Efficient energy storage connector dynamic regulation and control system based on nonlinear coupling mechanism

Abstract

The invention relates to the technical field of connector regulation and control, and discloses an efficient energy storage connector dynamic regulation and control system based on a nonlinear coupling mechanism. The system comprises an establishment module used for establishing an energy storage connector system model based on a voltage signal and a current signal of an energy storage connector; the disturbance identification module is used for performing nonlinear coupling disturbance identification on the energy transmission process according to the energy storage connector system model to obtain an additional coupling loss component, a dynamic impedance drift component and a phase coupling offset component; the dynamic observation module is used for performing weighted analysis through a nonlinear coupling dynamic observer to obtain a coupling mechanism observation result; and the generation module is used for generating an energy transmission path regulation and control instruction containing the conduction angle compensation amount and the phase synchronization compensation amount based on the coupling mechanism observation result. According to the invention, stable operation can be realized under complex working conditions such as load abrupt change and coupling impedance fluctuation, and the transmission efficiency is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of connector control, and in particular to a high-efficiency energy storage connector dynamic control system based on a nonlinear coupling mechanism. Background Art

[0002] As a key component for energy transmission within energy storage systems, the performance of efficient energy storage connectors directly impacts the efficiency and stability of the entire system. Modern energy storage connectors must withstand frequent load fluctuations and electrical parameter changes in complex and changing operating environments while ensuring high efficiency and low loss during energy transmission.

[0003] However, existing energy storage connector control technologies primarily rely on traditional linear impedance matching methods, which are unable to effectively address the nonlinear coupling phenomena generated during the operation of energy storage connectors. Traditional methods require additional impedance matching sensors or complex power factor compensation designs to monitor and adjust system parameters. This not only increases system hardware complexity and cost, but also makes it difficult to achieve precise real-time control in the face of dynamically changing coupling impedances and nonlinear perturbations, resulting in reduced energy storage efficiency and insufficient system reliability. Summary of the Invention

[0004] The main purpose of the present invention is to provide a high-efficiency energy storage connector dynamic control system based on a nonlinear coupling mechanism. The present invention can operate stably under complex working conditions such as load mutations and coupling impedance fluctuations, thereby maximizing transmission efficiency.

[0005] To achieve the above objectives, the present invention provides a high-efficiency energy storage connector dynamic control system based on a nonlinear coupling mechanism, comprising the following steps:

[0006] Establishing a module for establishing an energy storage connector system model based on a voltage signal and a current signal of the energy storage connector;

[0007] A disturbance identification module is used to identify nonlinear coupling disturbances in the energy transmission process according to the energy storage connector system model, and obtain an additional coupling loss component, a dynamic impedance drift component, and a phase coupling offset component;

[0008] A dynamic observation module, configured to input the additional coupling loss component, the dynamic impedance drift component, and the phase coupling offset component into a nonlinear coupling dynamic observer for weighted analysis to obtain a coupling mechanism observation result;

[0009] A generation module is used to generate an energy transmission path control instruction including a conduction angle compensation amount and a phase synchronization compensation amount based on the coupling mechanism observation result.

[0010] In combination with the first aspect, in a first implementation of the first aspect of the present invention, the establishing module further includes:

[0011] A frequency domain analysis unit, used to perform frequency domain analysis on the voltage signal of the energy storage connector to obtain voltage characteristic parameters;

[0012] an effective value calculation unit, configured to perform effective value calculation on the current signal of the energy storage connector to obtain current characteristic parameters;

[0013] A dynamic impedance calculation unit, configured to calculate a dynamic impedance parameter of the energy storage connector according to the voltage characteristic parameter and the current characteristic parameter;

[0014] A transmission relationship modeling unit is used to model the power transmission relationship of the energy storage connector based on the dynamic impedance parameter to obtain an energy storage connector system model.

[0015] In combination with the first aspect, in a second implementation of the first aspect of the present invention, the transmission relationship modeling unit is specifically configured to:

[0016] Constructing a power transmission basic relationship including an input power term and an output power term according to the dynamic impedance parameter;

[0017] Performing nonlinear loss calculation on the coupling loss characteristics of the energy storage connector to obtain nonlinear coupling loss parameters;

[0018] The power transmission basic relationship is processed by adding a coupling impedance change term based on the nonlinear coupling loss parameter to obtain a power transmission coupling relationship, and an energy storage connector system model reflecting the dynamic change characteristics of the energy storage connector coupling impedance is created based on the power transmission coupling relationship.

[0019] In combination with the first aspect, in a third implementation of the first aspect of the present invention, the disturbance identification module is specifically configured to:

[0020] Performing real-time power monitoring of the energy transmission process based on the energy storage connector system model to obtain power difference data;

[0021] Separating and identifying nonlinear coupling disturbance features according to the power difference data to obtain disturbance feature classification results including loss disturbance, impedance disturbance, and phase disturbance;

[0022] Performing a correlation calculation process on the loss-type disturbance in the disturbance feature classification result to obtain an additional coupling loss component related to the nonlinear coupling characteristic of the energy storage connector;

[0023] Calculating the load change rate of the impedance disturbance in the disturbance feature classification result to obtain a dynamic impedance drift component;

[0024] A phase offset is calculated for the phase-type disturbance in the disturbance feature classification result to obtain a phase coupling offset component.

[0025] In combination with the first aspect, in a fourth implementation of the first aspect of the present invention, the dynamic observation module further includes:

[0026] a disturbance intensity analysis unit, configured to input the additional coupling loss component, the dynamic impedance drift component, and the phase coupling offset component into a nonlinear coupling dynamic observer to perform disturbance intensity analysis and obtain disturbance intensity data;

[0027] A weight coefficient calculation unit, configured to calculate observer weight coefficients based on the disturbance intensity data to obtain adaptive weight coefficient groups corresponding to the additional coupling loss component, the dynamic impedance drift component, and the phase coupling offset component, respectively;

[0028] The parallel observation unit is used to perform parallel observation on the loss observation channel, impedance drift observation channel and phase offset observation channel of the nonlinear coupling dynamic observer based on the adaptive weight coefficient group to obtain a coupling mechanism observation result.

[0029] In combination with the first aspect, in a fifth implementation of the first aspect of the present invention, the parallel observation unit is specifically configured to:

[0030] performing weight configuration processing on the loss observation channel, the impedance drift observation channel, and the phase offset observation channel of the nonlinear coupling dynamic observer according to the adaptive weight coefficient group to obtain a channel configuration parameter group;

[0031] Performing Kalman filtering observation on the additional coupling loss component based on the loss channel weight parameter in the channel configuration parameter group to obtain additional coupling loss observation data;

[0032] Performing extended Kalman filtering observation on the dynamic impedance drift component based on the impedance drift channel weight parameter in the channel configuration parameter group to obtain dynamic impedance drift observation data;

[0033] Performing phase-locked loop tracking observation on the phase coupling offset component based on the phase offset channel weight parameter in the channel configuration parameter group to obtain phase coupling offset observation data;

[0034] The additional coupling loss observation data, the dynamic impedance drift observation data, and the phase coupling offset observation data are weightedly fused to obtain a coupling mechanism observation result.

[0035] In combination with the first aspect, in a sixth implementation of the first aspect of the present invention, the generating module further includes:

[0036] A feedforward compensation unit, configured to calculate a feedforward compensation gain based on the coupling mechanism observation result to obtain a feedforward compensation gain group including a loss compensation gain, an impedance drift compensation gain, and a phase offset compensation gain;

[0037] a compensation angle calculation unit, configured to perform compensation angle calculation on the basic conduction angle of the energy storage connector based on the feedforward compensation gain group to obtain a conduction angle compensation amount;

[0038] a synchronous adjustment unit, configured to synchronously adjust the reference phase angle according to the phase coupling offset observation data in the coupling mechanism observation result to obtain a phase synchronization compensation amount;

[0039] An instruction generating unit is used to generate an energy transmission path control instruction including a switch tube control parameter and a phase locking parameter according to the conduction angle compensation amount and the phase synchronization compensation amount.

[0040] In combination with the first aspect, in a seventh implementation of the first aspect of the present invention, the synchronization adjustment unit is specifically configured to:

[0041] Extracting the phase offset amplitude from the phase coupling offset observation data in the coupling mechanism observation results to obtain a phase offset characteristic parameter;

[0042] Adaptively calculating the synchronization adjustment coefficient based on the phase offset characteristic parameter to obtain the phase synchronization adjustment coefficient;

[0043] Performing offset compensation on the reference phase angle of the energy storage connector according to the phase synchronization adjustment coefficient to obtain a target phase angle;

[0044] A phase difference is calculated based on the target phase angle and the reference phase angle to obtain a phase synchronization compensation amount.

[0045] In combination with the first aspect, in an eighth implementation of the first aspect of the present invention, the present invention further includes:

[0046] a coordination control module configured to perform conduction angle adjustment control on the switch tube of the energy storage connector based on the switch tube control parameters in the energy transmission path control instruction to obtain a switch tube operating state with conduction angle compensation; perform phase synchronization control on the phase locking unit of the energy storage connector based on the phase locking parameters in the energy transmission path control instruction to obtain a phase locking operating state with phase synchronization compensation; and perform coordinated synchronization based on the switch tube operating state and the phase locking operating state to obtain an energy storage transmission control state.

[0047] In combination with the first aspect, in a ninth implementation of the first aspect of the present invention, the present invention further includes:

[0048] A bandwidth optimization module is configured to monitor the transmission efficiency of the energy storage connector under conditions of sudden energy storage load changes and coupling impedance fluctuations based on the energy storage transmission control state, thereby obtaining transmission efficiency monitoring data including transmission efficiency values ​​for multiple conditions; perform carrier frequency and harmonic order analysis on the correlation between the coupling observation bandwidth and the transmission efficiency based on the transmission efficiency monitoring data, thereby obtaining bandwidth efficiency relationship data reflecting the matching characteristics of the observation bandwidth and the transmission efficiency; perform a switching frequency correlation search on the optimal observation bandwidth based on the bandwidth efficiency relationship data, thereby obtaining optimal observation bandwidth parameters that maximize the transmission efficiency of the energy storage connector; and perform transmission efficiency optimization on the operating parameters of the energy storage connector based on the optimal observation bandwidth parameters, thereby obtaining optimal transmission efficiency operating parameters suitable for conditions of sudden load changes and impedance fluctuations.

[0049] In summary, the technical solution provided by the present invention can achieve accurate identification of nonlinear coupling mechanisms by establishing an energy storage connector system model based on voltage and current signals without the need for additional impedance matching sensors, and refines the impact of nonlinear coupling on the energy storage system into three specific disturbance types: additional coupling loss component, dynamic impedance drift component, and phase coupling offset component. Compared with the rough processing method of the prior art that only considers power factor compensation, it achieves refined identification and quantitative analysis of the disturbance mechanism. The nonlinear coupling dynamic observer of the present invention adopts a parallel observation architecture of loss observation channel, impedance drift observation channel, and phase offset observation channel, combined with an adaptive weight adjustment mechanism, which can dynamically optimize the observation accuracy according to the actual coupling state, overcoming the problem of insufficient adaptability caused by fixed observation parameters in the prior art. The conduction angle compensation amount and phase synchronization compensation amount are generated by the feedforward compensation algorithm, realizing coordinated control of the energy storage connector switch tube and the phase locking unit. It is fully compatible with the existing controller and does not require the replacement of the existing energy storage power regulation controller, thereby improving the engineering applicability of the system. This invention establishes a complete robust stability analysis framework. Lyapunov stability theory and linear matrix inequality optimization are used to ensure stable operation of the system under complex operating conditions such as sudden load changes and coupled impedance fluctuations. This significantly improves system reliability compared to existing technologies that lack systematic stability assurance. This invention automatically optimizes the coupled observation bandwidth through carrier frequency and harmonic order analysis, establishing a matching relationship between observation bandwidth and transmission efficiency. The system can automatically adjust to the optimal observation bandwidth under different operating conditions, maximizing transmission efficiency and avoiding the efficiency loss caused by fixed bandwidth in existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a structural block diagram of a high-efficiency energy storage connector dynamic control device based on a nonlinear coupling mechanism in one embodiment of the present invention.

[0051] 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

[0052] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0053] Reference Figure 1 This embodiment provides a high-efficiency energy storage connector dynamic control system based on a nonlinear coupling mechanism, including the following steps:

[0054] S1, establishing module 001, for establishing an energy storage connector system model based on the voltage signal and current signal of the energy storage connector;

[0055] The frequency domain analysis unit converts the time-domain sampled data of the energy storage connector voltage signal into a frequency domain representation by performing frequency-domain mapping techniques such as Fourier transform or wavelet transform. This unit extracts multiple voltage characteristic parameters, including the amplitude of the main frequency component, harmonic distribution, and frequency offset. This parameter set reflects the fundamental frequency response of the system and reveals the energy distribution characteristics of nonlinear perturbations in the frequency domain, thereby identifying harmonic amplification or frequency drift caused by coupling distortion. Simultaneously, the effective value calculation unit performs periodic integration analysis on the current signal of the energy storage connector, calculating the RMS value of the current in real time over the entire current waveform period to obtain current characteristic parameters reflecting the charge transfer intensity. The dynamic impedance calculation unit utilizes the amplitude-to-phase relationship between the aforementioned frequency-domain voltage characteristic parameters and the effective value current parameters to construct a dynamic impedance representation based on the definition. This representation captures the nonlinear impedance behavior over time and frequency caused by factors such as load changes, electromagnetic disturbances, or contact instability. By constructing a complex form of the impedance that includes both its amplitude and phase response information, it more accurately characterizes the dynamic electrical characteristics of the coupling mechanism. On this basis, the transmission relationship modeling unit uses the dynamic impedance parameters as the core modeling variables, and combines the coupling change patterns between input power, voltage, current and output power to establish an energy storage connector system model with dynamic response capabilities. By introducing nonlinear coupling loss functions, phase mismatch terms and dynamic feedback channels, it realizes comprehensive modeling of power delivery efficiency, stability and anti-interference in the entire energy transmission path, and finally generates a mathematical model of the energy storage connector system.

[0056] In this embodiment, the modeling process uses dynamic impedance parameters as the modeling benchmark to establish the basic power transmission relationship between the input and output of the energy storage connector. The input power term is constructed based on real-time voltage and current measurements, while the output power is modeled in combination with the power receiving state of the load end. The initial power balance expression between the two provides a transmission model framework under ideal conditions. Considering that non-ideal coupling states are widely present in energy storage connectors during actual operation, especially under conditions such as electromagnetic disturbances, load switching, or structural thermal expansion, the coupling impedance of the connector will experience nonlinear changes that fluctuate over time, thereby causing additional energy loss. Therefore, modeling of coupling loss characteristics is introduced. The loss mechanism of the energy storage connector is modeled using a nonlinear loss function. The product relationship between the square of the coupling impedance change amplitude and the current intensity is introduced in the calculation process to simulate the intensity change of nonlinear energy loss under different working conditions, thereby obtaining loss parameters that characterize the nonlinear coupling dissipation effect. Based on the existing basic power transmission relationship, the above nonlinear coupling loss parameters are superimposed on the model in the form of coupling impedance change terms, so that the original power relationship is no longer approximated in a linear equivalent manner, but instead a power transmission coupling relationship is constructed through coupling compensation terms. Based on this coupling relationship, a mathematical model that reflects the dynamic change characteristics of the coupling impedance of the energy storage connector is constructed. By introducing disturbance driving terms and feedback adjustment factors, its adaptability to system complexity and uncertainty is enhanced, making the model a direct basis for calling the control algorithm and having the ability to connect to the actual energy storage controller through a logical interface.

[0057] S2, a disturbance identification module 002, configured to identify nonlinear coupling disturbances in the energy transmission process according to the energy storage connector system model, and obtain an additional coupling loss component, a dynamic impedance drift component, and a phase coupling offset component;

[0058] Specifically, the energy storage connector system model is used to monitor the energy transmission process in real time. By collecting voltage and current signals at a high sampling rate, the input power and output power are calculated and their difference is taken in real time to generate power difference data reflecting the degree of energy transmission anomalies within the energy storage channel. This power difference data is input into the disturbance separation and identification engine. Combined with known parameters in the nonlinear coupling model and the current load state, the disturbance is refined and identified based on a feature decoupling algorithm. This effectively decomposes the original overall power disturbance into three non-overlapping disturbance features: loss-type disturbances reflecting coupling loss changes, impedance-type disturbances characterizing the time-varying impedance characteristics, and phase-type disturbances caused by signal mismatch or magnetic coupling fluctuations. Loss-type disturbances are correlated with the coupling impedance change. By combining the power difference with the current effective current parameters and using a nonlinear function fitting method, the additional loss component under the nonlinear coupling state is calculated. This additional coupling loss component directly quantifies the energy dissipation caused by the nonlinear impedance disturbance and is a key input to the energy control strategy. For impedance disturbances, the transient change rate of the coupled impedance is dynamically derived based on the change frequency sampled from the load side. This constructs a mapping relationship between the load change rate and the impedance response, thereby extracting the dynamic impedance drift component, which reflects the system's nonlinear impedance adaptability under dynamic electrical load disturbances. In the identification of phase disturbances, the reference signal is compared with the current transmission signal, and the phase error between the two is calculated using a phase extractor. This error is then smoothed using a time window sliding average method to obtain the phase coupling offset component, which is used to characterize the degree of frequency synchronization misalignment during the coupling process.

[0059] S3, a dynamic observation module 003, configured to input the additional coupling loss component, the dynamic impedance drift component, and the phase coupling offset component into a nonlinear coupling dynamic observer for weighted analysis to obtain a coupling mechanism observation result;

[0060] It should be noted that the disturbance intensity analysis unit synchronously inputs the additional coupling loss component, dynamic impedance drift component and phase coupling offset component output by the disturbance identification module into the nonlinear coupling dynamic observer, measures the intensity of each component through characteristic scale normalization and disturbance change rate analysis method, and constructs disturbance intensity data reflecting the disturbance amplitude of the current coupling state. The weight coefficient calculation unit executes the weight distribution strategy according to the above disturbance intensity data, and performs a weight analysis on the three types of components of loss, impedance and phase by setting the disturbance contribution factor or disturbance energy function to generate a corresponding adaptive weight coefficient group. Each disturbance component corresponds to an independent weight value, the size of which reflects the importance of the disturbance dimension in the current system operation. The change process of the weight coefficient reflects the transition trend of the system from a dominant disturbance state to another disturbance type. The parallel observation unit uses the weight coefficient group as the control input to adjust the three observation channels in the nonlinear coupling dynamic observer. These are the loss observation channel for additional loss, the impedance drift channel for impedance change, and the phase offset channel for phase mismatch. These three channels work together in a completely parallel manner, and their calculation priority and filtering parameters are controlled by the weight coefficients, thereby achieving synchronous tracking and differentiated response to multi-dimensional coupling disturbances. Each observation channel integrates an adaptation algorithm module, such as Kalman filtering for processing low-frequency slowly changing loss trends, extended filtering for impedance change perception under nonlinear modeling, and a phase-locked loop mechanism for quickly capturing phase jumps and frequency mismatches. The three can maintain stable collaborative observation capabilities under high-frequency coupling disturbances or sudden load shocks. Ultimately, the observation results of all channels are fused into a set of coupling mechanism observation output data.

[0061] In this embodiment, the loss observation channel, impedance drift observation channel, and phase offset observation channel of a nonlinear coupling dynamic observer are weighted according to an adaptive weight coefficient group to form a channel configuration parameter group. The loss channel weight parameters in the channel configuration parameter group are applied to real-time filtering of the additional coupling loss component. A Kalman filter algorithm is used to perform state estimation and error correction on the input disturbance sequence, thereby extracting additional coupling loss observation data with time continuity and data stationarity. Simultaneously, an extended Kalman filter is applied to the dynamic impedance drift component based on the impedance drift channel weight parameters. This method can predict and update the state under nonlinear model conditions. Its nonlinear linearization approximation is suitable for the dynamic impedance evolution of connectors under complex load fluctuations or temperature instability, thereby obtaining high-precision dynamic impedance drift observation data. A phase-locked loop tracking observation is performed on the phase coupling offset component based on the phase offset channel weight parameters in the channel configuration parameter group. This mechanism detects the phase difference between the reference signal and the actual signal and performs feedback correction, allowing the system to capture frequency disturbances and phase drift in real time and output phase coupling offset observation data. Through a predefined weighted fusion strategy, the additional coupling loss observation data, dynamic impedance drift observation data and phase coupling offset observation data are weightedly fused to obtain the coupling mechanism observation results.

[0062] S4, a generation module 004, is used to generate an energy transmission path control instruction including a conduction angle compensation amount and a phase synchronization compensation amount based on the coupling mechanism observation result.

[0063] Specifically, after receiving the coupling mechanism observation results output by the dynamic observation module, the feedforward compensation unit uses the additional coupling loss observation data, dynamic impedance drift observation data, and phase coupling offset observation data in the results as input variables. It then performs feedforward compensation gain calculation based on the system's preset disturbance response model and controller sensitivity parameters, and then solves for the loss compensation gain, impedance drift compensation gain, and phase offset compensation gain, respectively. The three together constitute a feedforward compensation gain group, which quantitatively reflects the active control intensity required by the system when facing the current disturbance state. The compensation angle calculation unit uses this feedforward compensation gain group and the basic conduction angle parameters of the energy storage connector to perform a fusion calculation. Based on the conduction angle adjustment function defined in the system, the three gain terms are weighted and mapped into a specific conduction angle compensation value. This compensation value is used at the execution level to adjust the conduction timing of power devices (such as MOSFETs or IGBTs) in real time to optimize the impedance matching state and suppress the increase in loss and energy reflection caused by nonlinear coupling. At the same time, the synchronization adjustment unit compares the phase coupling offset observation data from the coupling mechanism observation results with the reference phase angle within the system, calculates the offset in real time, and uses the synchronization adjustment function to generate a phase synchronization compensation value. This compensation value is used to adjust the system clock or phase-locking mechanism to ensure that the energy storage connector achieves frequency and phase consistency under multi-port or multi-module operating conditions, thereby effectively avoiding oscillations, power fluctuations, or energy loss caused by coupling asynchrony. The instruction generation unit combines and encodes the conduction angle compensation value and the phase synchronization compensation value to generate a complete energy transmission path control instruction containing the switch tube control parameters and phase locking parameters. This instruction is directly transmitted to the underlying controller for real-time adjustment of the power device switching behavior and the system synchronization strategy, thereby achieving a highly efficient and robust energy transmission control closed loop without relying on external sensors.

[0064] In this embodiment, the phase coupling offset observation data from the coupling mechanism observation results is analyzed for phase dynamic characteristics. This data is processed using frequency-domain filtering and a time-series smoothing algorithm to extract the phase offset amplitude, which reflects the changing trend of the coupling state. This amplitude is used as a highly abstract expression of phase perturbation and defined as a phase offset characteristic parameter. Based on this phase offset characteristic parameter, an adaptive calculation logic is executed to dynamically adjust the synchronization adjustment coefficient. This coefficient calculation method not only relies on the absolute value of the offset amplitude but also comprehensively considers factors such as the system operating frequency, carrier synchronization state, and observation accuracy margin. This ensures that the phase synchronization adjustment coefficient has good robustness and real-time performance, and can adapt to the system stability requirements under different load fluctuation conditions. This adaptively derived phase synchronization adjustment coefficient is applied to the reference phase angle correction process. By performing an offset compensation operation on the original reference phase angle of the energy storage connector, a target phase angle with coupling compensation capability is constructed. This angle value represents the synchronization reference state to which the current system should be adjusted under non-ideal coupling conditions and serves as an important benchmark for controlling the phase-locking mechanism and phase coordination mechanism. The difference between the target phase angle and the original reference phase angle is calculated, and the relative offset between the two is extracted using the phase difference calculation function to obtain the phase synchronization compensation amount. This compensation amount will be injected into the subsequent energy transmission path control module as a control amount, and drive multiple synchronization control sub-modules including phase-locked loop control, voltage phase locking, and staggered parallel module scheduling to achieve full-link phase compensation closed-loop control from the parameter level to the execution level.

[0065] In one example, the establishing module 001 further includes:

[0066] A frequency domain analysis unit, used to perform frequency domain analysis on the voltage signal of the energy storage connector to obtain voltage characteristic parameters;

[0067] an effective value calculation unit, configured to perform effective value calculation on the current signal of the energy storage connector to obtain current characteristic parameters;

[0068] a dynamic impedance calculation unit, configured to calculate the dynamic impedance parameters of the energy storage connector according to the voltage characteristic parameters and the current characteristic parameters;

[0069] The transmission relationship modeling unit is used to model the power transmission relationship of the energy storage connector based on the dynamic impedance parameter to obtain an energy storage connector system model.

[0070] In this example, the frequency domain analysis unit performs frequency domain analysis on the voltage signal collected by the energy storage connector. The time-domain voltage waveform is preprocessed with a window function and then fed into a fast Fourier transform (FT) or continuous wavelet transform (CWT) algorithm. Spectral reconstruction is then used to extract key frequency domain features of the voltage signal, including the fundamental frequency component, primary and secondary harmonic components, spectral band energy distribution, and high-frequency interference signals. The results are parameterized using a combined amplitude and phase spectrum to construct a set of voltage characteristic parameters reflecting the energy storage connector's input power fluctuations. The effective value calculation unit processes the current signal collected synchronously with the voltage signal from the energy storage connector. It calculates the effective value of the current signal by integrating the square of the current amplitude over a complete operating cycle, taking the arithmetic mean, and then taking the square root. This process extracts current intensity information that reflects the load's true consumption capacity while also accounting for factors such as waveform distortion, pulse spikes, and periodic jitter. The result is a set of current characteristic parameters that include comprehensive indicators such as the cycle mean, current fluctuation rate, and short-term peak amplitude ratio. The voltage characteristic parameters extracted by the frequency domain analysis unit and the current characteristic parameters output by the effective value calculation unit are input into the dynamic impedance calculation unit. In this unit, the complex ratio between voltage and current is used as the calculation core to construct the dynamic complex impedance parameters. This impedance includes a resistive component and takes into account the phase response changes caused by inductive and capacitive coupling. By introducing a time window sliding mechanism and a frequency domain energy weighted model, the impedance expression is further extended to multi-band, multi-time period, and multi-harmonic structures. A joint structure of impedance amplitude response curve and phase drift curve is established to characterize the transient response behavior and long-term evolution trend of the energy storage connector under non-steady-state coupling disturbance conditions. On this basis, the transmission relationship modeling unit uses the multi-dimensional impedance parameter set provided by the dynamic impedance calculation unit to construct a complete power transmission modeling structure based on the energy conservation relationship, extended by the nonlinear coupling loss function, and closed by dynamic response feedback. This unit first establishes the fundamental power transmission relationship—a theoretical output power expression expressed as a function of input voltage, current, and phase difference. Building on this foundation, it introduces a nonlinear coupling loss model. This model superimposes the dynamic impedance parameter change, phase offset term, and load frequency response term onto the loss term as a coupling function, forming a complete power transmission coupling relationship encompassing input power, output power, additional loss, dynamic offset, and feedback perturbation. To enhance the model's stability and robustness, the transmission relationship modeling unit introduces a state-space model and gain matrix. Using pole placement and disturbance response optimization methods, it corrects the sensitivity distribution of each parameter to output power, ultimately generating a system model that reflects the true operating state of the energy storage connector under non-ideal conditions.

[0071] In one example, the transmission relationship modeling unit is specifically configured to:

[0072] Constructing a power transmission basic relationship including an input power term and an output power term according to the dynamic impedance parameter;

[0073] Performing nonlinear loss calculation on the coupling loss characteristics of the energy storage connector to obtain nonlinear coupling loss parameters;

[0074] The power transmission basic relationship is processed by adding a coupling impedance change term based on the nonlinear coupling loss parameter to obtain a power transmission coupling relationship, and an energy storage connector system model reflecting the dynamic change characteristics of the energy storage connector coupling impedance is created based on the power transmission coupling relationship.

[0075] In this example, a fundamental power transmission relationship consisting of input and output power terms is constructed based on dynamic impedance parameters. Input power is calculated by multiplying the voltage and current characteristic parameters and combining them with the cosine component of the phase angle, while output power is extracted from the measured value at the load or a theoretical load response function. The difference between the two represents the energy loss during transmission, which is subdivided into linear loss and nonlinear coupling loss. The linear loss is due to internal resistance and parasitic parameters, while the nonlinear coupling loss is the most dynamic and critical factor in the coupling characteristics of energy storage connectors. To accurately identify the nonlinear coupling loss characteristics, the fluctuation of dynamic impedance, nonlinear amplitude variation, and phase response drift are jointly modeled. The squared change in dynamic impedance is combined with the current effective current intensity to form the core variable of the nonlinear loss function. Its contribution to power loss is adjusted using a perturbation coefficient. This results in a nonlinear coupling loss parameter that represents the nonlinear energy dissipation behavior under complex coupling conditions. This parameter grows nonlinearly with the input signal amplitude and reflects the multidimensional impact of different load characteristics or signal interference on transmission efficiency based on the real-time changes in the coupling angle. The nonlinear coupling loss parameter is substituted back into the initially constructed power transfer basic relationship as a regulating term, and the coupling impedance variation term is superimposed on this relationship structure to form a power transfer coupling relationship with dynamic response capabilities. This coupling relationship introduces a time-varying, disturbance-driven nonlinear compensation term. This compensation term automatically adjusts its influence weight during different operating cycles and handles high-frequency disturbances through gain amplification or filtering mechanisms, enabling adaptive tracking and correction of the energy transfer characteristics of the energy storage connector. This modeling approach not only reflects the energy transfer efficiency of the connector under normal load conditions but also accurately predicts its power attenuation pattern under complex conditions such as sudden coupling mismatch, voltage fluctuation, or frequency drift, thereby fully embedding the variation characteristics of the coupling impedance into the core structure of the system modeling. Based on this power transfer coupling relationship, a system-level mathematical model that reflects the nonlinear coupling characteristics of the energy storage connector is constructed. This model takes the input and output power as the main structure and uses the transient variation characteristics of the coupling impedance as the core dynamic variable throughout each dimension of the model's internal state space. The model's structure includes an adjustable state matrix, a disturbance response matrix, and nonlinear interaction terms, as well as an error feedback mechanism and a correction control interface for fitting and predicting unsteady-state fluctuations. Furthermore, the model, in conjunction with an observer module, enables adaptive model updates based on observation errors. This means that when the system detects a deviation between the actual transmission state and the model's prediction, the nonlinear weight coefficients of the coupling impedance terms are adjusted via an error backpropagation algorithm. This dynamically optimizes the parameter response relationships in the power coupling structure, achieving a continuous approach to optimal energy transfer efficiency.

[0076] In one example, the disturbance identification module 002 is specifically configured to:

[0077] Performing real-time power monitoring of the energy transmission process based on the energy storage connector system model to obtain power difference data;

[0078] Separating and identifying nonlinear coupling disturbance features according to the power difference data to obtain disturbance feature classification results including loss disturbance, impedance disturbance, and phase disturbance;

[0079] Performing a correlation calculation process on the loss-type disturbance in the disturbance feature classification result to obtain an additional coupling loss component related to the nonlinear coupling characteristic of the energy storage connector;

[0080] Calculating the load change rate of the impedance disturbance in the disturbance feature classification result to obtain a dynamic impedance drift component;

[0081] A phase offset is calculated for the phase-type disturbance in the disturbance feature classification result to obtain a phase coupling offset component.

[0082] In this example, based on the energy storage connector system model, voltage and current data are continuously collected at both ends of the energy storage connector during actual operation. Through effective value calculation and instantaneous sampling, continuous time series of real-time input power and output power are constructed. Based on this, sliding time window difference calculation is performed to generate a power difference data series representing the energy difference between input and output in real time. Based on the power difference data, the disturbance characteristics reflected in it are separated and identified. To this end, a classification method based on multidimensional time series decoupling and disturbance pattern matching is introduced. By constructing a composite disturbance feature template containing loss characteristic curves, impedance change trend curves, and phase offset response curves, the power difference series is projected onto various types of disturbance spaces according to the disturbance pattern. The weight distribution of the disturbance signal in each category is obtained, and the disturbance feature classification and identification are completed. The disturbance feature classification results, including loss disturbance, impedance disturbance, and phase disturbance, are output. Among them, loss-type disturbances are mainly manifested as a persistently low power difference, a small frequency change amplitude and a long duration. Impedance-type disturbances are manifested as periodic changes in power difference, floating synchronously with load changes, and phase-type disturbances are manifested as rapid reversals of power difference within a period, with high-frequency jitter and quasi-random peak changes. Therefore, the three have clear boundaries in statistical distribution and time-frequency structure, facilitating high-precision separation and identification by the system. For loss-type disturbances in the classification results, the current effective value and dynamic impedance model parameters at the current moment are combined to perform a correlation calculation of the coupling impedance change. By establishing a nonlinear coupling loss function, this function maps and correlates the energy share of the power difference attributable to loss-type disturbances with the nonlinear dynamic impedance increment, obtaining an additional coupling loss component directly related to the nonlinear coupling characteristics of the energy storage connector. This component can quantify the degree of energy dissipation caused by the nonlinear characteristics of the connector in the current coupling state, and serves as an important parameter source for the controller's feedforward compensation. At the same time, the impedance disturbances in the classification results are analyzed to obtain the load-side power, current or voltage change curves. By calculating the load change rate, that is, the rate of change of the load characteristic parameters per unit time, the dynamic response offset function is constructed in combination with the current impedance response curve, and the instantaneous increment of the coupling impedance offset due to the dynamic change of the load is extracted, thereby forming a dynamic impedance drift component. This component reflects the coupling hysteresis of the connector in the load disturbance response and is used to evaluate whether the system has hidden dangers such as high-frequency resonance or untimely control response.For phase-type disturbances in the classification results, a phase-locked processing mechanism is used to extract and track the instantaneous phase difference between the input signal and the output signal. The reference phase signal is used to construct a phase reference sequence, and a sliding correlation matching calculation is performed with the actual measured output voltage or current waveform to obtain the phase offset in the current cycle. Then, multiple parameters such as phase drift speed, phase jump amplitude and cumulative offset degree are extracted to form a phase coupling offset component. This component directly characterizes the energy alignment capability of the connector under electromagnetic instability or synchronization mismatch conditions, and provides a basis for phase compensation control.

[0083] In one example, the dynamic observation module 003 further includes:

[0084] a disturbance intensity analysis unit, configured to input the additional coupling loss component, the dynamic impedance drift component, and the phase coupling offset component into a nonlinear coupling dynamic observer to perform disturbance intensity analysis and obtain disturbance intensity data;

[0085] A weight coefficient calculation unit, configured to calculate observer weight coefficients based on the disturbance intensity data to obtain adaptive weight coefficient groups corresponding to the additional coupling loss component, the dynamic impedance drift component, and the phase coupling offset component, respectively;

[0086] The parallel observation unit is used to perform parallel observation on the loss observation channel, impedance drift observation channel and phase offset observation channel of the nonlinear coupling dynamic observer based on the adaptive weight coefficient group to obtain a coupling mechanism observation result.

[0087] In this example, the disturbance intensity analysis unit, as the pre-processing link of the entire observation module, quantitatively assesses the intensity of the three types of disturbance components that have been identified and classified. The additional coupling loss component, dynamic impedance drift component, and phase coupling offset component are input into the nonlinear coupled dynamic observer (NCDO) respectively. By jointly analyzing the time domain amplitude change rate, frequency domain energy distribution density, and mutation rate of each component, a three-dimensional disturbance intensity vector is constructed. The disturbance intensity data is weighted and fused according to the energy concentration, duration, and probability of exceeding the warning threshold of the disturbance component, and a disturbance intensity data set is output. This set clearly reflects the dominance and interference level of the three types of disturbances in the current coupling mechanism. The disturbance intensity data is transmitted to the weight coefficient calculation unit for dynamic weight adjustment calculation. This unit normalizes the disturbance intensity values ​​of each component through standardization based on the distribution pattern of disturbance intensity across different channels, and introduces an adaptive adjustment function that enables the system to automatically calculate the corresponding channel observation priority weight based on the suddenness of the disturbance and the duration of the coupling. In this process, a combination of linear weighting and nonlinear amplification is used to handle the influence of disturbances of different scales, and a disturbance evolution model is constructed based on historical disturbance trends to determine whether the current disturbance is in an outbreak period, a stable period, or a decay period, and to dynamically modify the weight adjustment strategy accordingly. The system outputs a three-item adaptive weight coefficient group, where each coefficient corresponds to a loss observation channel, an impedance drift channel, and a phase offset channel. This coefficient group not only reflects the current importance ranking of the disturbance components, but also regulates the resource allocation and computing intensity of each channel within NCDO, thereby realizing disturbance-guided observation resource reconstruction. The parallel observation unit constructs three functional channels in a parallel architecture, which are used to specifically observe the three disturbance dimensions of additional coupling loss, dynamic impedance drift, and phase coupling offset. The loss observation channel uses a Kalman filter to perform minimum mean square error estimation on the dynamic relationship between power differences and nonlinear impedance functions, dynamically extracting true loss trends and identifying noise and nonlinear anomalies. The impedance drift channel employs an extended Kalman filter to linearize and approximate the nonlinear variation characteristics of dynamic impedance and perform state prediction updates, estimating changes in the impedance response curve due to load fluctuations or thermal effects. The phase offset channel incorporates a phase-locked loop mechanism to detect the phase difference between input and output signals in real time and perform closed-loop synchronous corrections, extracting key information such as frequency drift, phase jumps, and long-period accumulated errors. In actual operation, the three channels operate together at the observation frame time granularity. Within each observation frame, the data processing priority and sampling window length of each channel are dynamically allocated based on adaptive weights. Under high-disturbance conditions, the filter bandwidth, gain response speed, and output update frequency of the dominant channel are automatically enhanced, ensuring a prioritized response to major disturbances.After integrating the observation data from each channel, the parallel observation unit performs a weighted fusion operation. This fusion process uses the current weight of each item in the adaptive weight coefficient group and uses error confidence, response stability, and channel output variance as fusion evaluation criteria to perform a differentiated weighted combination of the three-channel observation results, outputting a unified coupling mechanism observation result. This result includes a comprehensive description of the energy loss and phase offset trends within the energy storage connector under the current nonlinear coupling state, and can also provide multi-dimensional characteristic parameters such as the system's instantaneous coupling level, control demand level, and disturbance dominant mode.

[0088] In one example, the parallel observation unit is specifically configured to:

[0089] performing weight configuration processing on the loss observation channel, the impedance drift observation channel, and the phase offset observation channel of the nonlinear coupling dynamic observer according to the adaptive weight coefficient group to obtain a channel configuration parameter group;

[0090] Performing Kalman filtering observation on the additional coupling loss component based on the loss channel weight parameter in the channel configuration parameter group to obtain additional coupling loss observation data;

[0091] Performing extended Kalman filtering observation on the dynamic impedance drift component based on the impedance drift channel weight parameter in the channel configuration parameter group to obtain dynamic impedance drift observation data;

[0092] Performing phase-locked loop tracking observation on the phase coupling offset component based on the phase offset channel weight parameter in the channel configuration parameter group to obtain phase coupling offset observation data;

[0093] The additional coupling loss observation data, the dynamic impedance drift observation data, and the phase coupling offset observation data are weightedly fused to obtain a coupling mechanism observation result.

[0094] In this example, using an adaptive weight coefficient group as input, weight configuration is performed on the three sub-observation channels within a nonlinear coupled dynamic observer, constructing a channel configuration parameter group with real-time priority adjustment capabilities. During this process, the disturbance intensity value of the additional coupling loss component is mapped to the weight parameter of the loss observation channel, the changing trend of the dynamic impedance drift component is mapped to the weight parameter of the impedance drift observation channel, and the phase offset intensity of the phase coupling offset component serves as the weight control basis for the phase offset channel. During the configuration process, these three weight parameters not only control the observation frequency, filter bandwidth, and update rate of each channel, but also participate in adjusting the gain matrix of the channel's internal filter, the error covariance update method, and the estimated residual tolerance. This constitutes a multivariable-driven channel configuration parameter group that enables each channel to focus on the main disturbance direction when faced with different dominant coupling disturbance forms, thereby improving local observation accuracy. The channel configuration parameter group is applied to the actual observation execution of the nonlinear coupling dynamic observer. In the loss observation channel, Kalman filtering is performed on the additional coupling loss component based on the loss weight parameter in the configuration parameter group. By establishing state transition equations and observation equations, the loss component of the power difference data attributable to the nonlinear coupling impedance is modeled as a state variable. The prediction and update mechanism of the Kalman filter is combined to perform noise suppression, disturbance smoothing, and expectation estimation. This results in the extraction of additional coupling loss observation data that is continuous in time series and stable in terms of disturbances, reflecting the energy dissipation trend caused by the nonideal coupling process in the energy storage connector. Simultaneously, in the impedance drift observation channel, the impedance drift weight parameter in the configuration parameter group is embedded in the extended Kalman filter (EKF) structure. Because the dynamic impedance change contains nonlinear trends, the EKF transforms the nonlinear state transition problem into an iterative process of linear state prediction and error update by performing a first-order Taylor expansion on the nonlinear model. This enables the system to dynamically extract the impedance response trajectory of the energy storage connector under different load frequencies, coupling inductance changes, or temperature drift conditions while maintaining observation convergence, thereby obtaining dynamic impedance drift observation data. In the phase offset channel, the phase-locked loop (PLL) control module is tuned based on the phase offset weight parameter in the configuration parameter group. This parameter is used to adjust the phase comparator sensitivity, low-pass filter cutoff frequency, and loop gain constant of the phase-locked tracker, ensuring stable tracking under rapidly changing phase disturbances. Specifically, the phase difference between the reference input signal and the output signal is input as an error signal to the loop controller. The PLL continuously adjusts the local oscillator phase to achieve lock, thereby outputting phase coupling offset observation data. This data accurately quantifies the phase error trend of the current system under clock offset, carrier frequency fluctuation, or asynchronous operation.A weighted fusion of the additional coupling loss observation data, dynamic impedance drift observation data, and phase coupling offset observation data is performed. Using the original adaptive weights in the configuration parameter group as the base weight vector, a weighting factor correction matrix is ​​constructed by combining the current observation error covariance, residual variance, and response delay time of each channel to form a dynamic fusion weight structure. During the fusion process, a data consistency judgment mechanism is used to remove high-noise data fragments. Time alignment and sliding window averaging are used to improve the stability and real-time performance of cross-channel fusion. The final output is an observation output cluster that includes the global coupling state trend, disturbance energy distribution, and the dominant coupling factor determination result. This cluster is the final coupling mechanism observation result.

[0095] In one example, the generating module 004 further includes:

[0096] A feedforward compensation unit, configured to calculate a feedforward compensation gain based on the coupling mechanism observation result to obtain a feedforward compensation gain group including a loss compensation gain, an impedance drift compensation gain, and a phase offset compensation gain;

[0097] a compensation angle calculation unit, configured to perform compensation angle calculation on the basic conduction angle of the energy storage connector based on the feedforward compensation gain group to obtain a conduction angle compensation amount;

[0098] a synchronous adjustment unit, configured to synchronously adjust the reference phase angle according to the phase coupling offset observation data in the coupling mechanism observation result to obtain a phase synchronization compensation amount;

[0099] An instruction generating unit is used to generate an energy transmission path control instruction including a switch tube control parameter and a phase locking parameter according to the conduction angle compensation amount and the phase synchronization compensation amount.

[0100] In this example, the feedforward compensation unit receives coupling mechanism observations from the output of a nonlinear coupling dynamic observer and performs disturbance weight normalization and energy attribution calculations on the additional coupling loss observations, dynamic impedance drift observations, and phase coupling offset observations. Multi-channel feedforward compensation gain fitting is performed based on the system's operating power level, circuit load inertia coefficient, and response time constant. Disturbance trend information is obtained by integrating the time windows of various observation components and calculating short-term gradients. This trend drives three independent feedforward gain generation paths, which output loss compensation gain, impedance drift compensation gain, and phase offset compensation gain, respectively. The loss compensation gain is used to amplify the conduction strength adjustment amplitude in the power control channel. The impedance drift compensation gain reflects the impact of changes in the connector inductance or capacitance coupling path on the conduction strategy. The phase offset compensation gain is used to coordinate the error response capability between the internal carrier phase and the grid synchronization reference. These three compensation gains constitute the feedforward compensation gain group. Based on this feedforward compensation gain group, the compensation angle calculation unit applies an angle compensation function to the base conduction angle of the energy storage connector. Starting from the base conduction angle, this function transforms the three gain terms through linear combination, normalization, and exponential smoothing. It then generates a set of conduction angle variation strategies, taking into account the device response bandwidth and switching current slope constraints. The resulting conduction angle compensation represents the incremental angle adjustment required relative to the standard control angle under the current coupling disturbance. This angle compensation can be directly mapped to the gate drive time window of power devices such as IGBTs and MOSFETs, adjusting their turn-on and turn-off times. This substantially alters the energy injection rhythm and electromagnetic coupling rhythm of the energy storage module, enabling it to maintain efficient energy output and minimize coupling energy consumption despite voltage fluctuations, load disturbances, or non-ideal coupling conditions. Meanwhile, the synchronization adjustment unit focuses on processing phase coupling offset observations related to phase mismatch within the coupling mechanism observations. The system constructs a phase error function by identifying the offset trend between the reference phase angle and the currently observed phase. This function takes the offset as input and, through gain adjustment and anti-windup strategies, outputs a target phase adjustment amplitude. This adjustment amplitude, known as the phase synchronization compensation, is used to correct system synchronization mismatches caused by nonlinear coupling, electromagnetic interference, or frequency drift. This compensation can be applied to a PLL (phase-locked loop), DPLL (digital phase-locked loop), or soft synchronization controller to adjust the phase lock target in real time, aligning the energy storage connector output frequency with the system reference frequency. This eliminates energy waveform distortion and harmonic overlap caused by frequency offset, ensuring the system's multi-module interoperability. The calculated results of all compensations are input to the instruction generation unit, which structures and encapsulates the conduction angle compensation and phase synchronization compensation parameters into a protocolized parameter structure. Ultimately, it generates energy transmission path control instructions containing switch control parameters and phase locking parameters.Among them, the switch tube control parameters mainly include the conduction angle adjustment value, the driving voltage threshold and the gate opening and closing timing information, which act on the device drive unit; while the phase locking parameters include data items such as the target phase, locking bandwidth, and phase locking frequency sensitivity, which serve as feedback update inputs for the soft synchronization controller or PLL structure.

[0101] In one example, the synchronization adjustment unit is specifically configured to:

[0102] Extracting the phase offset amplitude from the phase coupling offset observation data in the coupling mechanism observation result to obtain a phase offset characteristic parameter;

[0103] Adaptively calculating the synchronization adjustment coefficient based on the phase offset characteristic parameter to obtain the phase synchronization adjustment coefficient;

[0104] Performing offset compensation on the reference phase angle of the energy storage connector according to the phase synchronization adjustment coefficient to obtain a target phase angle;

[0105] A phase difference is calculated based on the target phase angle and the reference phase angle to obtain a phase synchronization compensation amount.

[0106] In this example, coupling mechanism observations serve as the input data source, focusing on the observed component related to phase perturbations, namely, the phase coupling offset observation data. This data, derived from the phase-locked channel output of a nonlinear coupled dynamic observer, is presented as a continuous time series, recording the instantaneous phase difference between the reference signal and the actual output. To convert this data into a structured control variable that can be used for regulation, the sequence is subjected to phase offset amplitude extraction. This extraction method combines short-time Fourier transform analysis with an envelope detection algorithm. The maximum and minimum phase offset values, as well as the average drift rate within each sampling period, are obtained in the time domain. A comprehensive set of phase offset characteristic parameters is constructed, which not only characterize the magnitude of the phase offset but also reflect the dynamic trend and cumulative impact of the offset process. Using these phase offset characteristic parameters as driving factors, adaptive calculation of the synchronization adjustment coefficient is performed. A regulation function model based on disturbance response intensity and steady-state recovery time is constructed. The standard deviation, maximum offset, and slope of phase offset are input variables. Combined with additional parameters such as the controller's current operating state, synchronization deviation tolerance, and system clock sensitivity, a multidimensional mapping function is implemented to solve the problem. The model outputs a phase synchronization adjustment coefficient with real-time adaptability. This coefficient, numerically represented by a dynamic weight, adjusts the controller's correction amplitude for phase error. Logically, it represents the system's feedback sensitivity at a given disturbance level. A larger value indicates a greater degree of system deviation from synchronization and a larger correction amplitude is required. A smaller value indicates the system is in a steady-state operating region, resulting in a more moderate adjustment action, thus avoiding frequency oscillation or phase-lock error accumulation caused by over-control. Based on this phase synchronization adjustment coefficient, the reference phase angle currently used by the energy storage connector is compensated for offsets. This reference phase angle is essentially the clock reference or phase lock setpoint used as the synchronization target in the system. Its stability is directly related to the effectiveness of energy synchronization and phase control. In actual operation, by constructing an offset compensation function, the phase synchronization adjustment coefficient is applied to the original reference phase angle, and the target phase angle is updated incrementally or decrementally in combination with the actual observed offset direction (positive or negative), thereby generating a target phase angle. The target phase angle is an adaptive reference angle that is corrected in real time after the disturbance response, and is used to drive the PLL loop to lock the target or the phase setting input of the soft synchronization controller. The dynamic update of the target phase angle ensures that the energy storage connector will not continue to operate in a mismatched state under phase coupling disturbance conditions, but will be able to continuously converge to the ideal synchronization angle through its own adjustment mechanism, thereby minimizing the phase energy loss caused by frequency offset, signal alignment imbalance or coupling asymmetry. After the target phase angle is constructed, the phase difference calculation process is executed to perform an arithmetic difference operation on the actual reference phase angle and the target phase angle in the current system to obtain the phase synchronization compensation amount.

[0107] In one example, it also includes:

[0108] a coordination control module configured to perform conduction angle adjustment control on the switch tube of the energy storage connector based on the switch tube control parameters in the energy transmission path control instruction to obtain a switch tube operating state with conduction angle compensation; perform phase synchronization control on the phase locking unit of the energy storage connector based on the phase locking parameters in the energy transmission path control instruction to obtain a phase locking operating state with phase synchronization compensation; and perform coordinated synchronization based on the switch tube operating state and the phase locking operating state to obtain an energy storage transmission control state.

[0109] In this example, the coordinated control module receives energy transfer path control instructions, which already include conduction angle compensation and phase synchronization compensation. These instructions are encoded as two independent but logically linked control components: switch control parameters and phase lock parameters. The switch control parameters are passed as input variables to the power control channel, which includes components such as gate drive circuits, conduction cycle controllers, and real-time PWM modulators. The gate drive circuit receives the conduction angle target value and converts it into specific PWM control signals, thereby controlling the turn-on and turn-off times of MOSFETs or IGBTs during each operating cycle. The key to conduction angle regulation is to accurately synchronize the compensated conduction angle to the start of the switching cycle and dynamically expand or contract the conduction window based on the disturbance response strength, thereby ensuring that the energy storage current pulse has higher load adaptability and electromagnetic compatibility. Simultaneously, when the system detects a change in conduction angle, it updates the operating state of the switch, recording the actual conduction angle, switch response delay, peak current response, and device temperature rise data for the current cycle. These physical quantities are integrated through a state reconstruction function to form a switch operating state that describes the change in switching behavior. This state is used in the subsequent coordination process as the first input to the dynamic control branch. Phase lock parameters are fed into the synchronization control channel, whose core consists of a digital phase-locked loop (DPLL), a phase comparator, a low-pass filter, and a voltage-controlled oscillator. Its goal is to dynamically adjust the phase alignment between the energy storage connector's output frequency and a master reference signal based on the target phase angle and lock bandwidth specified in the control instructions. The system uses the phase comparator to calculate the difference between the current output signal and the target reference signal to generate a phase error signal. This error signal is then passed through a filter for bandwidth limiting and waveform smoothing. Finally, the corrected control variable is input into the oscillator to adjust the local carrier signal frequency, achieving dynamic correction of the output phase. During this process, the phase lock operating status is also updated synchronously, including multiple metrics such as the current loop error, lock status flag, jitter strength, frequency tracking offset, and phase lock time delay. These metrics collectively constitute the system's dynamic operating status at the frequency synchronization level and serve as the fundamental data set describing the effectiveness of the phase adjustment channel. Coordinated synchronization between the switch operating status and the phase lock operating status achieves scheduling consistency and control coordination between conduction control and phase lock. Because in the energy storage connector, changes in the conduction timing of the switch tube will directly affect the flow direction of the carrier energy in the power flow, and the phase locking error will cause multi-channel interference and frequency beat effects, so the two must maintain a time and phase alignment relationship.To this end, the coordinated control module jointly analyzes the operating states of the switching tubes and the phase-locked operating states to construct a coordinated synchronization matrix. This matrix determines whether the current system is within the coordination window by comparing the relative deviation between the turn-on response delay and the phase-locked tracking delay. Based on this judgment, it dynamically adjusts the gate drive advance, phase correction frequency, or turn-on inhibition time to eliminate energy overlap, short circuits, or drift caused by asynchrony between the two. This coordinated control process generates a unified set of energy storage transmission control states. This state reflects the energy transfer quality of the current energy storage connector in the context of nonlinear coupling and also includes multiple performance indicators such as device response, synchronization consistency, coupling robustness, and system stability.

[0110] In one example, it also includes:

[0111] A bandwidth optimization module is configured to monitor the transmission efficiency of the energy storage connector under conditions of sudden energy storage load changes and coupling impedance fluctuations based on the energy storage transmission control state, thereby obtaining transmission efficiency monitoring data including transmission efficiency values ​​for multiple conditions; perform carrier frequency and harmonic order analysis on the correlation between the coupling observation bandwidth and the transmission efficiency based on the transmission efficiency monitoring data, thereby obtaining bandwidth efficiency relationship data reflecting the matching characteristics of the observation bandwidth and the transmission efficiency; perform a switching frequency correlation search on the optimal observation bandwidth based on the bandwidth efficiency relationship data, thereby obtaining optimal observation bandwidth parameters that maximize the transmission efficiency of the energy storage connector; and perform transmission efficiency optimization on the operating parameters of the energy storage connector based on the optimal observation bandwidth parameters, thereby obtaining optimal transmission efficiency operating parameters suitable for conditions of sudden load changes and impedance fluctuations.

[0112] In this example, the bandwidth optimization module monitors the energy storage transmission control state in real time. This control state includes information such as the conduction behavior of the switch tube, the phase lock response, the coupling mechanism observation results, and the controller output dynamics. Based on these state quantities, the system establishes an energy channel comparison model between the power input and output. It also counts indicators such as energy loss, waveform distortion, phase offset frequency, and control error feedback cycle by cycle under different load mutations and impedance fluctuations. These indicators are further synthesized into a set of continuous transmission efficiency values, constructing a data set containing multi-condition transmission efficiency values. This data set is structured with condition identification, timestamp, frequency parameters, and efficiency values ​​as its dimensions. Correlation analysis is performed on this transmission efficiency monitoring data to identify the functional relationship between the coupling observation bandwidth and transmission efficiency. The coupling observation bandwidth is defined as the three-channel observation frequency domain range in the nonlinear coupled dynamic observer (NCDO) that can be used to process loss, impedance, and phase disturbances. The transmission efficiency directly represents the system's ability to respond to energy state changes under these observation conditions. To this end, the system introduces two key variables: carrier frequency and harmonic order. In the frequency domain, the observation capabilities under different bandwidth configurations are mapped to frequency-harmonic composition characteristics. A fitting model is established using the transmission efficiency corresponding to each bandwidth group as the response value. Methods such as spline interpolation, gradient regression, or Gaussian process modeling are used to analyze the nonlinear response relationship between frequency-bandwidth configuration and efficiency changes. This results in a set of bandwidth-efficiency relationship data that reflects the degree of match between the observation bandwidth and system efficiency. This data intuitively reveals the frequency distribution structure under which the system can minimize energy loss and achieve optimal coupled regulation. After completing this analysis, an optimal observation bandwidth search mechanism is constructed based on the bandwidth-efficiency relationship data. This mechanism uses the current energy storage system's switching frequency as the primary reference variable and combines the harmonic spectrum distribution to match bandwidth expansion by integer multiples and sub-multiples. During the matching process, the system uses an observation bandwidth search window function and performs a combined local search based on efficiency gradients and global hill climbing search on candidate bandwidth points. Furthermore, the system combines filter design boundary conditions with hardware processing bandwidth constraints to achieve a coupling between theoretical optimality and physical feasibility. After the search is completed, the system selects a set or an optimal observation bandwidth parameter to maximize the match between the nonlinear coupling observation and the energy transmission efficiency under the bandwidth configuration, thereby minimizing the problems of phase misjudgment, delay interference and misadjustment response caused by insufficient observation accuracy or excessive bandwidth.Based on the above-mentioned optimal observation bandwidth parameters, the operating parameters of the energy storage connector are adjusted and optimized. This process includes multiple operations such as updating the channel sampling frequency of the nonlinear coupled dynamic observer, configuring the filter cutoff frequency in the bandwidth controller, redistributing the controller calculation cycle, correcting the phase-locked channel response bandwidth, and adjusting the sensor data processing priority. These operations work together throughout the entire control chain, so that the sampling, perception, feedback, and execution links of the system in the future working cycle are all carried out under the control of the optimal observation bandwidth, forming a highly closed system adjustment structure with feedback optimization from the observation layer to the control layer. The resulting operating parameters are adapted to the current load mutation or impedance fluctuation conditions and have a certain forward prediction adaptability. Even when the operating conditions change, the optimal state of energy transmission efficiency can be maintained for a long period of time, thereby improving the dynamic stability, control robustness, and energy utilization of the entire energy storage system.

[0113] In an embodiment of the present invention, by establishing an energy storage connector system model based on voltage and current signals, accurate identification of the nonlinear coupling mechanism can be achieved without the need for additional impedance matching sensors, and the impact of nonlinear coupling on the energy storage system is refined into three specific disturbance types: additional coupling loss component, dynamic impedance drift component, and phase coupling offset component. Compared with the rough processing method of the prior art that only considers power factor compensation, a refined identification and quantitative analysis of the disturbance mechanism is achieved. The nonlinear coupling dynamic observer of the present invention adopts a parallel observation architecture of loss observation channel, impedance drift observation channel, and phase offset observation channel, combined with an adaptive weight adjustment mechanism, which can dynamically optimize the observation accuracy according to the actual coupling state, overcoming the problem of insufficient adaptability caused by fixed observation parameters in the prior art. The conduction angle compensation amount and phase synchronization compensation amount are generated by the feedforward compensation algorithm, realizing coordinated control of the energy storage connector switch tube and the phase locking unit. It is fully compatible with the existing controller and does not require the replacement of the existing energy storage power regulation controller, thereby improving the engineering applicability of the system. This invention establishes a complete robust stability analysis framework. Lyapunov stability theory and linear matrix inequality optimization are used to ensure stable operation of the system under complex operating conditions such as sudden load changes and coupled impedance fluctuations. This significantly improves system reliability compared to existing technologies that lack systematic stability assurance. This invention automatically optimizes the coupled observation bandwidth through carrier frequency and harmonic order analysis, establishing a matching relationship between observation bandwidth and transmission efficiency. The system can automatically adjust to the optimal observation bandwidth under different operating conditions, maximizing transmission efficiency and avoiding the efficiency loss caused by fixed bandwidth in existing technologies.

[0114] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or system comprising the element.

[0115] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A high-efficiency energy storage connector dynamic control system based on nonlinear coupling mechanism, characterized in that: include: Establishing a module for establishing an energy storage connector system model based on a voltage signal and a current signal of the energy storage connector; A disturbance identification module is used to identify nonlinear coupling disturbances in the energy transmission process according to the energy storage connector system model, and obtain an additional coupling loss component, a dynamic impedance drift component, and a phase coupling offset component; A dynamic observation module, configured to input the additional coupling loss component, the dynamic impedance drift component, and the phase coupling offset component into a nonlinear coupling dynamic observer for weighted analysis to obtain a coupling mechanism observation result; A generation module is used to generate an energy transmission path control instruction including a conduction angle compensation amount and a phase synchronization compensation amount based on the coupling mechanism observation result.

2. The high-efficiency energy storage connector dynamic control system based on nonlinear coupling mechanism according to claim 1 is characterized in that: The establishment module also includes: A frequency domain analysis unit, used to perform frequency domain analysis on the voltage signal of the energy storage connector to obtain voltage characteristic parameters; an effective value calculation unit, configured to perform effective value calculation on the current signal of the energy storage connector to obtain current characteristic parameters; A dynamic impedance calculation unit, configured to calculate a dynamic impedance parameter of the energy storage connector according to the voltage characteristic parameter and the current characteristic parameter; A transmission relationship modeling unit is used to model the power transmission relationship of the energy storage connector based on the dynamic impedance parameter to obtain an energy storage connector system model.

3. The high-efficiency energy storage connector dynamic control system based on nonlinear coupling mechanism according to claim 2 is characterized in that: The transmission relationship modeling unit is specifically used for: Constructing a power transmission basic relationship including an input power term and an output power term according to the dynamic impedance parameter; Performing nonlinear loss calculation on the coupling loss characteristics of the energy storage connector to obtain nonlinear coupling loss parameters; The power transmission basic relationship is processed by adding a coupling impedance change term based on the nonlinear coupling loss parameter to obtain a power transmission coupling relationship, and an energy storage connector system model reflecting the dynamic change characteristics of the energy storage connector coupling impedance is created based on the power transmission coupling relationship.

4. The high-efficiency energy storage connector dynamic control system based on nonlinear coupling mechanism according to claim 1 is characterized in that: The disturbance identification module is specifically used for: Performing real-time power monitoring of the energy transmission process based on the energy storage connector system model to obtain power difference data; Separating and identifying nonlinear coupling disturbance features according to the power difference data to obtain disturbance feature classification results including loss disturbance, impedance disturbance, and phase disturbance; Performing a correlation calculation process on the loss-type disturbance in the disturbance feature classification result to obtain an additional coupling loss component related to the nonlinear coupling characteristic of the energy storage connector; Calculating the load change rate of the impedance disturbance in the disturbance feature classification result to obtain a dynamic impedance drift component; A phase offset is calculated for the phase-type disturbance in the disturbance feature classification result to obtain a phase coupling offset component.

5. The high-efficiency energy storage connector dynamic control system based on nonlinear coupling mechanism according to claim 1 is characterized in that: The dynamic observation module also includes: a disturbance intensity analysis unit, configured to input the additional coupling loss component, the dynamic impedance drift component, and the phase coupling offset component into a nonlinear coupling dynamic observer to perform disturbance intensity analysis and obtain disturbance intensity data; A weight coefficient calculation unit, configured to calculate observer weight coefficients based on the disturbance intensity data to obtain adaptive weight coefficient groups corresponding to the additional coupling loss component, the dynamic impedance drift component, and the phase coupling offset component, respectively; The parallel observation unit is used to perform parallel observation on the loss observation channel, impedance drift observation channel and phase offset observation channel of the nonlinear coupling dynamic observer based on the adaptive weight coefficient group to obtain a coupling mechanism observation result.

6. The high-efficiency energy storage connector dynamic control system based on nonlinear coupling mechanism according to claim 5 is characterized in that: The parallel observation unit is specifically used for: performing weight configuration processing on the loss observation channel, the impedance drift observation channel, and the phase offset observation channel of the nonlinear coupling dynamic observer according to the adaptive weight coefficient group to obtain a channel configuration parameter group; Performing Kalman filtering observation on the additional coupling loss component based on the loss channel weight parameter in the channel configuration parameter group to obtain additional coupling loss observation data; Performing extended Kalman filtering observation on the dynamic impedance drift component based on the impedance drift channel weight parameter in the channel configuration parameter group to obtain dynamic impedance drift observation data; Performing phase-locked loop tracking observation on the phase coupling offset component based on the phase offset channel weight parameter in the channel configuration parameter group to obtain phase coupling offset observation data; The additional coupling loss observation data, the dynamic impedance drift observation data, and the phase coupling offset observation data are weightedly fused to obtain a coupling mechanism observation result.

7. The high-efficiency energy storage connector dynamic control system based on nonlinear coupling mechanism according to claim 1 is characterized in that: The generation module also includes: A feedforward compensation unit, configured to calculate a feedforward compensation gain based on the coupling mechanism observation result to obtain a feedforward compensation gain group including a loss compensation gain, an impedance drift compensation gain, and a phase offset compensation gain; a compensation angle calculation unit, configured to perform compensation angle calculation on the basic conduction angle of the energy storage connector based on the feedforward compensation gain group to obtain a conduction angle compensation amount; a synchronous adjustment unit, configured to synchronously adjust the reference phase angle according to the phase coupling offset observation data in the coupling mechanism observation result to obtain a phase synchronization compensation amount; An instruction generating unit is used to generate an energy transmission path control instruction including a switch tube control parameter and a phase locking parameter according to the conduction angle compensation amount and the phase synchronization compensation amount.

8. The high-efficiency energy storage connector dynamic control system based on nonlinear coupling mechanism according to claim 7 is characterized in that: The synchronization adjustment unit is specifically used for: Extracting the phase offset amplitude from the phase coupling offset observation data in the coupling mechanism observation results to obtain a phase offset characteristic parameter; Adaptively calculating the synchronization adjustment coefficient based on the phase offset characteristic parameter to obtain the phase synchronization adjustment coefficient; Performing offset compensation on the reference phase angle of the energy storage connector according to the phase synchronization adjustment coefficient to obtain a target phase angle; A phase difference is calculated based on the target phase angle and the reference phase angle to obtain a phase synchronization compensation amount.

9. The high-efficiency energy storage connector dynamic control system based on nonlinear coupling mechanism according to claim 8 is characterized in that: Also includes: a coordination control module, configured to adjust the conduction angle of the switch tube of the energy storage connector based on the switch tube control parameter in the energy transmission path control instruction, and obtain a working state of the switch tube after conduction angle compensation; Performing phase synchronization control on the phase locking unit of the energy storage connector according to the phase locking parameter in the energy transmission path control instruction to obtain a phase locking working state after phase synchronization compensation; Based on the working state of the switch tube and the phase-locked working state, coordinated synchronization is performed to obtain the energy storage transmission control state.

10. The high-efficiency energy storage connector dynamic control system based on nonlinear coupling mechanism according to claim 9 is characterized in that: Also includes: A bandwidth optimization module is configured to monitor the transmission efficiency of the energy storage connector under conditions of sudden energy storage load changes and coupling impedance fluctuations based on the energy storage transmission control state, thereby obtaining transmission efficiency monitoring data including transmission efficiency values ​​for multiple conditions; perform carrier frequency and harmonic order analysis on the correlation between the coupling observation bandwidth and the transmission efficiency based on the transmission efficiency monitoring data, thereby obtaining bandwidth efficiency relationship data reflecting the matching characteristics of the observation bandwidth and the transmission efficiency; perform a switching frequency correlation search on the optimal observation bandwidth based on the bandwidth efficiency relationship data, thereby obtaining optimal observation bandwidth parameters that maximize the transmission efficiency of the energy storage connector; and perform transmission efficiency optimization on the operating parameters of the energy storage connector based on the optimal observation bandwidth parameters, thereby obtaining optimal transmission efficiency operating parameters suitable for conditions of sudden load changes and impedance fluctuations.

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