptive PID control and output side dual optimization, the voltage fluctuation suppression capability is improved, the real-time performance and long-term stability of adjustment are considered, secondary fluctuation is reduced, the charging safety and the service life of the battery are guaranteed, and the problem that dynamic working condition adaptation is insufficient in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of DC charging pile technology, specifically to a method and system for optimizing the voltage stability of DC charging piles. Background Technology

[0002] With the rapid development of the new energy vehicle industry, DC charging piles, as core energy replenishment equipment, directly affect the lifespan, charging efficiency, and safety of power batteries through their charging performance and voltage stability. Currently, DC charging piles face various complex operating conditions during actual operation, leading to insufficient output voltage stability. The main problems are as follows:

[0003] On the one hand, the input power grid is prone to voltage fluctuations, frequency shifts, and harmonic pollution, which are directly transmitted to the charging pile's output, causing voltage fluctuations. On the other hand, during the charging process, the SOC (State of Charge), temperature, and equivalent internal resistance of the power battery dynamically change, especially under conditions of sudden changes in internal resistance, which can easily lead to drastic fluctuations in output voltage. Simultaneously, the charging pile's insufficient adaptability of its control parameters during power regulation, along with the fixed design of its filtering and impedance matching structures, prevents dynamic adjustments for different operating conditions, further exacerbating the voltage instability problem.

[0004] In existing technologies, optimization schemes for DC charging pile voltage stability mostly employ fixed PID control parameters combined with a single filter structure, lacking the ability to accurately identify and adaptively adjust to multi-dimensional operating conditions. For example, some schemes only improve voltage stability under steady-state charging conditions by optimizing PID parameters. Under dynamic conditions such as grid fluctuations and sudden changes in battery impedance, the adjustment response lags, failing to quickly suppress voltage fluctuations. Other schemes use fixed filter branches, which are difficult to adapt to voltage ripples of different frequencies, resulting in limited filtering effects. Furthermore, existing schemes lack a closed-loop iterative optimization mechanism, failing to continuously optimize parameters according to actual charging scenarios. This makes it difficult to achieve voltage stability optimization effects that cover various operating conditions, and thus cannot meet the stringent requirements of power batteries for charging voltage accuracy.

[0005] Therefore, there is an urgent need for a DC charging pile voltage stability optimization method and system that can accurately identify multi-dimensional operating conditions, dynamically adjust control and filtering parameters, and continuously optimize through closed-loop iteration, in order to solve the problems of weak voltage fluctuation suppression capability, narrow range of applicable operating conditions, and limited optimization effect in existing technologies. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a method and system for optimizing the voltage stability of DC charging piles. By collecting multi-dimensional operating conditions, classifying dynamic operating conditions, adaptive PID control, dual optimization on the output side, and closed-loop iterative adjustment, the method achieves accurate and stable control of the output voltage of DC charging piles under various operating conditions, thereby improving charging safety and the service life of power batteries.

[0007] To solve the above-mentioned technical problems, the technical solution provided by the present invention is: a method for optimizing the voltage stability of a DC charging pile, comprising the following steps:

[0008] S1. Multi-dimensional operating condition information acquisition: Construct a multi-sensor acquisition module to collect multi-dimensional operating condition information of DC charging piles in real time, including input grid parameters, output electrical parameters, and power battery status parameters; use digital filtering algorithms to perform noise reduction processing on the collected operating condition information data and remove abnormal interference signals;

[0009] S2. Dynamic Operating Condition Classification and Threshold Determination: Based on the processed multi-dimensional operating condition information, an operating condition classification model is constructed, dividing the charging process into four categories: grid fluctuation operating condition, battery impedance change operating condition, power regulation operating condition, and steady-state charging operating condition. The voltage stability threshold range for each operating condition is preset, where the output voltage fluctuation is allowed to be ≤±0.5% and the ripple coefficient is ≤1% under steady-state charging operating condition; the output voltage fluctuation is allowed to be ≤±1% and the ripple coefficient is ≤1.5% under dynamic operating condition. The operating condition identification algorithm determines the current operating condition and whether it exceeds the voltage stability threshold in real time. If it exceeds the threshold, the optimization adjustment process is triggered.

[0010] S3. Adaptive PID control parameter dynamic adjustment: Based on the working condition classification results, an adaptive PID control model is constructed, and the PID parameters are dynamically adjusted for different working conditions. The PID parameters include the proportional coefficient Kp, the integral coefficient Ki, and the derivative coefficient Kd.

[0011] S4. Output-side dynamic filtering and impedance matching adjustment: A switchable filtering unit and an impedance matching unit are set at the output end of the charging pile, which, together with adaptive PID control, achieve dual optimization.

[0012] S5. Closed-loop feedback and iterative optimization: The deviation between the actual output voltage and the preset stable threshold is used as a feedback signal and fed back to the adaptive PID control model and impedance matching unit in real time to form a closed-loop control loop. The optimization effect is evaluated every 10ms. If the output voltage still exceeds the threshold, the parameters are iteratively adjusted until the voltage stabilizes within the preset range. At the same time, the optimal parameter combination under each operating condition is recorded to build a parameter database.

[0013] Furthermore, the input grid parameters include grid voltage, grid frequency, and grid harmonic content; the output electrical parameters include output voltage, output current, voltage ripple coefficient, and output impedance; and the power battery state parameters include battery SOC, battery temperature, battery equivalent internal resistance, and battery terminal voltage requirement.

[0014] Furthermore, in step S1, the digital filtering algorithm adopts a combination of Kalman filtering and moving average filtering. First, random interference is eliminated by Kalman filtering, and then steady-state noise is eliminated by moving average filtering.

[0015] Furthermore, in step S2, the operating condition classification model is constructed using a decision tree algorithm, with grid voltage fluctuation, battery equivalent internal resistance change rate, and output power adjustment amplitude as feature parameters.

[0016] Furthermore, in step S3, the dynamic adjustment of the PID parameters adopts fuzzy control rules, taking voltage deviation and deviation change rate as input quantities, and combining them with the operating condition type, outputting the adjustment amount of the PID parameters through fuzzy inference to perform continuous and smooth adjustment of the parameters.

[0017] Furthermore, in step S3, the dynamic adjustment strategy of the PID parameters is as follows: under grid fluctuation conditions, the derivative coefficient Kd is increased first, while the proportional coefficient Kp is appropriately reduced; under battery impedance change conditions, the proportional coefficient Kp and integral coefficient Ki are increased; under power regulation conditions, an intermediate value Kp, a small Ki, and a medium Kd are used; under steady-state charging conditions, a small Kp, a medium Ki, and a very small Kd are used.

[0018] Furthermore, in step S4, the switchable filter unit includes a high-frequency filter branch and a low-frequency filter branch, and automatically switches the filter branch based on the collected voltage ripple frequency: the high-frequency filter branch is activated when the ripple frequency is ≥1kHz, and the low-frequency filter branch is activated when the ripple frequency is <1kHz, while the filter capacitor and inductor parameters are dynamically adjusted.

[0019] Furthermore, in step S4, the impedance matching unit detects the output impedance and the battery's equivalent internal resistance in real time through a controllable reactor and a variable resistor network, and adjusts its own impedance value so that the ratio of the output impedance to the battery's equivalent internal resistance is maintained between 0.8 and 1.2.

[0020] The present invention also provides a DC charging pile voltage stability optimization system, including a multi-dimensional operating condition information acquisition module, a dynamic operating condition classification and threshold determination module, an adaptive PID control module, an output-side dynamic optimization module, and a closed-loop feedback iteration module;

[0021] The multi-dimensional operating condition information acquisition module collects input power grid parameters, output electrical parameters, and power battery status parameters in real time through a multi-sensor array. It uses an algorithm combining Kalman filtering and moving average filtering to reduce noise and eliminate interference signals.

[0022] The dynamic operating condition classification and threshold determination module constructs a model based on noise reduction data using a decision tree algorithm. It uses grid voltage fluctuation, battery equivalent internal resistance change rate, and output power adjustment amplitude as features to classify the charging process into four types of operating conditions, preset corresponding voltage stability thresholds, and determine in real time whether the operating condition and voltage exceed the limit. If the limit is exceeded, optimization is triggered.

[0023] The adaptive PID control module is based on the operating condition classification results, adopts fuzzy control rules, takes voltage deviation and deviation change rate as input, and dynamically and smoothly adjusts PID parameters according to the operating conditions.

[0024] The output-side dynamic optimization module includes a switchable filter unit and an impedance matching unit. The switchable filter unit automatically switches between high and low frequency branches and adjusts parameters according to the voltage ripple frequency. The impedance matching unit matches the output impedance and the battery's equivalent internal resistance in real time, keeping their ratio between 0.8 and 1.2, thus forming a dual optimization with the adaptive PID control.

[0025] The closed-loop feedback iteration module constructs a closed-loop circuit using the deviation between the actual output voltage and the preset threshold as the feedback signal. It evaluates the optimization effect every 10ms, iteratively adjusts the parameters until the voltage stabilizes, and records the optimal parameter combination to build a database.

[0026] The advantages of this invention compared to the prior art are:

[0027] This invention achieves accurate identification of four typical charging conditions through multi-dimensional operating condition information collection and decision tree classification model. Combined with differentiated voltage stability thresholds, it provides a targeted basis for subsequent adaptive adjustment, solving the problem of insufficient adaptation of existing technologies to dynamic operating conditions.

[0028] This invention employs an adaptive PID parameter adjustment strategy that combines fuzzy control rules with operating condition classification to achieve continuous and smooth optimization of PID parameters under different operating conditions. Combined with dual optimization of dynamic filtering and impedance matching on the output side, it significantly improves the voltage fluctuation suppression capability and meets the stringent charging requirements of power batteries.

[0029] This invention utilizes a closed-loop feedback iteration mechanism and a parameter database to evaluate and iteratively adjust the optimization effect every 10ms in real time. It also enables the reuse of optimal parameters under similar operating conditions, balancing real-time adjustment with long-term stability, reducing secondary voltage fluctuations, and improving charging safety and the lifespan of the power battery. Attached Figure Description

[0030] Figure 1This is a flowchart of a DC charging pile voltage stability optimization method according to the present invention.

[0031] Figure 2 This is a system block diagram of a DC charging pile voltage stability optimization system according to the present invention. Detailed Implementation

[0032] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0033] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0034] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0035] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0036] The following detailed description, in conjunction with the accompanying drawings, provides a method and system for optimizing the voltage stability of a DC charging pile according to the present invention.

[0037] Combined with appendix Figure 1-2 The specific implementation process of the DC charging pile voltage stability optimization method and system of the present invention is as follows:

[0038] A method for optimizing the voltage stability of a DC charging pile includes the following steps:

[0039] S1. Multi-dimensional working condition information collection

[0040] A multi-sensor acquisition module is constructed, which includes power grid parameter sensors, output electrical parameter sensors, and battery status sensors. This module collects multi-dimensional operating condition information of the DC charging pile in real time. The input power grid parameters include power grid voltage, power grid frequency, and power grid harmonic content; the output electrical parameters include output voltage, output current, voltage ripple coefficient, and output impedance; and the power battery status parameters include battery SOC, battery temperature, battery equivalent internal resistance, and battery terminal voltage requirements.

[0041] The collected operating condition data is denoised using a digital filtering algorithm to remove abnormal interference signals. The algorithm combines Kalman filtering and moving average filtering. First, Kalman filtering, based on the state and observation equations, accurately removes random interference from the collected data. Then, moving average filtering smooths the filtered data, eliminating steady-state noise and ensuring the accuracy and reliability of the operating condition data. This provides precise data support for subsequent operating condition classification and parameter adjustment.

[0042] S2. Dynamic Operating Condition Classification and Threshold Determination

[0043] Based on the multi-dimensional operating condition information processed in step S1, an operating condition classification model is constructed. The operating condition classification model is constructed using a decision tree algorithm, with grid voltage fluctuation, battery equivalent internal resistance change rate, and output power adjustment amplitude as feature parameters. The decision tree branches the feature parameters to make threshold judgments, and the charging process is divided into four categories: grid fluctuation operating condition, battery impedance change condition, power adjustment condition, and steady-state charging condition.

[0044] The system presets voltage stability threshold ranges for various operating conditions. Under steady-state charging conditions, the allowable output voltage fluctuation is ≤ ±0.5%, and the ripple coefficient is ≤ 1%. Under dynamic conditions (including grid fluctuation conditions, battery impedance change conditions, and power regulation conditions), the allowable output voltage fluctuation is ≤ ±1%, and the ripple coefficient is ≤ 1.5%. A condition identification algorithm extracts the characteristic parameters of the current operating condition in real time and compares them with the characteristic thresholds in the condition classification model to determine whether the current operating condition and output voltage exceed the corresponding voltage stability threshold. If they do, an optimization adjustment process is triggered, proceeding to subsequent parameter adjustment and output optimization steps.

[0045] S3, Dynamic adjustment of adaptive PID control parameters

[0046] Based on the operating condition classification results in step S2, an adaptive PID control model is constructed. The PID parameters are dynamically adjusted for different operating conditions. The PID parameters include the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd. The dynamic adjustment of the PID parameters employs fuzzy control rules. Voltage deviation and the rate of change of deviation are used as inputs. A fuzzy rule base is constructed based on the current operating condition type. Fuzzy inference is used to output the adjustment amount of the PID parameters, achieving continuous and smooth parameter adjustment and avoiding secondary voltage fluctuations caused by sudden parameter changes.

[0047] The specific adjustment strategies are as follows: Under grid fluctuation conditions, prioritize increasing the differential coefficient Kd to enhance the system's anti-interference capability and quickly suppress voltage deviations caused by grid fluctuations, while appropriately reducing the proportional coefficient Kp to avoid system overshoot; Under battery impedance change conditions, increase the proportional coefficient Kp to accelerate the response speed and increase the integral coefficient Ki to eliminate static deviations and ensure that the voltage quickly returns to the stable range; Under power regulation conditions, use an intermediate value Kp, a small Ki, and a medium Kd to balance response speed and stability, and avoid large voltage fluctuations during power regulation; Under steady-state charging conditions, use a small Kp, a medium Ki, and a very small Kd to reduce system oscillations and maintain long-term voltage stability.

[0048] S4. Output-side dynamic filtering and impedance matching adjustment

[0049] A switchable filter unit and an impedance matching unit are set at the output end of the charging pile. Combined with the adaptive PID control in step S3, dual optimization is achieved to further improve voltage stability.

[0050] The switchable filter unit includes a high-frequency filter branch and a low-frequency filter branch. The filter branch is automatically switched based on the voltage ripple frequency collected in step S1: when the ripple frequency is ≥1kHz, the high-frequency filter branch is activated, and when the ripple frequency is <1kHz, the low-frequency filter branch is activated. At the same time, the filter capacitor and inductor parameters in the filter branch are dynamically adjusted by a controllable switching element to achieve accurate filtering of ripple at different frequencies and reduce the ripple coefficient to below the preset threshold.

[0051] The impedance matching unit uses a controllable reactor and a variable resistor network to detect the output impedance of the charging pile and the equivalent internal resistance of the power battery in real time. Based on the detection results, it automatically adjusts its own impedance value to keep the ratio of the output impedance to the battery's equivalent internal resistance between 0.8 and 1.2, thereby achieving precise impedance matching, reducing voltage reflection and fluctuations caused by impedance mismatch, improving charging efficiency, and ensuring voltage stability.

[0052] S5, Closed-Loop Feedback and Iterative Optimization

[0053] The deviation between the actual voltage at the charging pile output and the corresponding operating condition voltage stability threshold preset in step S2 is used as a feedback signal, which is fed back to the adaptive PID control model and impedance matching unit in real time, forming a closed-loop control loop of "acquisition-classification-adjustment-optimization-feedback". The optimization effect is evaluated every 10ms by comparing the actual output voltage fluctuation and ripple coefficient with the preset threshold to determine whether the voltage is stable within the preset range.

[0054] If the output voltage still exceeds the threshold, the PID parameters, filter parameters, and impedance matching parameters are iteratively adjusted based on the feedback deviation until the voltage stabilizes within the preset range. If the voltage has stabilized, the current operating condition type, optimized PID parameters, filter branch parameters, and impedance matching values ​​are recorded to build an optimal parameter database. During subsequent charging, when the same operating condition is identified, the optimal parameters in the database can be directly called, shortening the optimization and adjustment time and improving the real-time performance of voltage stability.

[0055] This invention also provides a DC charging pile voltage stability optimization system to implement the above-mentioned DC charging pile voltage stability optimization method. The system includes a multi-dimensional operating condition information acquisition module, a dynamic operating condition classification and threshold determination module, an adaptive PID control module, an output-side dynamic optimization module, and a closed-loop feedback iteration module. The modules work together to achieve full-process optimization of voltage stability.

[0056] The multi-dimensional operating condition information acquisition module uses a multi-sensor array (including a grid voltage sensor, frequency sensor, harmonic detector, output voltage / current sensor, ripple detector, SOC sensor, temperature sensor, and internal resistance detector) to collect input grid parameters, output electrical parameters, and power battery status parameters in real time. The built-in filtering unit uses an algorithm combining Kalman filtering and moving average filtering to denoise the acquired data, remove random interference and steady-state noise, and output accurate operating condition data to the dynamic operating condition classification and threshold determination module.

[0057] The dynamic operating condition classification and threshold determination module is based on the noise-reduced data output by the multi-dimensional operating condition information acquisition module. It constructs an operating condition classification model through a decision tree algorithm, using grid voltage fluctuation, battery equivalent internal resistance change rate, and output power adjustment amplitude as core feature parameters. The charging process is divided into four categories: grid fluctuation operating condition, battery impedance change condition, power adjustment condition, and steady-state charging condition. The module presets the voltage stability threshold range corresponding to each operating condition. Through real-time operating condition identification and threshold comparison, it determines whether the current operating condition and output voltage exceed the limits. If the limits are exceeded, an optimization trigger signal is sent to the adaptive PID control module and the output-side dynamic optimization module.

[0058] The adaptive PID control module receives operating condition information and optimization trigger signals sent by the dynamic operating condition classification and threshold determination module. Based on fuzzy control rules, it constructs an adaptive adjustment model, using voltage deviation and deviation change rate as inputs. Combined with the current operating condition type, it outputs PID parameter adjustment amounts through fuzzy inference, dynamically and smoothly adjusts the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd, and outputs control signals to the charging pile main circuit to achieve initial voltage regulation.

[0059] The output-side dynamic optimization module includes a switchable filter unit and an impedance matching unit, forming a dual optimization mechanism with the adaptive PID control module. The switchable filter unit automatically switches between high-frequency and low-frequency filter branches based on the voltage ripple frequency acquired by the multi-dimensional operating condition information acquisition module, and dynamically adjusts the parameters of the filter capacitor and inductor to accurately filter out ripple at different frequencies. The impedance matching unit, through a controllable reactor and a variable resistor network, detects the output impedance and the battery's equivalent internal resistance in real time, adjusting its own impedance value to maintain the ratio between 0.8 and 1.2, achieving impedance matching optimization and further suppressing voltage fluctuations.

[0060] The closed-loop feedback iteration module uses the deviation between the actual output voltage and the preset threshold as the feedback signal, and feeds it back to the adaptive PID control module and the output-side dynamic optimization module in real time to construct a closed-loop control loop. The optimization effect is evaluated every 10ms. If the voltage does not reach the stable threshold, the parameters are iteratively adjusted until the voltage stabilizes. At the same time, the optimal parameter combination under each operating condition is recorded to build a parameter database, providing a basis for rapid optimization under similar operating conditions in the future, and improving the system optimization efficiency and stability.

[0061] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for optimizing the voltage stability of a DC charging pile, characterized in that, Includes the following steps: S1. Real-time acquisition of multi-dimensional operating condition information of DC charging piles, including input grid parameters, output electrical parameters, and power battery status parameters; noise reduction processing of the acquired operating condition information data is performed through digital filtering algorithm to eliminate abnormal interference signals; S2. Based on the processed multi-dimensional operating condition information, an operating condition classification model is constructed to divide the charging process into four categories: grid fluctuation operating condition, battery impedance change operation condition, power regulation operating condition, and steady-state charging operating condition. The voltage stability threshold range is preset for each operating condition. Under steady-state charging conditions, the allowable output voltage fluctuation is ≤ ±0.5% and the ripple coefficient is ≤ 1%. Under dynamic conditions, the allowable output voltage fluctuation is ≤ ±1% and the ripple coefficient is ≤ 1.5%. The operating condition recognition algorithm determines the current operating condition and whether it exceeds the voltage stability threshold in real time. If it does, the optimization adjustment process is triggered. S3. Based on the working condition classification results, construct an adaptive PID control model and dynamically adjust the PID parameters for different working conditions. The PID parameters include proportional coefficient, integral coefficient and derivative coefficient. S4. Set a switchable filter unit and an impedance matching unit at the output end of the charging pile to achieve dual optimization in conjunction with adaptive PID control; S5. The deviation between the actual output voltage and the preset stable threshold is used as a feedback signal and fed back to the adaptive PID control model and impedance matching unit in real time to form a closed-loop control loop. The optimization effect is evaluated every 10ms. If the output voltage still exceeds the threshold, the parameters are iteratively adjusted until the voltage stabilizes within the preset range. At the same time, the optimal parameter combination under each working condition is recorded to build a parameter database.

2. The method for optimizing the voltage stability of a DC charging pile according to claim 1, characterized in that: The input grid parameters include grid voltage, grid frequency, and grid harmonic content; the output electrical parameters include output voltage, output current, voltage ripple coefficient, and output impedance; the power battery status parameters include battery SOC, battery temperature, battery equivalent internal resistance, and battery terminal voltage requirements.

3. The method for optimizing the voltage stability of a DC charging pile according to claim 2, characterized in that: In step S1, the digital filtering algorithm uses a combination of Kalman filtering and moving average filtering. First, random interference is eliminated by Kalman filtering, and then steady-state noise is eliminated by moving average filtering.

4. The method for optimizing the voltage stability of a DC charging pile according to claim 3, characterized in that: In step S2, the operating condition classification model is constructed using a decision tree algorithm, with grid voltage fluctuation, battery equivalent internal resistance change rate, and output power adjustment amplitude as feature parameters.

5. The method for optimizing the voltage stability of a DC charging pile according to claim 4, characterized in that: In step S3, the dynamic adjustment of the PID parameters adopts fuzzy control rules. The voltage deviation and the rate of change of deviation are used as input quantities. Combined with the operating condition type, the adjustment amount of the PID parameters is output through fuzzy inference to perform continuous and smooth adjustment of the parameters.

6. The method for optimizing the voltage stability of a DC charging pile according to claim 5, characterized in that: In step S4, the switchable filter unit includes a high-frequency filter branch and a low-frequency filter branch. The filter branch is automatically switched based on the collected voltage ripple frequency: the high-frequency filter branch is activated when the ripple frequency is ≥1kHz, and the low-frequency filter branch is activated when the ripple frequency is <1kHz. At the same time, the filter capacitor and inductor parameters are dynamically adjusted.

7. The method for optimizing the voltage stability of a DC charging pile according to claim 6, characterized in that: In step S4, the impedance matching unit detects the output impedance and the battery's equivalent internal resistance in real time through a controllable reactor and a variable resistor network, and adjusts its own impedance value to maintain the ratio of the output impedance to the battery's equivalent internal resistance between 0.8 and 1.

2.

8. A DC charging pile voltage stability optimization system, characterized in that: It includes a multi-dimensional operating condition information acquisition module, a dynamic operating condition classification and threshold determination module, an adaptive PID control module, an output-side dynamic optimization module, and a closed-loop feedback iteration module; The multi-dimensional operating condition information acquisition module collects input power grid parameters, output electrical parameters, and power battery status parameters in real time through a multi-sensor array. It uses an algorithm combining Kalman filtering and moving average filtering to reduce noise and eliminate interference signals. The dynamic operating condition classification and threshold determination module constructs a model based on noise reduction data using a decision tree algorithm. It uses grid voltage fluctuation, battery equivalent internal resistance change rate, and output power adjustment amplitude as features to classify the charging process into four types of operating conditions, preset corresponding voltage stability thresholds, and determine in real time whether the operating condition and voltage exceed the limit. If the limit is exceeded, optimization is triggered. The adaptive PID control module is based on the operating condition classification results, adopts fuzzy control rules, takes voltage deviation and deviation change rate as input, and dynamically and smoothly adjusts PID parameters according to the operating conditions. The output-side dynamic optimization module includes a switchable filter unit and an impedance matching unit. The switchable filter unit automatically switches between high and low frequency branches and adjusts parameters according to the voltage ripple frequency. The impedance matching unit matches the output impedance and the battery's equivalent internal resistance in real time, keeping their ratio between 0.8 and 1.2, thus forming a dual optimization with the adaptive PID control. The closed-loop feedback iteration module constructs a closed-loop circuit using the deviation between the actual output voltage and the preset threshold as the feedback signal. It evaluates the optimization effect every 10ms, iteratively adjusts the parameters until the voltage stabilizes, and records the optimal parameter combination to build a database.

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