Control method and system for bidirectional charging and discharging circuit

Abstract

The present invention relates to the field of circuit control technology, and in particular to a control method and system for a bidirectional charge and discharge circuit. The method comprises the following steps: real-time acquisition of current monitoring data and voltage monitoring data of the bidirectional charge and discharge circuit; arc feature analysis of the current monitoring data and voltage monitoring data to obtain arc feature data; analysis of the high-frequency harmonic components and current ripple fluctuation amplitude of the bidirectional charge and discharge circuit based on the arc feature data; DC arc detection of the bidirectional charge and discharge circuit according to the high-frequency harmonic components and current ripple fluctuation amplitude to generate a DC arc fault discrimination result; calculation of the instantaneous current change rate of the current monitoring data, and short-circuit detection of the bidirectional charge and discharge circuit by the instantaneous current change rate. The present invention improves the safety and reliability of the bidirectional charge and discharge circuit by real-time monitoring of current and voltage data, accurate analysis of arc and short-circuit characteristics, optimization of relay control, and generation of accurate control instructions.

Description

Technical Field

[0001] The present invention relates to the field of circuit control technology, and in particular to a control method and system for a bidirectional charging and discharging circuit. Background Art

[0002] Initial charge-discharge circuit designs primarily focused on unidirectional energy flow, primarily used in traditional battery management systems. With increasing demand in electric vehicles (EVs), smart grids, and energy storage systems, bidirectional charge-discharge technology has become a research hotspot, aiming to achieve efficient energy conversion and management during battery charging and discharging. With the rapid development of power electronics, embedded control systems, and communications technologies, control methods for bidirectional charge-discharge circuits have gradually improved. Advanced digital signal processor (DSP) and field-programmable gate array (FPGA) technologies have significantly enhanced control accuracy and response speed. Furthermore, the application of advanced algorithms such as model predictive control (MPC) and adaptive control has significantly enhanced the stability and reliability of bidirectional circuits in high-power and complex environments. However, many existing circuits suffer from long response times when short-circuit faults occur, preventing prompt implementation of safety measures. Furthermore, relay contact overcurrent detection and control typically rely on simple current limiting, failing to fully consider the relay's operating characteristics and overcurrent profile. This, in turn, results in low safety and reliability in bidirectional charge-discharge circuits. Summary of the Invention

[0003] Based on this, it is necessary to provide a control method and system for a bidirectional charge and discharge circuit to solve at least one of the above technical problems.

[0004] To achieve the above object, a control method for a bidirectional charge and discharge circuit is provided, the method comprising the following steps:

[0005] Step S1: collecting current monitoring data and voltage monitoring data of the bidirectional charge and discharge circuit in real time; performing arc characteristic analysis on the current monitoring data and the voltage monitoring data to obtain arc characteristic data;

[0006] Step S2: analyzing the high-frequency harmonic components and current ripple fluctuation amplitude of the bidirectional charge and discharge circuit based on the arc characteristic data; performing DC arc detection on the bidirectional charge and discharge circuit based on the high-frequency harmonic components and current ripple fluctuation amplitude, and generating a DC arc fault determination result;

[0007] Step S3: Calculate the instantaneous current change rate of the current monitoring data and perform short circuit detection on the bidirectional charge and discharge circuit based on the instantaneous current change rate. When a current change that continuously exceeds a preset threshold is detected, a time window accumulation analysis is initiated to generate instantaneous surge data and a continuous short circuit fault determination result.

[0008] Step S4: Generate control instructions based on the DC arc discrimination results and the continuous short-circuit fault discrimination results; analyze the contact overcurrent morphology of the relay in the bidirectional charge and discharge circuit based on the transient surge data, and optimize the on-time control of the control instructions based on the analysis results to perform safety control optimization operations for the bidirectional charge and discharge circuit.

[0009] By extracting arc characteristics and analyzing high-frequency harmonics from real-time current and voltage data, the present invention effectively identifies high-frequency characteristics during arc generation, significantly improving the sensitivity and accuracy of DC arc detection. Combined with a dynamic monitoring mechanism for the instantaneous current rate of change, it enables rapid early warning and judgment of persistent short-circuit faults, effectively preventing the risk of circuit damage or battery overheating. Cumulative analysis of surge events using a time window mechanism not only identifies sudden current fluctuations but also distinguishes between transient surges and persistent short circuits, helping to enhance the system's fault classification capabilities. Optimizing the on-time control strategy based on relay contact overcurrent profile analysis extends relay service life, reduces contactor malfunction rates, and improves the safety and reliability of the entire bidirectional charge-discharge system. By integrating multiple discrimination results to generate control instructions and dynamically optimizing and controlling them, a complete closed-loop monitoring-judgment-control system is established, effectively enhancing the robustness and adaptability of the power management system in complex electrical environments. Therefore, by real-time monitoring of current and voltage data, accurately analyzing arc and short-circuit characteristics, optimizing relay control, and generating precise control instructions, the present invention improves the safety and reliability of the bidirectional charge-discharge circuit.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: collecting current monitoring data and voltage monitoring data of the bidirectional charge and discharge circuit in real time;

[0012] Step S12: performing time alignment on the current monitoring data and the voltage monitoring data to generate time-aligned current and voltage monitoring data; performing differential operation on the time-aligned current and voltage monitoring data to generate current change rate data and voltage change rate data;

[0013] Step S13: performing mutation edge detection on the current change rate data and the voltage change rate data to generate suspicious arc trigger point data; extracting the time window of the suspicious arc trigger point data, and performing envelope analysis on the current and voltage monitoring data according to the time window to generate arc voltage waveform feature data;

[0014] Step S14: performing spectrum analysis on the arc voltage waveform characteristic data to generate arc frequency domain characteristic data; performing key feature screening processing on the arc frequency domain characteristic data to generate arc characteristic data.

[0015] The present invention avoids misjudgment caused by sampling clock asynchrony by performing time alignment and differential operations on the collected current and voltage monitoring data, improves the ability to perceive slight changes in electrical signals, and lays a solid foundation for subsequent feature extraction. By processing the current and voltage change rate data through the mutation edge detection method, potential arc triggering points can be quickly located, and the ability to capture abnormal discharge transient behavior is enhanced. Based on the suspicious trigger point, a local time window is constructed and envelope analysis is performed to effectively extract the overall trend characteristics of the arc waveform, thereby improving the accuracy of modeling the time domain dynamics of the arc behavior. By performing spectral analysis on the envelope voltage waveform and performing key feature screening on the frequency domain features, noise interference and redundant information in irrelevant frequency bands can be eliminated, further improving the efficiency and accuracy of the arc discrimination algorithm. This method has good modularity and scalability by sequentially constructing a multi-stage analysis chain including time domain difference, mutation edge detection, envelope modeling, spectrum extraction, and key feature screening, providing a high-quality data foundation for the subsequent introduction of AI or machine learning models for intelligent arc identification.

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: performing spectrum decomposition processing on the arc characteristic data, extracting the high-frequency harmonic components of the current signal, and generating high-frequency harmonic data; performing amplitude analysis on the high-frequency harmonic data, and generating high-frequency harmonic amplitude data;

[0018] Step S22: performing time domain waveform analysis on the current monitoring data, calculating the current ripple fluctuation amplitude, and generating current ripple fluctuation data; calculating the arc characteristic index of the circuit based on the high-frequency harmonic amplitude data and the current ripple fluctuation data, and generating arc characteristic index data;

[0019] Step S23: using the arc characteristic index data to perform DC arc detection on the bidirectional charge and discharge circuit to generate a DC arc fault determination result.

[0020] By performing spectral decomposition on arc signature data, the present invention accurately extracts high-frequency harmonic components from complex current signals, effectively reproducing the high-frequency disturbances associated with arcing, and providing a high-resolution frequency-domain basis for arc identification. Time-domain waveform analysis is used to precisely calculate the ripple amplitude in the current, enabling the system to detect subtle instabilities during the charging and discharging process, thereby improving the sensitivity of detecting abnormal discharge conditions. By integrating the high-frequency harmonic amplitude with the current ripple amplitude to construct an arc signature index, the system effectively avoids the misjudgment problem associated with a single feature dimension, significantly enhancing the reliability and generalization capabilities of DC arc detection. The arc signature index provides real-time identification of circuit operating status, simplifying the traditional complex multi-feature joint classification process and making DC arc fault detection faster and more efficient, making it suitable for deployment in real-time power management systems. The constructed arc signature index mechanism exhibits excellent model compatibility, making it applicable to bidirectional charging and discharging scenarios in different operating modes, such as constant voltage and constant current. It also supports future integration and expansion with intelligent discrimination algorithms to achieve adaptive arc detection in multiple scenarios.

[0021] Preferably, step S23 includes the following steps:

[0022] Step S231: When the arc characteristic index shows any of the following conditions, it is determined to be a DC arc fault and the first fault discrimination data is obtained: the arc characteristic index is greater than 5.0; the current fluctuation exceeds ±10% and lasts for more than 3 seconds; the arc frequency is greater than 50 times / minute; the discharge voltage is greater than 400V or less than 200V;

[0023] Step S232: When the relationship between the arc characteristic index and the arc occurrence frequency meets the following conditions, generating second fault discrimination data: the arc characteristic index is greater than 6.0 and the arc occurrence frequency is greater than 60 times / minute; the fluctuation range of the arc characteristic index is greater than ±20%, and the current value fluctuation range exceeds ±15%;

[0024] Step S233: When determining a DC arc fault in the bidirectional charge and discharge circuit, third fault determination data is generated if the following conditions occur: the current is greater than 50A or the voltage is greater than 500V and the duration exceeds 60 seconds; the current fluctuation is greater than ±20% and the voltage fluctuation is greater than ±15%, and the duration of these fluctuations exceeds 30 seconds;

[0025] Step S234: Integrate the first fault determination data, the second fault determination data, and the third fault determination data to ultimately generate a DC arc fault determination result.

[0026] This invention establishes a structured arc fault identification system by setting multiple fault discrimination conditions (primary, secondary, and tertiary discrimination data) and performing joint judgments from multiple characteristic perspectives, effectively avoiding the risks of misjudgment and missed detection. When the current or voltage reaches a limit (e.g., current > 50A, voltage > 500V) and persists for a certain period, the judgment mechanism is triggered independently, enhancing the system's self-protection capabilities during abnormal peak conditions. By coupling the analysis of "arc occurrence frequency" with the "arc characteristic index," it accurately identifies potential arc hazards caused by short-duration, high-frequency discharges, improving detection sensitivity for periodic and intensive arcing. Incorporating dynamic parameters such as current fluctuation amplitude, voltage fluctuation amplitude, and duration into the discrimination logic ensures that arc fault identification more closely reflects the dynamic evolution of real-world operating conditions, thereby enhancing the accuracy of identifying persistent arc events. By combining and integrating multiple discrimination rules into a final fault judgment result, the system provides a modular and adjustable discrimination strategy framework, facilitating subsequent dynamic adjustment and threshold optimization based on specific circuit application scenarios.

[0027] Preferably, step S3 includes the following steps:

[0028] Step S31: performing numerical differentiation calculation on the current monitoring data to obtain instantaneous current change rate data; performing smoothing filtering on the instantaneous current change rate data to generate smoothed instantaneous current change rate data;

[0029] Step S32: Compare the smoothed instantaneous current change rate data with the short circuit determination threshold value. When the smoothed instantaneous current change rate data is greater than or equal to the short circuit determination threshold value, generate short circuit trigger flag data.

[0030] Step S33: Performing a Boolean check on the short-circuit trigger flag data. When the short-circuit trigger flag data is true, starting a time window cumulative analysis to analyze the fluctuation amplitude of the current monitoring data under the short-circuit trigger flag data within the time window to generate time window cumulative data.

[0031] Step S34: Perform continuity analysis on the accumulated data in the time window. If the current change amplitude in the accumulated data in the time window is greater than 20A or the current change duration is greater than 30s, a continuous short circuit fault judgment result is generated; based on the accumulated data in the time window, a surge analysis is performed on the bidirectional charging and discharging circuit to generate instantaneous surge data.

[0032] The present invention performs numerical differentiation processing on the current monitoring data to quickly extract the instantaneous current change rate, effectively identify short-circuit precursor characteristics such as current surge or drop, and improve the real-time performance and response speed of fault warning. The instantaneous current change rate is processed in combination with a smoothing filter algorithm to remove high-frequency noise interference, effectively prevent false alarms or missed alarms due to instantaneous jitter, and ensure the reliability and accuracy of fault identification. The introduction of short-circuit trigger flags and Boolean discrimination logic, combined with time window cumulative analysis, can not only judge abnormal pulses in a short period of time, but also identify continuous short-circuit characteristics, and construct a fault judgment model with more time-series perception capabilities. By making a linkage judgment on the amplitude and duration of the current change within the time window, the two typical fault states of instantaneous surge and continuous short circuit are effectively distinguished, and the perception and control capabilities of extreme working conditions are improved. Introducing a surge analysis process in the context of short-circuit triggering and extracting instantaneous surge feature data can provide accurate input for subsequent relay overcurrent protection or control instruction adjustment, thereby enhancing the overall safety performance of the system.

[0033] Preferably, performing surge analysis on the bidirectional charge and discharge circuit based on the accumulated data in the time window in step S34 includes:

[0034] Performing current feature extraction on the smoothed instantaneous current change rate data to obtain current instantaneous change feature data, wherein the current feature extraction includes current change rate, fluctuation amplitude and high-frequency components;

[0035] The instantaneous current change characteristic data is mapped to a high-dimensional space through radial basis function to construct a hyperplane, thereby obtaining a surge hyperplane. The accumulated data in the time window is input into the surge hyperplane for time shift analysis, thereby generating surge time shift data.

[0036] The plane lateral proportion of the surge hyperplane is calculated based on the surge time-shift data to obtain the surge lateral proportion data; based on the surge lateral proportion data, the instantaneous surge intensity of the current monitoring data under the short-circuit trigger mark data is calculated to obtain the instantaneous surge intensity at each time point;

[0037] The instantaneous surge intensity at each time point is aggregated to generate instantaneous surge data.

[0038] This invention significantly enhances the ability to identify complex current fluctuation patterns by extracting multiple features—including rate of change, fluctuation amplitude, and high-frequency components—from smoothed instantaneous current rate of change data, providing solid data support for subsequent surge analysis. A radial basis function (RBF) is used to map instantaneous current variation features into a high-dimensional feature space, effectively amplifying differences in nonlinear surge patterns and making previously difficult-to-separate surge events separable within a high-dimensional hyperplane. A surge hyperplane is constructed in high-dimensional space. Time-shift analysis is performed by projecting accumulated data from a time window onto this hyperplane, accurately capturing the onset and intensity evolution of surge events and improving the temporal sensitivity of surge responses. A lateral contribution calculation mechanism based on the surge hyperplane determines the spatial coverage of current anomalies, effectively distinguishing between weak surges, strong surges, and non-surge phenomena, and improving anomaly identification accuracy. The introduction of an instantaneous surge intensity calculation mechanism generates a quantitative surge intensity indicator at each time point. Data aggregation then captures overall trend characteristics, facilitating adaptive control logic adjustments and early warning. This method can not only perform high-resolution surge recognition in short-circuit trigger scenarios, but also combine short-circuit trigger flags to achieve high-accuracy abnormal event modeling and risk level classification, facilitating intelligent decision-making and control of bidirectional charging and discharging circuit systems.

[0039] Preferably, generating a control instruction according to the DC arc determination result and the continuous short circuit fault determination result in step S4 includes:

[0040] Perform high-level analysis on the signal characteristics in the DC arc identification results to generate arc duration and waveform change data;

[0041] Perform real-time pattern recognition on the current waveform in the short-circuit fault identification result to generate short-circuit pattern category data;

[0042] Perform joint spatiotemporal analysis on arc duration and waveform change data and short-circuit mode category data to generate fault synergistic impact data;

[0043] Fuse the fault collaborative impact data in the time and frequency domains to generate comprehensive fault severity data;

[0044] The DC arc identification results and the continuous short circuit fault identification results are redundantly verified by comprehensive fault severity data and control instructions are generated to obtain control instructions.

[0045] This invention uses high-level analysis of signal features from DC arc identification results to obtain arc duration and waveform evolution information, enabling dynamic tracking and structural understanding of arc behavior, and improving the ability to identify arc risk sources. Real-time pattern recognition of short-circuit fault identification results allows for classification of short-circuit behavior into different modes (such as sudden, continuous, and periodic), enhancing the system's ability to analyze short-circuit behavior and providing a basis for control strategy adaptation. By leveraging the intersecting characteristics of arc and short-circuit data in both temporal and spatial dimensions, fault synergistic impact data is generated, effectively revealing arc-short-circuit coupling relationships and thereby determining fault propagation paths and risk diffusion trends. By fusing and analyzing synergistic impact data in both the time and frequency domains, the system not only improves the accuracy of fault severity characterization but also enhances the ability to adapt to multi-source abnormal signals in complex waveforms. A redundant verification mechanism for identification results before control command generation prevents malfunctions caused by single judgment errors, effectively improving control command triggering accuracy and system stability. Combined with the fault severity level assessment results, it can automatically generate control instructions with different levels of urgency, such as current limiting, power outage, system reconstruction, etc., thereby achieving differentiated and adaptive processing of response actions, ensuring the safe operation of the system in sudden arc or short circuit events.

[0046] Preferably, in step S4, analyzing the contact overcurrent profile of the relay in the bidirectional charge and discharge circuit based on the transient surge data includes:

[0047] Based on the instantaneous surge data, the current monitoring data of the bidirectional charging and discharging circuit is used to capture high-frequency surge events and generate instantaneous surge waveform data;

[0048] Perform FIR bandpass filtering on the instantaneous surge waveform data to generate denoised surge characteristic data;

[0049] Extract the time domain features of the denoised surge characteristic data to obtain surge parameter quantification data;

[0050] Using surge parameter quantification data, the contact current distribution of the relay in the bidirectional charge and discharge circuit is analyzed to generate contact current distribution data. Based on the contact current distribution data, the local contact current propagation of the relay is simulated to generate relay contact current exchange simulation data.

[0051] Perform electron quantum state change analysis on relay contact current exchange simulation data to generate electron quantum state change data;

[0052] The contact wear of the relay contacts is predicted through the data of electron quantum state changes, and the analysis results of the contact overcurrent morphology are generated.

[0053] The present invention can accurately identify and separate important surge event features from current monitoring data by performing high-frequency surge event capture and FIR bandpass filtering denoising on transient surge data, ensuring a clear presentation of abnormal arc and current waveforms, and providing high-quality data for subsequent analysis. By extracting time domain features from the denoised surge feature data and quantifying the surge parameters, key current parameters such as surge amplitude, duration, and transient waveform can be more accurately described, improving the system's ability to respond to surge events in a timely manner and enhancing the accuracy of fault warnings. By performing a detailed analysis of the current distribution of relay contacts using surge parameter quantification data, not only can the current distribution be understood, but also its potential impact on relay life and performance can be predicted. This provides a scientific basis for the design optimization and practical use of relays. By dynamically modeling relays using local contact current propagation simulation, the behavioral characteristics of contacts under different current loads can be understood in advance, the potential risks of contact overcurrent and poor contact can be identified, and damage to equipment caused by current fluctuations or overloads can be prevented. Analyzing the changes in the electron quantum state during current exchange across relay contacts can delve into the microscopic dynamics of electron behavior, revealing subtle variations in current flow through the contacts and helping to further enhance the ability to predict arcing and overcurrent. By analyzing this data, the wear of relay contacts can be effectively assessed, providing early warning of potential relay failures, avoiding system failures caused by excessive contact wear, and improving the reliability and safety of the charge-discharge circuit. This precise analysis enables precise control and prediction of relays, reducing risks such as overcurrent and overload, thereby improving the stability and safety of bidirectional charge-discharge circuits under complex operating conditions and ensuring the long-term stable operation of the equipment.

[0054] Preferably, in step S4, optimizing the on-time control of the control instruction according to the analysis result includes:

[0055] The analysis results of the contact overcurrent morphology are processed by accumulating the overcurrent duration to generate the total contact overcurrent duration data;

[0056] Perform interval comparison processing on the total contact overcurrent duration data to generate contact overcurrent trend data;

[0057] Extract the conduction signal limit of the contact overcurrent trend data and generate the contact conduction signal allowable duration data, where the contact conduction signal allowable duration data includes the maximum single duration and the minimum interval time;

[0058] Optimize the circuit continuous conduction time of the control instruction according to the maximum single duration and minimum interval time, and generate conduction time limit parameter data;

[0059] The on-time limit parameter data is used to adjust the on-time control of the control instruction to perform safety control optimization operations of the bidirectional charging and discharging circuit.

[0060] By accumulating overcurrent durations based on contact overcurrent profile analysis results, the present invention accurately quantifies the overcurrent duration of relay contacts, monitors contact load conditions in real time, and ensures timely intervention before safety thresholds are exceeded, thereby preventing damage or aging of relay contacts. Interval-by-interval comparisons are performed on the total contact overcurrent duration data to generate contact overcurrent trend data, providing a key basis for subsequent optimization of control instructions. By identifying contact overcurrent trends, future overcurrent risks can be predicted in advance, ensuring the long-term stable operation of the charge-discharge circuit. The conduction signal limits extracted from the contact overcurrent trend data are generated to generate contact conduction signal allowable duration data. This ensures that control instructions can reasonably limit the maximum single duration and minimum interval of the conduction signal during execution, preventing circuit overload or relay contact damage caused by excessively long conduction durations or short intervals. Based on the maximum single duration and minimum interval, the control instructions are optimized for the continuous conduction time of the circuit, further optimizing the circuit's operating state. Under varying load conditions, the conduction time can be dynamically adjusted to ensure smooth circuit operation under different operating conditions and reduce unnecessary energy consumption. By controlling and adjusting the on-time limit parameters, relay contact failures due to overload can be effectively avoided, improving the safety and reliability of bidirectional charge and discharge circuits. While ensuring charge and discharge efficiency, this also extends equipment life, reducing maintenance costs and downtime. Control command on-time optimization not only adapts to varying current loads but also automatically adjusts to different fault conditions or operating conditions, providing the system with more flexible and efficient adaptive control capabilities. This flexibility ensures optimal circuit operation under diverse circumstances. Through intelligent analysis and control parameter optimization, the automation and intelligentization of bidirectional charge and discharge circuits will be promoted, providing higher precision and efficiency for real-time monitoring and regulation in next-generation power systems, and driving innovation and application of intelligent control technologies for power equipment.

[0061] In this specification, a control system of a bidirectional charge and discharge circuit is provided, which is used to execute the above-mentioned control method of the bidirectional charge and discharge circuit. The control system of the bidirectional charge and discharge circuit includes:

[0062] The arc feature extraction module is used to collect the current monitoring data and voltage monitoring data of the bidirectional charging and discharging circuit in real time; perform arc feature analysis on the current monitoring data and voltage monitoring data to obtain arc feature data;

[0063] An arc fault identification module is used to analyze the high-frequency harmonic components and current ripple fluctuation amplitude of the bidirectional charge and discharge circuit based on arc characteristic data; perform DC arc detection on the bidirectional charge and discharge circuit based on the high-frequency harmonic components and current ripple fluctuation amplitude, and generate a DC arc fault identification result;

[0064] The short-circuit detection module is used to calculate the instantaneous current change rate of the current monitoring data and perform short-circuit detection on the bidirectional charge and discharge circuit based on the instantaneous current change rate. When a current change that continuously exceeds a preset threshold is detected, a time window accumulation analysis is initiated to generate instantaneous surge data and a continuous short-circuit fault detection result.

[0065] The contact conduction control module is used to generate control instructions based on the DC arc judgment results and the continuous short-circuit fault judgment results; based on the transient surge data, it analyzes the contact overcurrent morphology of the relay in the bidirectional charge and discharge circuit, and optimizes the conduction time control of the control instructions based on the analysis results to perform safety control optimization operations for the bidirectional charge and discharge circuit.

[0066] The beneficial effect of the present invention is that through the real-time current and voltage monitoring of the arc feature extraction module, current fluctuations and voltage changes can be accurately captured, providing a reliable data basis for subsequent arc fault identification and short circuit detection. Real-time monitoring can detect potential arc problems and circuit faults at an early stage, significantly improving the safety of circuit operation. The arc fault identification module can effectively detect and identify DC arc faults by analyzing the high-frequency harmonic components and current ripple fluctuation amplitude in the arc feature data. This analysis method not only improves the accuracy of fault identification, but also can provide early warnings to avoid further development of arc faults, reducing system damage and downtime. The short circuit identification module performs intelligent short circuit detection by calculating the instantaneous current change rate and combining it with a preset threshold. Once an abnormal current fluctuation is detected, the module can quickly initiate a time window accumulation analysis and generate instantaneous surge data to effectively identify persistent short circuit faults. This mechanism can respond to current anomalies in a timely manner to prevent short circuit faults from causing safety risks such as equipment damage or fire. The contact conduction control module not only generates control instructions based on arc and short-circuit fault detection results, but also uses transient surge data to analyze the overcurrent profile of the relay contacts, accurately predicting and adjusting the contact current. This function ensures optimal relay operation, preventing contact wear or circuit failure due to overcurrent or overload. After analyzing the overcurrent profile of the relay contacts, the module intelligently optimizes the conduction time, limiting the maximum on-time and minimum interval of the circuit. This optimization enables safe control under variable current load conditions, reduces unnecessary energy loss, and extends the service life of electrical components. By integrating multiple modules, such as arc fault detection and short-circuit detection, the system can respond to electrical faults in real time, preventing potential hazards from escalating. Furthermore, intelligent optimization of circuit control instructions avoids safety incidents caused by equipment overload, significantly improving system safety and reliability. Therefore, the present invention improves the safety and reliability of bidirectional charge and discharge circuits by real-time monitoring of current and voltage data, accurately analyzing arc and short-circuit characteristics, optimizing relay control, and generating precise control instructions. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 1. A schematic flow chart of a method for controlling a bidirectional charge and discharge circuit;

[0068] Figure 2 for Figure 1 Detailed implementation steps of step S1 in FIG.

[0069] Figure 3 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0070] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0071] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0072] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0073] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0074] To achieve this, please refer to Figures 1 to 3 , a control method for a bidirectional charge and discharge circuit, the method comprising the following steps:

[0075] Step S1: collecting current monitoring data and voltage monitoring data of the bidirectional charge and discharge circuit in real time; performing arc characteristic analysis on the current monitoring data and the voltage monitoring data to obtain arc characteristic data;

[0076] Step S2: analyzing the high-frequency harmonic components and current ripple fluctuation amplitude of the bidirectional charge and discharge circuit based on the arc characteristic data; performing DC arc detection on the bidirectional charge and discharge circuit based on the high-frequency harmonic components and current ripple fluctuation amplitude, and generating a DC arc fault determination result;

[0077] Step S3: Calculate the instantaneous current change rate of the current monitoring data and perform short circuit detection on the bidirectional charge and discharge circuit based on the instantaneous current change rate. When a current change that continuously exceeds a preset threshold is detected, a time window accumulation analysis is initiated to generate instantaneous surge data and a continuous short circuit fault determination result.

[0078] Step S4: Generate control instructions based on the DC arc discrimination results and the continuous short-circuit fault discrimination results; analyze the contact overcurrent morphology of the relay in the bidirectional charge and discharge circuit based on the transient surge data, and optimize the on-time control of the control instructions based on the analysis results to perform safety control optimization operations for the bidirectional charge and discharge circuit.

[0079] By extracting arc characteristics and analyzing high-frequency harmonics from real-time current and voltage data, the present invention effectively identifies high-frequency characteristics during arc generation, significantly improving the sensitivity and accuracy of DC arc detection. Combined with a dynamic monitoring mechanism for the instantaneous current rate of change, it enables rapid early warning and judgment of persistent short-circuit faults, effectively preventing the risk of circuit damage or battery overheating. Cumulative analysis of surge events using a time window mechanism not only identifies sudden current fluctuations but also distinguishes between transient surges and persistent short circuits, helping to enhance the system's fault classification capabilities. Optimizing the on-time control strategy based on relay contact overcurrent profile analysis extends relay service life, reduces contactor malfunction rates, and improves the safety and reliability of the entire bidirectional charge-discharge system. By integrating multiple discrimination results to generate control instructions and dynamically optimizing and controlling them, a complete closed-loop monitoring-judgment-control system is established, effectively enhancing the robustness and adaptability of the power management system in complex electrical environments. Therefore, by real-time monitoring of current and voltage data, accurately analyzing arc and short-circuit characteristics, optimizing relay control, and generating precise control instructions, the present invention improves the safety and reliability of the bidirectional charge-discharge circuit.

[0080] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart of a method for controlling a bidirectional charge and discharge circuit according to the present invention. In this embodiment, the method for controlling a bidirectional charge and discharge circuit includes the following steps:

[0081] Step S1: collecting current monitoring data and voltage monitoring data of the bidirectional charge and discharge circuit in real time; performing arc characteristic analysis on the current monitoring data and the voltage monitoring data to obtain arc characteristic data;

[0082] Step S2: analyzing the high-frequency harmonic components and current ripple fluctuation amplitude of the bidirectional charge and discharge circuit based on the arc characteristic data; performing DC arc detection on the bidirectional charge and discharge circuit based on the high-frequency harmonic components and current ripple fluctuation amplitude, and generating a DC arc fault determination result;

[0083] Step S3: Calculate the instantaneous current change rate of the current monitoring data and perform short circuit detection on the bidirectional charge and discharge circuit based on the instantaneous current change rate. When a current change that continuously exceeds a preset threshold is detected, a time window accumulation analysis is initiated to generate instantaneous surge data and a continuous short circuit fault determination result.

[0084] Step S4: Generate control instructions based on the DC arc discrimination results and the continuous short-circuit fault discrimination results; analyze the contact overcurrent morphology of the relay in the bidirectional charge and discharge circuit based on the transient surge data, and optimize the on-time control of the control instructions based on the analysis results to perform safety control optimization operations for the bidirectional charge and discharge circuit.

[0085] In this embodiment of the present invention, high-precision Hall current sensors and differential voltage sampling circuits are deployed at the positive and negative poles of a bidirectional charge-discharge circuit to synchronously collect real-time data at a 10kHz frequency. Signal noise reduction preprocessing is performed using a sliding average filter and a hardware RC low-pass filter. A wavelet packet decomposition algorithm is then used to extract energy features in the 3-10kHz frequency band, which are then combined with voltage mutation rate analysis to form an arc signature database. In the fault detection phase, three FIR digital filter banks (20kHz, 50kHz, and 100kHz) are used to calculate the proportion of high-frequency harmonic components. A dynamic threshold method is simultaneously used to monitor the current ripple amplitude. A three-layer decision tree model is triggered to identify DC arc faults when the harmonics continuously exceed 5%, the ripple exceeds the rated value by 20%, and the voltage mutation rate exceeds 10 times / second. A three-point central difference method is also used to calculate the current change rate in real time. A hardware comparator is configured to initiate a dual-time window accumulation analysis (8 times exceeding the limit in the short window of 10ms and continuous triggering in the long window of 100ms) when di / dt exceeds 500A / ms. Combined with 100kHz fault recording, the three inrush current elements are extracted, enabling accurate short-circuit fault identification. Finally, the arc and short-circuit detection results are integrated through the state machine decision system, and 5ms-level arc rapid interruption is prioritized. The interruption timing is dynamically optimized based on the contact ablation model established by HIL simulation: pre-arc extinguishing control or hardware direct cutting protection is enabled based on the integral value of the surge current. After the fault is cleared, the circuit integrity is verified through a three-level self-test (insulation resistance measurement, low-current conduction test, and impedance spectrum analysis). The entire system uses the STM32H7+FPGA heterogeneous architecture to achieve real-time processing and hardware redundancy, and combines Model-Based Design to generate code and back-to-back testing to ensure operational reliability.

[0086] As an example of the present invention, refer to Figure 2As shown, in this example, step S1 includes:

[0087] Step S11: collecting current monitoring data and voltage monitoring data of the bidirectional charge and discharge circuit in real time;

[0088] Step S12: performing time alignment on the current monitoring data and the voltage monitoring data to generate time-aligned current and voltage monitoring data; performing differential operation on the time-aligned current and voltage monitoring data to generate current change rate data and voltage change rate data;

[0089] Step S13: performing mutation edge detection on the current change rate data and the voltage change rate data to generate suspicious arc trigger point data; extracting the time window of the suspicious arc trigger point data, and performing envelope analysis on the current and voltage monitoring data according to the time window to generate arc voltage waveform feature data;

[0090] Step S14: performing spectrum analysis on the arc voltage waveform characteristic data to generate arc frequency domain characteristic data; performing key feature screening processing on the arc frequency domain characteristic data to generate arc characteristic data.

[0091] In this embodiment, a high-response Hall-effect current sensor (such as the LEM LA55-P) and an isolated differential voltage probe (such as the TI ISO224) are deployed at the positive and negative electrodes of a bidirectional charge-discharge circuit, respectively. A multi-channel simultaneous sampling ADC module (ADI AD7606C-16) is used to synchronously capture current and voltage signals at a 20kHz sampling rate, ensuring the clocks of both data sets are identical (the FPGA provides hardware-level timestamp alignment). The current signal is filtered through a second-order Butterworth low-pass filter (cutoff frequency 15kHz) to eliminate switching noise, while the voltage signal is filtered through a common-mode choke to suppress ground loop interference. The FPGA's hardware FIFO buffer is used to interpolate and resample the current and voltage data, compensating for sensor transmission delays (calibrated to within ±1μs synchronization error) and generating strictly time-aligned data streams. A hardware-accelerated three-point central difference method (sliding window: n-1, n, n+1) is used to calculate the current rate of change (di / dt) and voltage rate of change (dv / dt), with Δt = 50μs. The differential results are stored as 16-bit fixed-point numbers to reduce computational latency. A dual-threshold comparator is designed to flag suspicious points when the instantaneous value of the current rate of change exceeds 200A / ms or the absolute value of the voltage rate of change exceeds 30% of the nominal value. This is combined with a sliding window variance check (10ms window width) to eliminate sporadic interference and generate a time-stamped trigger event queue. For the raw data segment 50ms before and after the trigger point, a full-wave rectifier circuit (such as the OPA2182 precision rectifier) ​​is used in conjunction with a low-pass filter with a cutoff frequency of 2kHz to extract the current / voltage envelope. The envelope peak, rise time (10%-90%), and duration are calculated as waveform features. An FFT acceleration core (FPGA built-in IP core) is used to perform 4096-point FFT operations on the arc waveform segment with a frequency resolution of 4.88Hz, focusing on the 0.5-20kHz frequency band. An energy proportion algorithm is used to extract three key features: high-frequency resonance peak (the frequency of the maximum amplitude point in the 5-10kHz range), fundamental harmonic distortion rate (the ratio of the third harmonic amplitude to the fundamental frequency), and spectral flatness (energy variance in the 10-20kHz frequency band). A random forest model is trained based on a historical fault dataset to screen out the five most discriminative frequency domain features (such as the 8.2kHz energy proportion and the 15kHz spectrum steep drop slope). Real-time feature mapping is achieved through a hardware lookup table (LUT), and the compressed arc feature vector is finally output.

[0092] As an example of the present invention, refer to Figure 3 As shown, in this example, step S2 includes:

[0093] Step S21: performing spectrum decomposition processing on the arc characteristic data, extracting the high-frequency harmonic components of the current signal, and generating high-frequency harmonic data; performing amplitude analysis on the high-frequency harmonic data, and generating high-frequency harmonic amplitude data;

[0094] Step S22: performing time domain waveform analysis on the current monitoring data, calculating the current ripple fluctuation amplitude, and generating current ripple fluctuation data; calculating the arc characteristic index of the circuit based on the high-frequency harmonic amplitude data and the current ripple fluctuation data, and generating arc characteristic index data;

[0095] Step S23: using the arc characteristic index data to perform DC arc detection on the bidirectional charge and discharge circuit to generate a DC arc fault determination result.

[0096] In this embodiment of the present invention, an FPGA-hardened discrete wavelet transform (DWT) module (using the db6 wavelet base) is used to perform a six-layer decomposition of arc characteristic data, extracting high-frequency harmonic components in the 5-20 kHz frequency band (corresponding to the 4th and 5th layers of detail coefficients). Full-wave rectification is performed on the decomposed high-frequency subband signals, and the effective mean square (RMS) value of each frequency band is calculated using a sliding window integration (window width of 10ms). The result is normalized to a percentage of the fundamental amplitude (e.g., 5 kHz harmonic proportion = high-frequency RMS / 50 Hz fundamental RMS × 100%). A white noise test signal is injected before harmonic calculation to dynamically calibrate the background noise threshold of each frequency band, retaining only valid harmonic data exceeding three standard deviations of the noise baseline. An IIR digital filter with a 1kHz cutoff frequency is applied to the raw current signal to remove high-frequency interference. A dynamic baseline tracking algorithm is then used to calculate the dynamic baseline current value using a sliding average (100ms window length). The ripple absolute value is obtained through full-wave rectification. Peak-to-peak (Max-Min) and RMS values ​​within each 10ms window are then calculated. A weighted index formula is constructed: Arc Signature Index = 0.6 × (5kHz harmonic fraction / 10%) + 0.4 × (ripple peak-to-peak value / 20% of rated current). A primary alarm is triggered when the index exceeds 1.0, while a high-risk arc is identified when it exceeds 1.5. A two-level fault determination mechanism is designed: if the index exceeds 1.5 for three consecutive sampling points (with 0.1ms intervals), protective tripping is immediately initiated. A fault confirmation is issued if the cumulative duration exceeding the threshold (>1.0) exceeds 60% within a 200ms window and is accompanied by a voltage drop greater than 15% of the nominal value. In sudden load changes (such as charge-discharge switching), a pre-flagging mechanism is activated: control signal changes are monitored 10ms in advance, and the judgment threshold is temporarily raised to 1.8. A hardware comparator monitors current polarity reversals in real time to eliminate false harmonics caused by reverse recovery current. A dedicated data path is allocated within the FPGA to implement parallel pipeline processing of harmonic decomposition (DWT) and ripple calculation (IIR filtering + sliding statistics), ensuring an end-to-end latency of less than 500μs from data input to fault detection. A CAN bus communication port is reserved to support the host computer's injection of simulated arc waveforms (such as the IEC 62619 standard test template) and online adjustment of harmonic weight coefficients and threshold parameters. After an arc fault is detected, a high-speed storage unit (SRAM cache) is triggered to record the complete data stream 50ms before and after the fault, including the original current / voltage, harmonic components, and characteristic exponential curves, for offline retrospective analysis. A hardware protection circuit independent of the main MCU is set up. When the characteristic index is greater than 2.0 or the current mutation rate is greater than 1000A / ms, the CPLD directly drives the MOSFET to forcibly cut off the main circuit, with a response time of less than 20μs.

[0097] Preferably, step S23 includes the following steps:

[0098] Step S231: When the arc characteristic index shows any of the following conditions, it is determined to be a DC arc fault and the first fault discrimination data is obtained: the arc characteristic index is greater than 5.0; the current fluctuation exceeds ±10% and lasts for more than 3 seconds; the arc frequency is greater than 50 times / minute; the discharge voltage is greater than 400V or less than 200V;

[0099] Step S232: When the relationship between the arc characteristic index and the arc occurrence frequency meets the following conditions, generating second fault discrimination data: the arc characteristic index is greater than 6.0 and the arc occurrence frequency is greater than 60 times / minute; the fluctuation range of the arc characteristic index is greater than ±20%, and the current value fluctuation range exceeds ±15%;

[0100] Step S233: When determining a DC arc fault in the bidirectional charge and discharge circuit, third fault determination data is generated if the following conditions occur: the current is greater than 50A or the voltage is greater than 500V and the duration exceeds 60 seconds; the current fluctuation is greater than ±20% and the voltage fluctuation is greater than ±15%, and the duration of these fluctuations exceeds 30 seconds;

[0101] Step S234: Integrate the first fault determination data, the second fault determination data, and the third fault determination data to ultimately generate a DC arc fault determination result.

[0102] In this embodiment of the present invention, six independent hardware comparators (such as ADcmp601) are deployed to monitor key parameters in real time and in parallel: Comparator 1: Arc characteristic index > 5.0 → triggers a red LED alarm and latches a flag bit; Comparator 2: Current fluctuation absolute value > 10% (output by the RMS calculation module) + 3-second timer (FPGA built-in 32-bit counter) → timeout trigger; Comparator 3: Arc frequency counter (based on the rising edge detection circuit) > 50 times / minute → pulse accumulator over-limit trigger; Comparator 4: Voltage monitoring channel > 400V or < 200V → threshold comparison trigger; Comparator 5: Arc characteristic index > 6.0 + arc frequency > 60 times / minute → AND gate logic trigger; Comparator 6: Arc index fluctuation > ±20% (calculated by sliding window standard deviation) + current change > ±15% → window variance over-limit trigger. For the long-duration conditions of S233 (e.g., 60 seconds / 30 seconds), a double-buffered ring queue is used to store current / voltage fluctuation data. The fluctuation ratio (Max-Min) / Avg) is calculated every second. A hardware watchdog timer (such as the MAX6818) is configured to monitor the duration. If the fluctuation ratio exceeds the limit for 30 consecutive seconds, a timer overflow interrupt is triggered. A three-level fault hierarchy is implemented: Level 1 (Emergency Trip): Voltage > 500V or current > 50A in S231 triggers a hardware direct trip circuit (<100μs response time). Level 2 (Confirmed Trip): If either condition in S231 or S232 is met and the circuit does not recover for 200ms, the MCU drives a relay to trip the circuit. Level 3 (Pre-warning Load Reduction): If only the condition in S233 is met, the charge and discharge power is reduced to 30% and reported to the management system. Data integration is implemented using hardware or a gate array (SN74LVC1G32). Any fault signal triggers a global interrupt. At the moment of switching between charge and discharge modes (control commands received via the CAN bus), the arc signature index threshold is automatically raised to 7.0 and remains at this level for 500ms before recovering. When the ambient temperature exceeds 85°C (detected via an I2C temperature sensor), the current / voltage fluctuation threshold is relaxed to ±25%. 4MB of SRAM is configured as waveform memory. When a fault is triggered, the following data is automatically saved: the original current / voltage waveforms (20kHz sampling) from 10 seconds before to 2 seconds after the fault, the arc signature index change curve and comparator trigger timestamp, and system operation logs (charging and discharging status, temperature, and load impedance). Key decision signals (such as the Level 1 trip command) are generated by three independent comparators, output using a 2-out-of-3 voting mechanism. An analog protection circuit independent of the MCU is implemented: when the current exceeds 80A, a Zener diode (BZX55C) triggers a thyristor (BT152) to force a short-circuit fuse.Using a programmable load (such as the Chroma 63200A), a standard arc waveform (compliant with UL 1699B test specifications) is injected via the GPIB interface to automatically cycle through test cases: a voltage range of 50-1000V, current steps of 10-200A, and an arc frequency of 50-500Hz. A hardware self-test is performed at power-up, sequentially triggering virtual fault signals to verify the response time and accuracy of each level. A calibration sequence is automatically run every 24 hours, injecting harmonic signals of known amplitude to dynamically correct deviations in wavelet decomposition coefficients and RMS calculations.

[0103] Preferably, step S3 includes the following steps:

[0104] Step S31: performing numerical differentiation calculation on the current monitoring data to obtain instantaneous current change rate data; performing smoothing filtering on the instantaneous current change rate data to generate smoothed instantaneous current change rate data;

[0105] Step S32: Compare the smoothed instantaneous current change rate data with the short circuit determination threshold value. When the smoothed instantaneous current change rate data is greater than or equal to the short circuit determination threshold value, generate short circuit trigger flag data.

[0106] Step S33: Performing a Boolean check on the short-circuit trigger flag data. When the short-circuit trigger flag data is true, starting a time window cumulative analysis to analyze the fluctuation amplitude of the current monitoring data under the short-circuit trigger flag data within the time window to generate time window cumulative data.

[0107] Step S34: Perform continuity analysis on the accumulated data in the time window. If the current change amplitude in the accumulated data in the time window is greater than 20A or the current change duration is greater than 30s, a continuous short circuit fault judgment result is generated; based on the accumulated data in the time window, a surge analysis is performed on the bidirectional charging and discharging circuit to generate instantaneous surge data.

[0108] In this embodiment of the present invention, an analog differentiator is constructed using an operational amplifier (such as the OPA2182) to perform real-time differential calculations on the Hall effect current sensor's output signal, enabling hardware-level extraction of di / dt (with a bandwidth limited to 50kHz to prevent high-frequency noise amplification). A digital three-point central difference method (formula: di / dt = (I_{n+1} - I_{n-1}) / (2Δt), with Δt = 20μs) is simultaneously implemented within the FPGA as a redundant verification channel. An adaptive Kalman filter (implemented in an FPGA core) dynamically adjusts the filter gain based on the current rate variance, enhancing smoothing when noise is high (with a cutoff frequency of 500Hz) while maintaining a 1kHz bandwidth in steady state. A second-order Butterworth low-pass filter (with a cutoff frequency of 2kHz) is configured as hardware preprocessing to eliminate the effects of switching noise on the differential. The threshold is dynamically calculated based on the circuit's rated current (e.g., a threshold of 0.2×I_rated, or 20A / ms for an I_rated of 100A) and updated online via the SPI interface. A fixed value of 50A / ms serves as an emergency trigger line (e.g., for MOSFET direct current protection). A high-speed comparator (such as the TI TLV3201) compares the smoothed di / dt value against a threshold in real time. If the threshold is exceeded, the FPGA interrupt pin is immediately pulled high, triggering a priority task. The current timestamp and current value are latched into registers for subsequent modules to read. A stable short-circuit trigger flag (True) is output only when three consecutive sampling points (with 10μs intervals) exceed the threshold. A short window (100ms) stores 1000 points of current data per second and calculates the peak-to-peak value (Max-Min) and RMS value within the window in real time. A long window (30s) records the duration of the short-circuit trigger flag and uses a 32-bit counter (internal to the FPGA) to calculate the cumulative True state. Within the short window, if the current fluctuation exceeds 10A (absolute value) and the rate of change exceeds 5A / ms, it is marked as a "valid fluctuation event." The density of valid events within the window (e.g., ≥5 events within 100ms) is calculated as a cumulative indicator. A sustained short circuit fault (Level 2) is triggered when the long window counter displays True for >25 seconds and the short window fluctuation density exceeds 70%. If the current suddenly changes to >20A and fails to recover to ±5% of the baseline within 30 seconds, an irreversible short circuit is determined based on the temperature sensor data (e.g., >90°C). After a short circuit is triggered, 100kHz oversampling mode is enabled (the ADC switches to high-speed mode), and the current waveform is recorded from 5ms before the fault to 50ms after the fault.The three key elements of surge calculations are captured: the absolute maximum value of the waveform and the inverse of the time difference between the 10% and 90% peak values ​​(e.g., if the 90% peak value is reached within 0.1ms, the slope = 800A / ms). ∫I²dt calculations are performed on the surge segment (accelerated by the FPGA's multiply-accumulate MAC hard core) for contact life prediction, generating instantaneous surge data. The system can also read the heatsink temperature (e.g., the MAX30205) via the I2C interface, dynamically lowering the threshold by 5% for every 10°C increase in temperature. When the voltage fluctuation exceeds 15%, the short-circuit determination is automatically frozen for 500ms to prevent false positives. A three-level segmentation strategy is employed: Level 1 (<100μs): When di / dt exceeds 50A / ms, the CPLD directly drives a SiC MOSFET (e.g., the C3M0065090D) to forcibly disconnect the main circuit. Level 2 (<5ms): After a sustained short-circuit fault is confirmed, the MCU controls the relay to trip and simultaneously triggers the pre-charge resistor to prevent arc reignition. Level 3 (early warning): When the fluctuation exceeds the limit but does not persist, a load reduction command is sent via the CAN bus (such as limiting the BMS current to 50%).

[0109] Preferably, performing surge analysis on the bidirectional charge and discharge circuit based on the accumulated data in the time window in step S34 includes:

[0110] Performing current feature extraction on the smoothed instantaneous current change rate data to obtain current instantaneous change feature data, wherein the current feature extraction includes current change rate, fluctuation amplitude and high-frequency components;

[0111] The instantaneous current change characteristic data is mapped to a high-dimensional space through radial basis function to construct a hyperplane, thereby obtaining a surge hyperplane. The accumulated data in the time window is input into the surge hyperplane for time shift analysis, thereby generating surge time shift data.

[0112] The plane lateral proportion of the surge hyperplane is calculated based on the surge time-shift data to obtain the surge lateral proportion data; based on the surge lateral proportion data, the instantaneous surge intensity of the current monitoring data under the short-circuit trigger mark data is calculated to obtain the instantaneous surge intensity at each time point;

[0113] The instantaneous surge intensity at each time point is aggregated to generate instantaneous surge data.

[0114] In an embodiment of the present invention, three parallel processing channels are constructed within an FPGA: the instantaneous di / dt is calculated using a three-point central difference method (Δt=10μs), the result is output as a 16-bit fixed-point number, sliding window statistics are performed on the current data (window length 100ms), the peak-to-peak value (Max-Min) and standard deviation (σ) are calculated in real time, the high-frequency signal is extracted using a 6th-order Butterworth high-pass filter (cutoff frequency 5kHz), its effective value (RMS) is calculated, and the three-channel output is packaged into a 32-bit feature vector (12-bit di / dt + 10-bit peak-to-peak value + 10-bit high-frequency RMS). A programmable RBF core is configured within the FPGA. 256 basis function center points are pre-stored (generated by clustering historical surge data) using a Gaussian kernel function (σ = 0.5). The distance calculation between the input feature vector and the center point is implemented using a lookup table (LUT). This table stores 4096 pre-calculated Gaussian response values ​​(address lines correspond to quantized Euclidean distance values). In real time, the Euclidean distance between the feature vector and the center point is calculated: → LUT address → kernel output value. The 256 kernel outputs are weighted and summed (weights updated via online learning) to generate an 8-bit hyperplane state value. A 1024-deep FIFO buffer (corresponding to 10.24ms at 100kHz) is constructed to store the hyperplane state sequence within a time window. A sliding window cross-correlation algorithm (window length 50 points) is used to calculate the offset between the current state sequence and the historical template. The FPGA's built-in DSP48 unit computes 50 dot products in parallel, and peak detection is used to determine the maximum correlation offset. A three-dimensional feature space coordinate system (di / dt, peak-to-peak value, high-frequency RMS) is established. Lateral component extraction is performed by projecting the current feature vector onto a plane perpendicular to the hyperplane normal vector using the following formula: lateral vector = original vector - (original vector × normal vector) × normal vector. The squared modulus is calculated using a hard multiplier-accumulator (MAC) core, and the result is normalized to a 0-100% ratio. Based on the lateral ratio (X) and the current di / dt value (Y), a two-dimensional intensity matrix is ​​constructed: intensity = 0.7 × X + 0.3 × (Y / Y_max), where Y_max = 100 A / ms (configurable). The matrix outputs an 8-bit intensity value (0-255), corresponding to a 0-100% surge intensity. Perform a sliding average on the intensity value to output a smoothed surge trend, calculate the area integral of the intensity value (∫intensity dt) for energy assessment, record and latch the maximum intensity value in real time, and continuously output it until manually reset. Package the instantaneous intensity, trend, and integral values ​​into a 64-bit data frame (32-bit timestamp + 16-bit intensity value + 16-bit energy value), and transfer it to the dual-port RAM through the DMA channel for real-time reading by the host computer.

[0115] Preferably, generating a control instruction according to the DC arc determination result and the continuous short circuit fault determination result in step S4 includes:

[0116] Perform high-level analysis on the signal characteristics in the DC arc identification results to generate arc duration and waveform change data;

[0117] Perform real-time pattern recognition on the current waveform in the short-circuit fault identification result to generate short-circuit pattern category data;

[0118] Perform joint spatiotemporal analysis on arc duration and waveform change data and short-circuit mode category data to generate fault synergistic impact data;

[0119] Fuse the fault collaborative impact data in the time and frequency domains to generate comprehensive fault severity data;

[0120] The DC arc identification results and the continuous short circuit fault identification results are redundantly verified by comprehensive fault severity data and control instructions are generated to obtain control instructions.

[0121] In this embodiment of the present invention, high-order features are extracted from the high-frequency disturbance segments of the DC arc detection results. The analysis methods include, but are not limited to, wavelet transform to obtain multi-scale temporal local variations; instantaneous frequency extraction to determine arc onset and extinction moments; and arc envelope feature extraction to analyze arc duration fluctuations. This ultimately generates arc duration and waveform change data, which accurately describes the arcing phenomenon's evolutionary trends and interference characteristics. Based on the persistent short-circuit fault detection results, key current waveform features (such as rising edge slope, stable current amplitude, and oscillation frequency) are extracted. Pattern recognition of the short-circuit signal is performed using a real-time signal classification algorithm (such as a support vector machine (SVM), a convolutional neural network (CNN), or a time series clustering method). Outputs include: short-circuit type (e.g., single-point short, persistent short, intermittent short); and short-circuit response mode category (e.g., sudden, transitional, or oscillatory). Finally, short-circuit mode category data is generated, which can be used to identify the fault evolution path. Arc duration and waveform change data are time-aligned and spatially correlated with short-circuit mode category data. Joint analysis operations include establishing a collaborative interference time window, analyzing the triggering and mutual influence relationships between the two fault types, building a spatiotemporal feature cross-mapping matrix, and extracting coupling behavior characteristics. This output is fault collaborative impact data, which is used to further describe the global state of the system under the influence of the combined faults. Spectral fusion methods (such as the STFT+EMD combined algorithm and spectral energy clustering) are used to analyze the fault collaborative impact data, combined with time-domain metrics (such as mean, variance, and fluctuation rate) for feature fusion analysis. The outputs include: a comprehensive frequency-domain energy distribution; a fault evolution trend curve; and severity level identification labels (such as L1-L4 classification). This yields comprehensive fault severity data, providing precision support for subsequent control strategies. Comprehensive fault severity data is used to cross-validate the DC arc and short-circuit fault identification results. If the two types of results are consistent, the confidence level of the fault judgment is improved. If there are differences in the results, the priority is corrected through the fault model weight mechanism. Based on the verified results, the preset control strategy library is called to automatically match the following instructions: cut off the DC power supply; start the protection device (such as the arc extinguisher); notify the remote system for manual review; and finally output the control instructions to achieve real-time and accurate response of the system to complex fault events.

[0122] Preferably, in step S4, analyzing the contact overcurrent profile of the relay in the bidirectional charge and discharge circuit based on the transient surge data includes:

[0123] Based on the instantaneous surge data, the current monitoring data of the bidirectional charging and discharging circuit is used to capture high-frequency surge events and generate instantaneous surge waveform data;

[0124] Perform FIR bandpass filtering on the instantaneous surge waveform data to generate denoised surge characteristic data;

[0125] Extract the time domain features of the denoised surge characteristic data to obtain surge parameter quantification data;

[0126] Using surge parameter quantification data, the contact current distribution of the relay in the bidirectional charge and discharge circuit is analyzed to generate contact current distribution data. Based on the contact current distribution data, the local contact current propagation of the relay is simulated to generate relay contact current exchange simulation data.

[0127] Perform electron quantum state change analysis on relay contact current exchange simulation data to generate electron quantum state change data;

[0128] The contact wear of the relay contacts is predicted through the data of electron quantum state changes, and the analysis results of the contact overcurrent morphology are generated.

[0129] In embodiments of the present invention, real-time current monitoring data is acquired from a bidirectional charge-discharge circuit, with particular attention paid to transient surge events in the current. This data is typically acquired through real-time acquisition by a current sensor or current probe. Signal processing techniques are used to capture high-frequency surge events in the current signal. Using transient surge data acquisition technology, high-frequency components of the surge signal can be identified in real time. These components typically contain sudden or transient current changes, such as short-term overcurrents or short pulses. A high-frequency capture algorithm is used to quickly extract and process the sudden change regions in the current waveform, generating accurate transient surge waveform data that provides a foundation for subsequent filtering and feature extraction. The generated transient surge waveform data is then passed through an FIR bandpass filter. The FIR bandpass filter filters out unwanted low- and high-frequency noise in the current signal, which typically does not contribute to current feature analysis. The surge information in the mid-frequency band of the signal is of primary interest. The bandpass filter is configured with an appropriate frequency band to ensure that irrelevant noise is filtered out through the mid-frequency bandpass signal. After filtering, the surge characteristics of the signal are preserved while removing unnecessary noise. The filtered data is the denoised surge characteristic data. This data is clearer and reduces interference, facilitating subsequent time-domain feature extraction. Time-domain analysis is performed on the denoised surge characteristic data to extract key parameters from the current waveform. These time-domain features, including peak current, overcurrent duration, waveform amplitude, rise time, and decay time, directly reflect the transient behavior of the current. The extracted time-domain features are quantified and converted into standardized surge parameter data. This parameter data clearly quantifies the behavior of the current signal, facilitating subsequent analysis and comparison. Based on the quantized surge parameter data, the current distribution at the relay contacts is calculated using electromagnetic field simulation models or circuit analysis software. The current flow characteristics at the contacts can be used to determine the current density and distribution, identifying areas of the contacts experiencing high current. The spatial distribution of the contact current is analyzed to generate contact current distribution data, which will be useful for subsequent contact wear analysis and current propagation simulation. Based on this contact current distribution data, local current propagation simulation of the relay contacts is performed using circuit simulation software. The simulation investigates the exchange and propagation of current between contacts, including its direction, intensity, and interaction with the contact surface. Factors such as contact impedance and surface friction are considered during the simulation to generate simulated data for the relay contact current exchange. This data helps understand the dynamic effects of current exchange, such as contact heating and arcing. Utilizing the principles of electron quantum physics, the simulation data for relay contact current exchange analyzes the quantum state changes of electrons during current transmission. This considers current-induced heating and arcing, as well as the scattering and acceleration of electrons at the contact points.Using quantum physics models and electronic state change theory, the electron state evolution process is analyzed and data is generated. This data reflects the microscopic behavior and physical effects of electrons during contact exchange. Based on this data, combined with the current distribution in the relay contacts, contact current exchange simulation results, and quantum state analysis, a wear prediction model for relay contacts is constructed. This model considers contact current, electron state changes, and their physical effects on the contact surface. This model calculates the degree and rate of relay contact wear, as well as its potential impact on current transmission efficiency. The generated analysis results can help identify potential overcurrent risks and provide an assessment of relay contact life. Relay contact wear prediction generates comprehensive contact overcurrent profile analysis results. These results include predicted contact wear and changes in contact performance under different operating conditions. This data provides valuable support for relay design optimization and maintenance.

[0130] Of particular importance is the simulation of local contact current propagation in relays based on contact current distribution data, which also includes:

[0131] Based on the contact current distribution data, the relay is subjected to collective empirical mode decomposition to generate an intrinsic mode function (IMF) set. The IMF set data is subjected to Hilbert spectrum analysis to generate time-frequency energy distribution data.

[0132] Perform phase space reconstruction and recursive network construction on the time-frequency energy distribution data to generate recursive network adjacency matrix data; perform continuous homology analysis on the recursive network adjacency matrix data to generate topological characteristic Betti number sequence data;

[0133] Perform fractional differentiation on the Betti number sequence data to generate fractional current diffusion response data; perform transfer entropy calculation on the fractional current diffusion response data to generate directional coupling coefficient matrix data;

[0134] Monte Carlo sampling is performed on the directional coupling coefficient matrix data to generate relay contact current exchange simulation data.

[0135] In one embodiment of the present invention, current sampling devices are placed on each relay contact array to synchronously record the current time series of each contact at a fixed sampling frequency (e.g., 10,000 times per second). A bandpass filter is first used to remove DC offset and noise far above or below the target frequency band. The filtered data is then subtracted from its mean and divided by its standard deviation to align the amplitude scales across all channels for subsequent analysis. For each preprocessed current signal, multiple groups of random noise are repeatedly added and traditional empirical mode decomposition is performed. The first mode in the noise-assisted decomposition results is averaged to obtain the first intrinsic mode function (IMF) of this round. The remaining signal after removing the first mode is subjected to the same steps until the remaining signal becomes monotonic or falls below the noise level. The number of noise groups can be hundreds, and the noise amplitude is set to approximately one-twentieth of the original signal amplitude to ensure decomposition stability and accuracy. For each resulting IMF, the Hilbert transform technique is applied to combine its real and imaginary parts to generate the corresponding analytical signal. The instantaneous amplitude and frequency are extracted from the analytical signal to characterize the temporal evolution of the intensity and frequency of the current oscillations. The squared instantaneous amplitudes of all modal functions are summed at the corresponding time-frequency locations to form a two-dimensional data matrix reflecting the dual distribution of energy over time and frequency. Appropriate time delays and embedding dimensions are selected, and a sliding window approach is used to convert the time-frequency energy sequence into a series of multidimensional vectors, obtaining the system's state-space trajectory. The reconstructed multidimensional state vector sequence is then fed into a gated recurrent unit or long-short-term memory network for training, enabling the network to capture the temporal dependence and dynamics of the sequence. Based on the trained hidden layer outputs of the network, the similarity between any two hidden states is calculated, and a weighted matrix is ​​formed based on the similarity, serving as the adjacency matrix of the recurrent network. Based on the adjacency matrix, a filtering process is gradually constructed using a series of similarity thresholds, generating a multi-layer network structure from the most dense to the most loose. At each threshold level, the number of connected branches and loops in the network is extracted, and these topological features, which vary with the threshold, are recorded to form a continuous sequence of Betti numbers. Using the Betti number sequence as input, the fractional-order difference method is applied to perform differentiation operations on the sequence with long-term memory properties, yielding a new sequence reflecting the diffusion response of the topological features. A fractional order between zero and one (e.g., 0.5) is chosen to balance sensitivity to short-term fluctuations and long-term trends. For each pair of channels, a metric measuring the amount of information transferred from channel A to channel B is calculated based on their fractional diffusion response sequence, revealing the direction of dynamic causal influence. The transmission entropy values ​​of all channel pairs are permuted and combined to form a complete directional coupling coefficient matrix, where each entry represents a specific current propagation intensity and direction. A probability distribution (e.g., normal distribution or kernel density estimation) is fitted to the directional coupling coefficient matrix, and sample matrices are randomly drawn from it multiple times.The same initial current distribution is set for each sampling matrix, and the current exchange process is iterated for a fixed number of steps. At each step, the current vector is multiplied by the sampling matrix to update the current distribution. The current distribution results for all sampling cases at each iteration are collected, and statistics such as the mean curve, variance, maximum and minimum values ​​are calculated to form the final simulation data set. Finally, a complete simulation report is generated, including time-frequency energy plots, Betti number persistence plots, coupling coefficient heat maps, and Monte Carlo distribution statistics.

[0136] Of particular importance is the analysis of electron quantum state changes in relay contact current exchange simulation data, which also includes:

[0137] Extract the initial value of the quantum state of the relay contact current exchange simulation data;

[0138] Analyze the electron energy level distribution of the initial value of the quantum state to generate electron energy level change data;

[0139] Perform quantum state time evolution simulation on the electron energy level change data to generate electron quantum state time change data;

[0140] Performing electron spin state analysis on the electron quantum state time variation data to generate electron spin variation data;

[0141] The electron spin change data is evaluated for quantum state changes to generate electron quantum state change evaluation data.

[0142] In this embodiment of the present invention, based on the simulation of relay contact current exchange, the correspondence between current signals and quantum states must first be established. By measuring the current data of the relay contacts, the electronic state of each contact at the initial moment can be inferred. Based on the current exchange process, the initial value of the quantum state related to the current can be extracted. At this point, the quantum state can be mapped to the state of the quantum bit using information such as the magnitude and phase of the contact current, preliminarily constructing the quantum state parameters of each contact, such as the electron energy and spin. Based on the principles of quantum mechanics, an energy distribution model of the quantum state is established. The current data of each relay contact is mapped to a specific energy level. The contact current data is subjected to a Fourier transform or wavelet transform to extract the frequency components related to energy. The energy information obtained from the frequency domain analysis is matched with the energy levels of the quantum state. Through statistical analysis, the distribution of each quantum state at different energy levels is determined. By analyzing the time evolution of the relay contact current exchange, a curve of the electron energy level over time is plotted to generate electron energy level change data. The quantum state evolution is simulated using the time-dependent Schrödinger equation, taking into account external influences caused by the current exchange (such as changes in the electric and magnetic fields). The initial quantum state is simulated over time, calculating the electron energy distribution and state changes at each time point. The quantum state evolution at different time steps is sampled, and changes in electron energy, phase, and spin state are recorded to generate complete quantum state time evolution data. Based on the mathematical representation of the quantum state, the electron spin component at each time step is extracted. Electron spin states are typically represented by the wave function of quantum bits, and the spin direction of each electron can be extracted from the quantum state. Quantum physics methods such as the spin Hall effect and the spin transfer torque effect are used to analyze the impact of the current exchange process on electron spin and detect changes in spin over time. By combining simulation and experimental data, the temporal trend of the spin state is determined, and a spin change curve is plotted to generate electron spin change data. The quantum state evolution process is evaluated for stability. The analysis is conducted to determine whether the quantum state evolution tends to be stable or whether fluctuations, chaos, or decoherence occur at certain time points. Using quantum information theory metrics such as quantum entanglement and quantum coherence, it is assessed whether changes in the quantum state during current exchange across the relay contacts lead to loss or degradation of quantum information. The evaluation results are quantified through statistical methods to generate evaluation data of electronic quantum state changes, including indicators such as quantum state stability, entanglement change, and coherence loss.

[0143] Preferably, in step S4, optimizing the on-time control of the control instruction according to the analysis result includes:

[0144] The analysis results of the contact overcurrent morphology are processed by accumulating the overcurrent duration to generate the total contact overcurrent duration data;

[0145] Perform interval comparison processing on the total contact overcurrent duration data to generate contact overcurrent trend data;

[0146] Extract the conduction signal limit of the contact overcurrent trend data and generate the contact conduction signal allowable duration data, where the contact conduction signal allowable duration data includes the maximum single duration and the minimum interval time;

[0147] Optimize the circuit continuous conduction time of the control instruction according to the maximum single duration and minimum interval time, and generate conduction time limit parameter data;

[0148] The on-time limit parameter data is used to adjust the on-time control of the control instruction to perform safety control optimization operations of the bidirectional charging and discharging circuit.

[0149] In an embodiment of the present invention, a current monitoring system collects real-time current data from relay contacts in a bidirectional charge-discharge circuit. This data includes the current waveform during each contact conduction cycle. When the contact current exceeds a set overcurrent threshold, the duration of the overcurrent event is recorded and labeled as the "overcurrent duration." Based on this, the duration of all overcurrent events is accumulated to generate total contact overcurrent duration data. For example, during analysis of an actual charge-discharge circuit, it was found that the relay contacts frequently experienced current surges during multiple charge-discharge cycles. Each overcurrent event lasted for 10 milliseconds, 8 milliseconds, 12 milliseconds, and so on. Adding these durations resulted in a total contact overcurrent duration of 30 milliseconds. Further analysis of the total contact overcurrent duration data was performed, comparing overcurrent events according to specific time intervals (e.g., hourly or minutely). This approach can identify patterns in which the contacts experience significant overcurrent loads during certain time periods. For example, a specific analysis revealed that the relay contacts experienced a high number of overcurrent events between 8:00 AM and 10:00 AM, with a total overcurrent duration of 20 milliseconds. However, between 3:00 PM and 5:00 PM, the overcurrent duration decreased significantly, to only 5 milliseconds. This interval comparison yields contact overcurrent trend data, which tracks the changes in contact overcurrent over different time periods. Based on this contact overcurrent trend data, the constraints for the conduction signal can be extracted. Specifically, the following needs to be calculated: Maximum single conduction duration: The maximum duration the relay contacts can safely withstand during each conduction. Analysis of historical overcurrent data revealed that overcurrents exceeding 15 milliseconds accelerate contact wear. Therefore, a maximum single conduction duration of 15 milliseconds is set. Minimum interval: The minimum time interval required between conduction events. Suppose, for example, that the contact temperature rises excessively when the interval between conduction events is less than 2 seconds, leading to overcurrent damage. Therefore, a minimum interval of 2 seconds is set. Based on the above settings, data for the allowed duration of the contact conduction signal is generated, including a maximum single duration of 15 milliseconds and a minimum interval of 2 seconds. Based on the generated maximum single duration and minimum interval, the control instructions are optimized. In circuit design and control systems, PWM (pulse width modulation) signals are typically used to control the relay's conduction duration. Therefore, using these parameters as constraints, the on-time settings in the control instructions are optimized. An optimization algorithm calculates appropriate on-time limit parameters to ensure a maximum duration of 15 milliseconds per conduction and a minimum interval of 2 seconds between conductions. Thus, the on-time limit parameter data in the control system is set to: maximum single duration = 15 milliseconds, minimum interval = 2 seconds. Using this generated on-time limit parameter data, the control system instructions are adjusted. Specifically, in the bidirectional charge and discharge circuit, the relay's conduction control signal is adjusted based on the optimized parameters.By limiting the maximum single-time conduction duration to 15 milliseconds and setting a minimum interval of 2 seconds between two conductions, the relay contacts are protected from overcurrent damage caused by prolonged continuous conduction, effectively extending the relay's service life and improving system stability. For example, during actual operation, the system automatically adjusts the conduction time based on these settings. When a contact receives a conduction command while in operation, the control system determines whether the conduction duration meets the maximum single-time duration requirement. If it exceeds 15 milliseconds, the conduction is automatically interrupted and a minimum interval of 2 seconds is required before it can be resumed, thus preventing the risk of overcurrent in the relay contacts.

[0150] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0151] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A control method for a bidirectional charge and discharge circuit, characterized in that: The following steps are involved: Step S1: real-time acquisition of current monitoring data and voltage monitoring data of the bidirectional charge and discharge circuit; Perform arc characteristic analysis on the current monitoring data and the voltage monitoring data to obtain arc characteristic data; Step S2: analyzing the high-frequency harmonic components and current ripple fluctuation amplitude of the bidirectional charge and discharge circuit based on the arc characteristic data; performing DC arc detection on the bidirectional charge and discharge circuit based on the high-frequency harmonic components and current ripple fluctuation amplitude, and generating a DC arc fault determination result; Step S3: Calculate the instantaneous current change rate of the current monitoring data and perform short circuit detection on the bidirectional charge and discharge circuit based on the instantaneous current change rate. When a current change that continuously exceeds a preset threshold is detected, a time window accumulation analysis is initiated to generate instantaneous surge data and a continuous short circuit fault determination result. Step S4: generating a control instruction according to the DC arc determination result and the continuous short circuit fault determination result; Based on the transient surge data, the contact overcurrent profile of the relay in the bidirectional charge and discharge circuit is analyzed, and the on-time control of the control instruction is optimized according to the analysis result, so as to perform the safety control optimization operation of the bidirectional charge and discharge circuit; wherein, in step S4, the control instruction is generated according to the DC arc judgment result and the continuous short circuit fault judgment result, including: Perform high-level analysis on the signal characteristics in the DC arc identification results to generate arc duration and waveform change data; Perform real-time pattern recognition on the current waveform in the short-circuit fault identification result to generate short-circuit pattern category data; Perform joint spatiotemporal analysis on arc duration and waveform change data and short-circuit mode category data to generate fault synergistic impact data; Fuse the fault collaborative impact data in the time and frequency domains to generate comprehensive fault severity data; The DC arc determination result and the continuous short circuit fault determination result are redundantly verified by comprehensive fault severity data and a control instruction is generated to obtain a control instruction; wherein, in step S4, analyzing the contact overcurrent morphology of the relay in the bidirectional charge and discharge circuit based on the transient surge data includes: Based on the instantaneous surge data, the current monitoring data of the bidirectional charging and discharging circuit is used to capture high-frequency surge events and generate instantaneous surge waveform data; Perform FIR bandpass filtering on the instantaneous surge waveform data to generate denoised surge characteristic data; Extract the time domain features of the denoised surge characteristic data to obtain surge parameter quantification data; Using surge parameter quantification data, the contact current distribution of the relay in the bidirectional charge and discharge circuit is analyzed to generate contact current distribution data. Based on the contact current distribution data, the local contact current propagation of the relay is simulated to generate relay contact current exchange simulation data. Perform electron quantum state change analysis on relay contact current exchange simulation data to generate electron quantum state change data; Contact wear of the relay contacts is predicted by using the electron quantum state change data to generate an analysis result of the contact overcurrent morphology; wherein, in step S4, the on-time control optimization of the control instruction according to the analysis result includes: The analysis results of the contact overcurrent morphology are processed by accumulating the overcurrent duration to generate the total contact overcurrent duration data; Perform interval comparison processing on the total contact overcurrent duration data to generate contact overcurrent trend data; Extract the conduction signal limit of the contact overcurrent trend data and generate the contact conduction signal allowable duration data, where the contact conduction signal allowable duration data includes the maximum single duration and the minimum interval time; Optimize the circuit continuous conduction time of the control instruction according to the maximum single duration and minimum interval time, and generate conduction time limit parameter data; The on-time limit parameter data is used to adjust the on-time control of the control instruction to perform safety control optimization operations of the bidirectional charging and discharging circuit.

2. The control method of the bidirectional charge and discharge circuit according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting current monitoring data and voltage monitoring data of the bidirectional charge and discharge circuit in real time; Step S12: performing time alignment on the current monitoring data and the voltage monitoring data to generate time-aligned current and voltage monitoring data; performing differential operation on the time-aligned current and voltage monitoring data to generate current change rate data and voltage change rate data; Step S13: performing mutation edge detection on the current change rate data and the voltage change rate data to generate suspicious arc trigger point data; extracting the time window of the suspicious arc trigger point data, and performing envelope analysis on the current and voltage monitoring data according to the time window to generate arc voltage waveform feature data; Step S14: performing spectrum analysis on the arc voltage waveform characteristic data to generate arc frequency domain characteristic data; performing key feature screening processing on the arc frequency domain characteristic data to generate arc characteristic data.

3. The control method of the bidirectional charge and discharge circuit according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing spectrum decomposition processing on the arc characteristic data, extracting the high-frequency harmonic components of the current signal, and generating high-frequency harmonic data; performing amplitude analysis on the high-frequency harmonic data, and generating high-frequency harmonic amplitude data; Step S22: performing time domain waveform analysis on the current monitoring data, calculating the current ripple fluctuation amplitude, and generating current ripple fluctuation data; calculating the arc characteristic index of the circuit based on the high-frequency harmonic amplitude data and the current ripple fluctuation data, and generating arc characteristic index data; Step S23: using the arc characteristic index data to perform DC arc detection on the bidirectional charge and discharge circuit to generate a DC arc fault determination result.

4. The control method of the bidirectional charge and discharge circuit according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: When the arc characteristic index shows any of the following conditions, it is determined to be a DC arc fault and the first fault discrimination data is obtained: the arc characteristic index is greater than 5.0; the current fluctuation exceeds ±10% and lasts for more than 3 seconds; the arc frequency is greater than 50 times / minute; the discharge voltage is greater than 400V or less than 200V; Step S232: When the relationship between the arc characteristic index and the arc occurrence frequency meets the following conditions, generating second fault discrimination data: the arc characteristic index is greater than 6.0 and the arc occurrence frequency is greater than 60 times / minute; the fluctuation range of the arc characteristic index is greater than ±20%, and the current value fluctuation range exceeds ±15%; Step S233: When determining a DC arc fault in the bidirectional charge and discharge circuit, third fault determination data is generated if the following conditions occur: the current is greater than 50A or the voltage is greater than 500V and the duration exceeds 60 seconds; the current fluctuation is greater than ±20% and the voltage fluctuation is greater than ±15%, and the duration of these fluctuations exceeds 30 seconds; Step S234: Integrate the first fault determination data, the second fault determination data, and the third fault determination data to ultimately generate a DC arc fault determination result.

5. The control method of the bidirectional charge and discharge circuit according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing numerical differentiation calculation on the current monitoring data to obtain instantaneous current change rate data; performing smoothing filtering on the instantaneous current change rate data to generate smoothed instantaneous current change rate data; Step S32: Compare the smoothed instantaneous current change rate data with the short circuit determination threshold value. When the smoothed instantaneous current change rate data is greater than or equal to the short circuit determination threshold value, generate short circuit trigger flag data. Step S33: Performing a Boolean check on the short-circuit trigger flag data. When the short-circuit trigger flag data is true, starting a time window cumulative analysis to analyze the fluctuation amplitude of the current monitoring data under the short-circuit trigger flag data within the time window to generate time window cumulative data. Step S34: Perform continuity analysis on the accumulated data in the time window. If the current change amplitude in the accumulated data in the time window is greater than 20A or the current change duration is greater than 30s, a continuous short circuit fault judgment result is generated; based on the accumulated data in the time window, a surge analysis is performed on the bidirectional charging and discharging circuit to generate instantaneous surge data.

6. The control method of the bidirectional charge and discharge circuit according to claim 4, characterized in that: The surge analysis of the bidirectional charge and discharge circuit based on the accumulated data in the time window in step S34 includes: Performing current feature extraction on the smoothed instantaneous current change rate data to obtain current instantaneous change feature data, wherein the current feature extraction includes current change rate, fluctuation amplitude and high-frequency components; The instantaneous current change characteristic data is mapped to a high-dimensional space through radial basis function to construct a hyperplane, thereby obtaining a surge hyperplane. The accumulated data in the time window is input into the surge hyperplane for time shift analysis, thereby generating surge time shift data. The plane lateral proportion of the surge hyperplane is calculated based on the surge time-shift data to obtain the surge lateral proportion data; based on the surge lateral proportion data, the instantaneous surge intensity of the current monitoring data under the short-circuit trigger mark data is calculated to obtain the instantaneous surge intensity at each time point; The instantaneous surge intensity at each time point is aggregated to generate instantaneous surge data.

7. A control system for a bidirectional charge and discharge circuit, characterized in that: For executing the control method of the bidirectional charge and discharge circuit according to claim 1, the control system of the bidirectional charge and discharge circuit comprises: The arc feature extraction module is used to collect the current monitoring data and voltage monitoring data of the bidirectional charging and discharging circuit in real time; perform arc feature analysis on the current monitoring data and voltage monitoring data to obtain arc feature data; An arc fault identification module is used to analyze the high-frequency harmonic components and current ripple fluctuation amplitude of the bidirectional charge and discharge circuit based on arc characteristic data; perform DC arc detection on the bidirectional charge and discharge circuit based on the high-frequency harmonic components and current ripple fluctuation amplitude, and generate a DC arc fault identification result; The short-circuit detection module is used to calculate the instantaneous current change rate of the current monitoring data and perform short-circuit detection on the bidirectional charge and discharge circuit based on the instantaneous current change rate. When a current change that continuously exceeds a preset threshold is detected, a time window accumulation analysis is initiated to generate instantaneous surge data and a continuous short-circuit fault detection result. The contact conduction control module is used to generate control instructions based on the DC arc judgment results and the continuous short-circuit fault judgment results; based on the transient surge data, it analyzes the contact overcurrent morphology of the relay in the bidirectional charge and discharge circuit, and optimizes the conduction time control of the control instructions based on the analysis results to perform safety control optimization operations for the bidirectional charge and discharge circuit.

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