A network-constructing type adaptive virtual synchronous control method and device

Abstract

The application discloses a network-constructing type adaptive virtual synchronous control method and device, and applies to the field of power electronics technology. The method obtains real-time terminal voltage by constructing a battery terminal voltage equation, and obtains real-time voltage value based on a dynamic equation of a DC bus voltage constructed according to an energy balance principle; a recursive iteration algorithm is adopted to correct a parameter vector, and a real-time value of an ohmic internal resistance is obtained, according to which a battery health state parameter is determined; adaptive virtual inertia is determined in combination with rated virtual inertia and the DC bus voltage, and a virtual damping coefficient is determined according to a basic damping coefficient, the ohmic internal resistance and a reference internal resistance; frequency dynamic adjustment is realized by constructing a virtual synchronous machine swing equation, and a potential amplitude synchronization instruction is determined in combination with power parameters and voltage parameters, so that the problem that traditional fixed VSG parameters are disconnected from the aging characteristics of the battery is effectively solved, excessive loss of the battery is avoided, bus voltage oscillation and wideband risk are inhibited, and the anti-interference ability of the control system is improved.

Description

Technical Field

[0001] This application relates to the field of power electronics technology, and in particular to a grid-based adaptive virtual synchronization control method and device. Background Technology

[0002] As the global energy structure transitions towards a low-carbon model, the penetration rate of distributed energy storage resources, represented by electric vehicles (EVs), in new power systems is increasing year by year. Through vehicle-to-grid (V2G) technology, electric vehicles can participate as a flexible resource in grid frequency regulation, voltage support, and ancillary services such as black start. In weak grids or microgrids composed of large-scale renewable energy sources, grid-forming control technology, especially virtual synchronous generator (VSG) technology, has become a research hotspot in the V2G field because it can simulate the inertia and damping characteristics of traditional synchronous generators and provide dynamic inertial support for the system.

[0003] Through in-depth analysis of existing grid-connected control schemes and long-term engineering practice, it has been found that current electric vehicle grid-connected control technologies have the following significant technical bottlenecks and loopholes when dealing with the evolution of batteries throughout their entire life cycle and complex operating conditions: 1. Severe disconnect between control parameters and physical lifespan: Existing VSG control technologies typically set virtual moment of inertia and virtual damping to fixed constants, or perform static power distribution solely based on the remaining battery charge. This model completely ignores the evolution of the electrochemical characteristics of the power battery throughout its entire life cycle, especially the increase in internal resistance and weakening of transient power handling capacity caused by the decline in battery health. This leads to the controller blindly using aging batteries for high-frequency support when the grid frequency fluctuates drastically, which not only accelerates battery deterioration but also causes bus voltage collapse due to the significant drop in battery terminal voltage. 2. Energy imbalance coupling in two-stage converters: Traditional grid-connected control methods mostly treat the front-end DC / DC (Direct Current to Direct Current converter) and the rear-end DC / AC (Direct Current to Alternating Current) inverter as independent control links, lacking a closed-loop energy coordination mechanism based on DC bus voltage stability. When the rear-end DC / AC inverter executes high-inertia grid connection commands, if the aging front-end battery cannot provide real-time energy replenishment due to internal resistance limitations, it will cause large-amplitude nonlinear oscillations in the DC bus capacitor voltage. This oscillation is fed back to the grid side through the inverter's duty cycle modulation process, easily inducing wide-frequency oscillations in the 0 to 1000Hz range, causing damage to grid-connected equipment. 3. Sensitivity of inner-loop algorithms to DC-side fluctuations: The inner loop of existing grid-connected converters mostly adopts linear PI (Proportional-Integral) control or traditional integral sliding mode control. These methods perform well when the DC side voltage (bus voltage) remains constant, but their disturbance rejection performance drops sharply when the DC voltage fluctuates significantly due to battery aging conditions. They are also prone to control gain saturation and cannot achieve fast, zero steady-state error tracking and active suppression of complex oscillation components.

[0004] In view of the above-mentioned technologies, finding a collaborative control method with full life cycle adaptability is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a network-based adaptive virtual synchronization control method and device. This can solve the problems in existing technologies such as the severe disconnect between control parameters and physical lifetime, energy loss and imbalance coupling in two-stage converters, and the sensitivity of the inner-loop algorithm to DC-side fluctuations.

[0006] To address the aforementioned technical problems, this application provides a network-based adaptive virtual synchronization control method, applied to a two-stage bidirectional power conversion system consisting of a front-stage interleaved parallel bidirectional DC / DC converter and a rear-stage T-type three-level inverter. The method includes: The battery terminal voltage equation is constructed based on the battery's electrical characteristic parameters, and the battery terminal voltage at the current moment is obtained. Based on the principle of DC bus energy balance including battery terminal voltage, a dynamic equation for DC bus voltage is constructed, and the real-time value of DC bus voltage at the current moment is obtained. The parameter vector is corrected using a recursive iterative algorithm, and the corresponding real-time value of the ohmic internal resistance is obtained. The battery health status parameters are determined based on the real-time value of the ohmic internal resistance, and the adaptive virtual inertia is determined by combining the rated virtual inertia and the real-time value of the DC bus voltage. The corresponding virtual damping coefficient is determined based on the basic damping coefficient, impedance reshaping compensation gain, real-time value of ohmic internal resistance, and reference ohmic internal resistance. The virtual synchronous machine oscillation equation is constructed based on the adaptive virtual inertia and virtual damping coefficient to enable dynamic frequency adjustment; The corresponding potential amplitude synchronization command is determined based on the reactive power reference value, actual output reactive power, grid rated voltage, and public grid point voltage in the virtual synchronous generator corresponding to the virtual synchronous machine swing equation.

[0007] Preferably, the battery terminal voltage equation is constructed based on the battery's electrical characteristic parameters, and the battery terminal voltage at the current moment is obtained, including: Construct a second-order RC broadband equivalent impedance model of the battery corresponding to its electrical characteristic parameters; The terminal voltage equation is constructed based on the second-order RC broadband equivalent impedance model of the battery. The expression for the terminal voltage equation is as follows: ; It is a function of the battery terminal voltage; This is the open-circuit voltage; It is an open-circuit current function; The internal resistance is ohmic; It is an electrochemical polarization voltage function; This is the concentration polarization voltage function.

[0008] Preferably, the expression for the dynamic equation of the DC bus voltage is: ; in, For DC bus capacitors; This is the real-time value of the DC bus voltage; The converter efficiency corresponding to the front-end interleaved parallel bidirectional DC / DC converter; This refers to the battery terminal voltage. Open circuit current; This refers to the electromagnetic power corresponding to the subsequent T-type three-level inverter. This refers to stray power loss.

[0009] Preferably, a recursive iterative algorithm is used to correct the parameter vector and obtain the corresponding real-time value of the ohmic internal resistance, including: The second-order RC broadband equivalent impedance model of the battery is discretized, and the bilinear transformation method is used to map the second-order RC broadband equivalent impedance model from the s-domain to the z-domain. Construct the system transfer function and reconstruct it into a linear regression equation; The recursive least squares algorithm with an introduced forgetting factor is used to iteratively correct the parameter vector in the linear regression equation; Based on the iteratively corrected parameter vector, the corresponding real-time value of the ohmic internal resistance is obtained by using the mapping relationship from the s-domain to the z-domain through the second-order RC broadband equivalent impedance model.

[0010] Preferably, a recursive least squares algorithm incorporating a forgetting factor is used to iteratively correct the parameter vector in the linear regression equation, including: Determine the corresponding output observation value and data vector based on the current battery charging / discharging current and battery terminal voltage; The gain matrix is ​​corrected based on the data vector, the covariance matrix at the previous time step, and the forgetting factor. The parameter vector at the current time step is corrected based on the corrected gain matrix, the output observations, and the parameter vector at the previous time step.

[0011] Preferably, the battery health status parameters are determined based on the real-time value of the ohmic internal resistance, and the adaptive virtual inertia is determined in combination with the rated virtual inertia and the real-time value of the DC bus voltage, including: Based on the real-time value of the ohmic internal resistance, combined with the reference ohmic internal resistance and the internal resistance threshold, the corresponding health state parameters are determined by the battery health state parameter equation constructed through the impedance increase rate. Based on the health status parameters and the rated virtual inertia as a benchmark, an adaptive virtual inertia equation is constructed by introducing a self-regulating attenuation factor based on the health status and an energy coordination factor based on DC bus voltage feedback, and the corresponding adaptive virtual inertia is determined. The expression for the battery health state parameter equation is as follows: ; The expression for the adaptive virtual inertia equation is: ; in, For health status parameters; The internal resistance threshold; This is the real-time value of the ohmic internal resistance; The reference internal resistance is ohms; For adaptive virtual inertia; This is the rated virtual inertia; It is the self-decrease factor; It is an energy synergist.

[0012] Preferably, the expression corresponding to the virtual damping coefficient is: ; in, This is the virtual damping coefficient; The basic damping coefficient; To compensate for impedance reshaping gain; This is the real-time value of the ohmic internal resistance; The reference internal resistance is ohms.

[0013] Preferably, a virtual synchronous machine oscillation equation is constructed based on the adaptive virtual inertia and virtual damping coefficient to enable dynamic frequency adjustment, including: Based on adaptive virtual inertia and combined with virtual damping coefficient, the swing equation of virtual synchronous machine is reconstructed, wherein the swing equation of virtual synchronous machine satisfies the frequency-active power adaptive adjustment logic. When the grid frequency fluctuates, frequency-active power adaptive adjustment is achieved through the virtual synchronous machine swing equation; The expression for the virtual synchronizer's oscillation equation is as follows: ; in, For adaptive virtual inertia; This is the rated angular frequency; This refers to the virtual rotational angular frequency. This is a reference value for active power. Electromagnetic output power; This is the virtual damping coefficient.

[0014] Preferably, the corresponding potential amplitude synchronization command is determined based on the reactive power reference value, actual output reactive power, grid rated voltage, and public grid point voltage in the virtual synchronous generator corresponding to the virtual synchronous machine swing equation, including: Based on the operating status of the virtual synchronous generator corresponding to the swing equation of the virtual synchronous machine, the reactive power reference value, actual output reactive power, grid rated voltage and public grid point voltage of the virtual synchronous generator are collected. A voltage control strategy incorporating droop characteristics is adopted to construct a synchronous command equation for potential amplitude. Substitute the reactive power reference value, actual output reactive power, grid rated voltage, and public grid point voltage into the potential amplitude synchronization command equation to obtain the potential amplitude synchronization command. The expression for the potential amplitude synchronization command equation is as follows: ; in, This is a command to synchronize the potential amplitude. Rated internal potential amplitude; This is the reactive power droop coefficient; This is a reference value for reactive power. This refers to the actual output reactive power. The voltage-supported gain coefficient; This is the rated voltage of the power grid; This refers to the voltage at the public network point.

[0015] On the other hand, this application also provides a network-based adaptive virtual synchronization control device, including a memory for storing computer programs; The processor is used to implement the steps of the above-described network-based adaptive virtual synchronization control method when executing computer programs.

[0016] On the other hand, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described network-type adaptive virtual synchronization control method.

[0017] This application provides a network-based adaptive virtual synchronous control method applied to a two-stage bidirectional power conversion system consisting of a front-stage interleaved parallel bidirectional DC / DC converter and a rear-stage T-type three-level inverter. The method includes: constructing the battery terminal voltage equation based on the battery's electrical characteristic parameters to obtain the battery terminal voltage at the current moment; constructing the DC bus voltage dynamic equation based on the DC bus energy balance principle including the battery terminal voltage to obtain the corresponding real-time value of the DC bus voltage at the current moment; correcting the parameter vector using a recursive iterative algorithm to obtain the corresponding real-time value of the ohmic internal resistance; and based on the ohmic... The real-time value of the internal resistance determines the battery's health status parameters, and the adaptive virtual inertia is determined by combining the rated virtual inertia and the real-time value of the DC bus voltage. The corresponding virtual damping coefficient is determined based on the basic damping coefficient, impedance reshaping compensation gain, real-time value of the internal resistance, and reference internal resistance. The virtual synchronous machine swing equation is constructed based on the adaptive virtual inertia and the virtual damping coefficient to enable dynamic frequency adjustment. The corresponding potential amplitude synchronization command is determined based on the reactive power reference value, actual output reactive power, grid rated voltage, and public grid point voltage in the virtual synchronous generator corresponding to the virtual synchronous machine swing equation. Therefore, this application achieves dynamic voltage control by constructing a terminal voltage model based on battery electrical characteristic parameters and combining it with DC bus energy balance. It identifies the battery's ohmic internal resistance in real time to accurately reflect its health status, and dynamically adjusts the adaptive virtual inertia and virtual damping coefficient accordingly. This effectively solves the problems of traditional fixed VSG parameters being disconnected from battery aging characteristics, blindly supporting high-frequency power to accelerate battery loss, and causing bus voltage collapse. Simultaneously, through the energy coordination closed loop between the two-stage converters, it avoids large bus voltage oscillations caused by decoupling of control between DC / DC and DC / AC stages, suppressing the risk of wideband oscillations. Furthermore, relying on adaptive parameter adjustment and synchronous machine potential amplitude command optimization, it significantly improves the anti-interference capability of the control system under DC-side voltage fluctuations, avoids control saturation, and achieves a comprehensive beneficial effect that balances battery lifecycle safety, DC bus stability, and grid-friendly support. Attached Figure Description

[0018] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a network-based adaptive virtual synchronization control method provided in this application embodiment; Figure 2 This application provides a schematic diagram of the main circuit topology of a two-stage bidirectional converter. Figure 3This is a structural diagram of a network-type adaptive virtual synchronization control device provided in another embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0021] The core of this application is to provide a network-based adaptive virtual synchronization control method and device.

[0022] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Figure 1 This is a flowchart illustrating a network-based adaptive virtual synchronization control method provided in an embodiment of this application. Figure 2 The main circuit topology diagram of the two-stage bidirectional converter provided in the embodiments of this application is as follows: Figure 1 A diagram of the system topology to which the method is applied. (e.g.) Figure 2As shown, the front-end interleaved parallel bidirectional DC / DC converter consists of two symmetrical power branches, including inductors L1 and L2 and bridge arms composed of power transistors Q1-Q4. The mechanism of the interleaved parallel technology is that by controlling the drive signals of the two converters to be interleaved by 180° in time, the ripple currents of the two inductors are physically canceled at the common node. This topology can significantly reduce the total ripple current flowing through the power battery. Studies have shown that high-frequency current ripple can cause additional Joule heat to be generated inside the battery and accelerate the degradation of the polarization layer. Therefore, this application achieves preliminary delay of battery SOH (State of Health) degradation at the hardware level, providing hardware redundancy for the life protection of the subsequent algorithm layer. The rear-end T-Type 3-Level inverter adopts a three-level topology and achieves flexible and controlled access to the DC bus midpoint through the midpoint clamping switches (Q6-Q11). Compared to traditional two-level inverters, three-level topologies can output five voltage levels (considering zero-level and half-level switching). The increased number of steps in the output voltage waveform makes the output voltage closer to a sine wave, thus significantly reducing the inductance of the output filter (LCL). More importantly, when executing high-frequency inertia response commands from a Virtual Synchronous Generator (VSG), the three-level structure offers finer and more stable voltage regulation steps, enabling more precise reshaping of the system's output impedance. Furthermore, Figure 2 The remaining structures are all based on the general architecture of a conventional electric vehicle network control system, including capacitor and inductor components, SVPWM (Space Vector Pulse Width Modulation) modules, Conversion, The bidirectional charger control includes functions such as conversion, dual closed-loop voltage and current control, power calculation, and bidirectional charger control. , , , as well as Equal coefficients. Apart from this, the principles of conventional electric vehicle grid-type control systems will not be elaborated upon here.

[0024] like Figure 1 As shown, its S10: Construct the battery terminal voltage equation based on the battery's electrical characteristic parameters, and obtain the battery terminal voltage at the current moment.

[0025] S11: Construct the dynamic equation of DC bus voltage based on the DC bus energy balance principle including battery terminal voltage, and obtain the real-time value of DC bus voltage at the current moment.

[0026] In a specific embodiment, a terminal voltage equation is constructed based on the battery's electrical characteristic parameters (including open-circuit voltage, polarization voltage, etc.). By combining the correlation between the battery's electrical characteristic parameters and the terminal voltage, a terminal voltage calculation model is constructed by accurately capturing the battery's electrical characteristic parameters, thereby obtaining the battery's terminal voltage at the current moment in real time. At the same time, relying on this terminal voltage data and combining the DC bus energy balance principle, the coordinated linkage between the terminal voltage and the DC bus voltage is realized, providing reliable data support for subsequent virtual synchronous control.

[0027] The core of steps S10-S11 is to lay the foundation for subsequent ohmic internal resistance identification, virtual inertia adjustment, and frequency and voltage stability control by accurately calculating the battery terminal voltage. This effectively solves the problems of fixed parameters and inability to adapt to battery aging and voltage fluctuations in traditional control, ensuring the stability and reliability of the entire control link. At the same time, it provides accurate data for subsequent potential synchronization control, taking into account both battery life protection and system operation stability, and adapting to the complex operating conditions required in V2G scenarios.

[0028] S12: The parameter vector is corrected by a recursive iterative algorithm, and the corresponding real-time value of the ohmic internal resistance is obtained.

[0029] In a specific embodiment, based on the discretized model and linear regression scheme related to the battery terminal voltage equation, the parameter vector containing battery physical parameter information is dynamically corrected by using real-time acquired battery electrical signal data through a recursive iterative algorithm. Then, the real-time value of the ohmic internal resistance is obtained by solving the mapping relationship between the parameter vector and the ohmic internal resistance.

[0030] The key to this step is to use a recursive iterative approach to achieve real-time correction of the parameter vector, avoiding the data saturation problem of traditional algorithms. This ensures that the parameter vector can be dynamically updated according to the battery's operating conditions and aging status, thereby accurately extracting the ohmic internal resistance. This enables real-time and accurate acquisition of the battery's ohmic internal resistance value, providing crucial sensing data for subsequent battery health status assessment and virtual synchronizer parameter adjustment. It solves the problem that traditional fixed parameters cannot adapt to the battery's entire life cycle evolution, providing reliable support for battery life protection and system stability control.

[0031] S13: Determine the battery's health status parameters based on the real-time value of the ohmic internal resistance, and determine the adaptive virtual inertia by combining the rated virtual inertia and the real-time value of the DC bus voltage.

[0032] In a specific embodiment, the characteristic that the ohmic internal resistance increases linearly or non-linearly with battery aging is utilized. The battery health status parameters are determined by the quantitative relationship between the real-time value of the ohmic internal resistance and the reference internal resistance and the end-of-life threshold. Then, combined with the constraint requirements of the battery health status on the virtual inertia and the real-time fluctuation state of the DC bus voltage, the rated virtual inertia is dynamically corrected to obtain the adaptive virtual inertia.

[0033] The key point of this step is to use the battery health state parameter and the real-time value of the DC bus voltage as the adjustment basis for virtual inertia, so as to achieve the linkage adaptation of virtual inertia with the battery state and the system voltage state. Thus, the virtual inertia can be adapted to the full life cycle state of the battery, avoiding high-frequency power support by aging batteries, preventing excessive battery loss and DC bus voltage collapse, while taking into account the grid frequency support requirements, and achieving the dual goals of battery life protection and stable system operation.

[0034] S14: Determine the corresponding virtual damping coefficient according to the basic damping coefficient, impedance reshaping compensation gain, real-time value of ohmic internal resistance, and reference ohmic internal resistance.

[0035] In a specific embodiment, based on the deviation between the real-time value of the battery ohmic internal resistance and the reference ohmic internal resistance, combined with the basic damping coefficient and the impedance reshaping compensation gain, the virtual damping coefficient is dynamically corrected through quantitative calculation, that is, the virtual damping coefficient is obtained, so as to achieve the adaptation of the damping characteristic to the battery aging state.

[0036] The key point of this step is to introduce the real-time value of ohmic internal resistance as the correction basis for the damping coefficient, cancel the negative damping effect caused by battery aging through the impedance reshaping compensation gain, and optimize the system damping characteristic. Thus, the system damping can be actively reshaped, the risk of broadband oscillation caused by the high internal resistance of aging batteries can be cancelled, the power fluctuation can be suppressed, the frequency adjustment process of the virtual synchronous machine can be ensured to be stable without overshoot, and the operation stability of the entire two-stage power conversion system can be improved to adapt to the requirements of V2G complex working conditions.

[0037] S15: Construct a virtual synchronous machine swing equation according to the adaptive virtual inertia and the virtual damping coefficient for frequency dynamic adjustment.

[0038] In a specific embodiment, taking the adaptive virtual inertia obtained in S13 and the virtual damping coefficient obtained in S14 as the core parameters, the rotor motion equation (virtual synchronous machine swing equation) of the virtual synchronous machine is reconstructed. By quantifying the correlation between virtual inertia, virtual damping, virtual rotational angular frequency, active power, etc., the adaptive adjustment of frequency and active power is achieved.

[0039] The key point of this step is to incorporate the adaptive virtual inertia and the virtual damping coefficient into the swing equation, so that the swing equation can be dynamically adjusted following the battery state, breaking the limitation of the traditional fixed parameter swing equation. Thus, the dynamic adjustment of the grid frequency can be achieved, taking into account the grid frequency stability and battery life protection, avoiding damage to aging batteries caused by high-frequency power support, while suppressing power fluctuations, preventing system instability, and ensuring the reliable operation of the two-stage power conversion system in the V2G scenario.

[0040] S16: Determine the corresponding potential amplitude synchronization command based on the reactive power reference value, actual output reactive power, grid rated voltage, and public grid point voltage in the virtual synchronous generator corresponding to the virtual synchronous machine swing equation.

[0041] In a specific embodiment, based on the operating state corresponding to the swing equation of the virtual synchronous machine, the reactive power reference value, the actual output reactive power, the grid rated voltage and the public grid point voltage are collected. By constructing a voltage control model that includes droop characteristics, the correlation between each parameter and the potential amplitude synchronization command is quantified, and the potential amplitude synchronization command is obtained by solving.

[0042] The key to this step is to combine the virtual synchronous machine's operating status with the introduction of droop regulation characteristics to achieve coordinated control of reactive power and grid voltage, adapting to the needs of multi-machine parallel operation. This enables the precise generation of potential amplitude synchronization commands to control the inverter's output voltage, achieving grid voltage support and reactive power sharing, avoiding grid voltage fluctuations. Simultaneously, in conjunction with the aforementioned adaptive parameter adjustment, it further enhances system operational stability and adapts to the complex grid conditions in V2G scenarios.

[0043] This application provides a network-based adaptive virtual synchronous control method applied to a two-stage bidirectional power conversion system consisting of a front-stage interleaved parallel bidirectional DC / DC converter and a rear-stage T-type three-level inverter. The method includes: constructing the battery terminal voltage equation based on the battery's electrical characteristic parameters to obtain the battery terminal voltage at the current moment; constructing the DC bus voltage dynamic equation based on the DC bus energy balance principle including the battery terminal voltage to obtain the corresponding real-time value of the DC bus voltage at the current moment; correcting the parameter vector using a recursive iterative algorithm to obtain the corresponding real-time value of the ohmic internal resistance; and based on the ohmic... The real-time value of the internal resistance determines the battery's health status parameters, and the adaptive virtual inertia is determined by combining the rated virtual inertia and the real-time value of the DC bus voltage. The corresponding virtual damping coefficient is determined based on the basic damping coefficient, impedance reshaping compensation gain, real-time value of the internal resistance, and reference internal resistance. The virtual synchronous machine swing equation is constructed based on the adaptive virtual inertia and the virtual damping coefficient to enable dynamic frequency adjustment. The corresponding potential amplitude synchronization command is determined based on the reactive power reference value, actual output reactive power, grid rated voltage, and public grid point voltage in the virtual synchronous generator corresponding to the virtual synchronous machine swing equation. Therefore, this application achieves dynamic voltage control by constructing a terminal voltage model based on battery electrical characteristic parameters and combining it with DC bus energy balance. It identifies the battery's ohmic internal resistance in real time to accurately reflect its health status, and dynamically adjusts the adaptive virtual inertia and virtual damping coefficient accordingly. This effectively solves the problems of traditional fixed VSG parameters being disconnected from battery aging characteristics, blindly supporting high-frequency power to accelerate battery loss, and causing bus voltage collapse. Simultaneously, through the energy coordination closed loop between the two-stage converters, it avoids large bus voltage oscillations caused by decoupling of control between DC / DC and DC / AC stages, suppressing the risk of wideband oscillations. Furthermore, relying on adaptive parameter adjustment and synchronous machine potential amplitude command optimization, it significantly improves the anti-interference capability of the control system under DC-side voltage fluctuations, avoids control saturation, and achieves a comprehensive beneficial effect that balances battery lifecycle safety, DC bus stability, and grid-friendly support.

[0044] Based on the above embodiments, as a preferred embodiment, the battery terminal voltage equation is constructed based on the battery's electrical characteristic parameters to obtain the battery terminal voltage at the current moment, including: Construct a second-order RC broadband equivalent impedance model of the battery corresponding to its electrical characteristic parameters; The terminal voltage equation is constructed based on the second-order RC broadband equivalent impedance model of the battery. The expression for the terminal voltage equation is as follows: ; It is a function of the battery terminal voltage; This is the open-circuit voltage; It is an open-circuit current function; The internal resistance is ohmic; It is an electrochemical polarization voltage function; This is the concentration polarization voltage function.

[0045] In a specific embodiment, considering the impedance evolution characteristics of an electric vehicle's power battery throughout its entire life cycle, this application does not employ a simple linear internal resistance model. Instead, it constructs the following second-order RC broadband equivalent impedance model, which can capture the battery's dynamic response in the range from milliseconds (electrochemical polarization) to seconds (concentration polarization): ; Its open circuit voltage It primarily characterizes the open-circuit voltage under thermodynamic equilibrium conditions within the battery and is a nonlinear function of the State of Charge (SOC); Ohmic internal resistance It is the core sensing variable of this application. It mainly consists of electrolyte resistance, tab resistance and contact resistance. It increases linearly or non-linearly with the increase of battery cycle number (aging) and is the most critical physical criterion for evaluating SOH.

[0046] In this formula, This is the electrochemical polarization voltage; The concentration polarization voltage has the following time-domain differential equation (state-space equation): ; ; ; in, The rate of change of electrochemical polarization voltage over time, i.e., the electrochemical polarization voltage. The first derivative with respect to time; The rate of change of concentration polarization voltage over time, i.e., concentration polarization voltage The first derivative with respect to time; and The electrochemical kinetics of the charge transfer process are described; and The process of ion diffusion of active substances is described; This is the open-circuit current.

[0047] Based on the above content and formula, it can be seen that the formula can completely map out the ohmic internal resistance when the battery ages (SOH decreases). The increase not only generates heat, but also affects the open circuit current. A sudden change (VSG inertial output) produces a significant instantaneous voltage drop, thus limiting the upper limit of the battery's active power throughput.

[0048] Based on the above embodiments, in a preferred embodiment, the expression for the dynamic equation of the DC bus voltage is as follows: ; in, For DC bus capacitors; This is the real-time value of the DC bus voltage; The converter efficiency corresponding to the front-end interleaved parallel bidirectional DC / DC converter; This refers to the battery terminal voltage. Open circuit current; This refers to the electromagnetic power corresponding to the subsequent T-type three-level inverter. This refers to stray power loss.

[0049] In a specific embodiment, the capacitor on the DC-link between the front-end interleaved parallel bidirectional DC / DC converter and the rear-end T-type three-level inverter undertakes the task of energy decoupling and transient buffering. Therefore, this application establishes a dynamic equation for the DC bus voltage based on the power conservation law (which can also be understood as a dynamic model of the bus voltage) to reveal the instability mechanism caused by the energy imbalance between the source and the grid. ; This model reveals the root cause of system-level oscillations: in a weak power grid environment, when the power grid frequency fluctuates, the VSG algorithm generates a value related to the rate of frequency change. proportional pulse power demand If the battery is in a low SOH state (severe aging) at this time, its ohmic internal resistance will be... The voltage is relatively high, resulting in a higher battery terminal voltage. It drops significantly as output current increases. Maximum available power of the current stage. Unable to cover At the instantaneous peak value, the real-time value of the DC bus voltage A forced descent is necessary to release stored energy in the capacitors to compensate for the power deficit. This "power supply deficit" caused by battery aging will affect the real-time value of the DC bus voltage. The system experiences severe fluctuations. These fluctuations, through control loop feedback, cause the system to exhibit "negative damping" characteristics, which in turn induces broadband oscillations or even voltage collapse in the cascaded system. Therefore, establishing this model is the logical starting point for the subsequent steps of "dynamically correcting virtual inertia and virtual damping coefficients based on SOH".

[0050] Based on the above embodiments, as a preferred embodiment, a recursive iterative algorithm is used to correct the parameter vector and obtain the corresponding real-time value of the ohmic internal resistance, including: The second-order RC broadband equivalent impedance model of the battery is discretized, and the bilinear transformation method is used to map the second-order RC broadband equivalent impedance model from the s-domain to the z-domain. Construct the system transfer function and reconstruct it into a linear regression equation; The recursive least squares algorithm with an introduced forgetting factor is used to iteratively correct the parameter vector in the linear regression equation; Based on the iteratively corrected parameter vector, the corresponding real-time value of the ohmic internal resistance is obtained by using the mapping relationship from the s-domain to the z-domain through the second-order RC broadband equivalent impedance model.

[0051] Furthermore, a recursive least squares algorithm incorporating a forgetting factor is used to iteratively correct the parameter vector in the linear regression equation, including: Determine the corresponding output observation value and data vector based on the current battery charging / discharging current and battery terminal voltage; The gain matrix is ​​corrected based on the data vector, the covariance matrix at the previous time step, and the forgetting factor. The parameter vector at the current time step is corrected based on the corrected gain matrix, the output observations, and the parameter vector at the previous time step.

[0052] In a specific embodiment, based on the second-order RC broadband equivalent impedance model of the battery, its complex frequency domain impedance expression is as follows: ; Among them, the discretization operator ; The sampling period.

[0053] Furthermore, the relationship between battery voltage and current is transformed into the following difference equation form: ; in, The output observation value (which can also be understood as the terminal voltage drop); The battery charging and discharging current function; and This refers to the system's "pole" information (battery dynamic characteristics), which can also be understood as the system's output feedback coefficient. , and The weighting factor is the input current.

[0054] To facilitate recursive solution, the above equation is reconstructed into a linear regression equation: ; Its data vector A collection containing historical sampling data is defined as: ; Parameter vector A set containing information about the physical parameters to be identified is defined as: ; Noise item Characterize measurement noise and modeling errors.

[0055] The regression model described above transforms the original nonlinear parameter identification problem based on electrochemical mechanisms into a problem concerning parameter vectors. For linear regression problems, the parameters can be estimated online using least squares or recursive least squares methods, thus avoiding the direct solution of complex electrochemical models and improving computational efficiency and engineering feasibility.

[0056] Mapping: Further, by performing a Z-transform based on the difference equation, the discrete impedance expression of the system can be obtained as follows: ; Under high frequency conditions ( ),have Therefore, the system impedance approaches the ohmic internal resistance, that is: ; in, This is the real-time value of the ohmic internal resistance.

[0057] In actual V2G operation, battery parameters fluctuate slowly with changing conditions. Traditional least squares methods suffer from "data saturation," meaning that as the number of sampling points increases, the influence of new data on the results tends to zero. This application addresses this by introducing a forgetting factor. The weight of historical data is dynamically adjusted to ensure the real-time tracking of the identification results.

[0058] Utilizing the real-time acquired battery charging and discharging current function (which can also be understood as charging and discharging current). And the battery terminal voltage function (which can also be understood as the battery terminal voltage). The closed-loop iteration follows the logic below: Gain matrix update: Calculate the identification gain (gain matrix) at the current time step. This determines the contribution of the current residual to the parameter correction; ; Parameter vector update: Correct the parameter estimates in real time based on the current measurement error, that is, obtain the corrected parameter vector. ; ; Covariance matrix update: Update the uncertainty matrix of the error : ; in, Let this be the covariance matrix at the current moment; This is the covariance matrix corresponding to the previous time step; This is the parameter vector at the current moment (after correction); This is the parameter vector from the previous time step.

[0059] In addition, the forgetting factor in this application The value is set between 0.96 and 0.99. The principle is as follows: if the forgetting factor... The smaller the forgetting factor, the faster the algorithm tracks parameter changes, but it is sensitive to measurement noise (such as high-frequency noise from current sensors); When the value approaches 1, the algorithm's stability is high, but its sensitivity decreases. By finely adjusting the value within the range of 0.96-0.99, this application accurately captures the milliohm-level internal resistance drift caused by battery aging while suppressing inverter switching noise in the V2G system.

[0060] After completing parameter identification, this application extracts parameters from the parameter vector. The real-time value of the ohmic internal resistance is obtained from the analysis. Its real-time ohmic internal resistance value It is the most direct indicator of battery conductivity, and its increase directly reflects aging phenomena such as electrolyte depletion and SEI film thickening inside the battery.

[0061] Based on the above embodiments, as a preferred embodiment, the battery health status parameters are determined according to the real-time value of the ohmic internal resistance, and an adaptive virtual inertia is determined by combining the rated virtual inertia and the real-time value of the DC bus voltage, including: Based on the real-time value of the ohmic internal resistance, combined with the reference ohmic internal resistance and the internal resistance threshold, the corresponding health state parameters are determined by the battery health state parameter equation constructed through the impedance increase rate. Based on the health status parameters and the rated virtual inertia as a benchmark, an adaptive virtual inertia equation is constructed by introducing a self-regulating attenuation factor based on the health status and an energy coordination factor based on DC bus voltage feedback, and the corresponding adaptive virtual inertia is determined. The expression for the battery health state parameter equation is as follows: ; The expression for the adaptive virtual inertia equation is: ; in, For health status parameters; The internal resistance threshold; This is the real-time value of the ohmic internal resistance; The reference internal resistance is ohms; For adaptive virtual inertia; This is the rated virtual inertia; It is the self-decrease factor; It is an energy synergist.

[0062] In a specific embodiment, this application defines a battery health state parameter equation based on the impedance increase rate: ; Among them, the reference ohmic internal resistance Primarily provided by laboratory testing or the battery manufacturer's manual; internal resistance threshold. This mainly refers to the internal resistance threshold at the end of the battery's service life. In automotive industry standards, the internal resistance is typically increased to twice the initial value (i.e., (This is defined as the end of life.)

[0063] The resulting health status parameters It is used not only to display battery health but also as a real-time reference variable for correcting virtual synchronizer parameters in subsequent steps. This deep closed loop of "perception-decision" is the key data foundation for solving the grid connection stability of aging batteries in this invention.

[0064] Due to virtual inertia The active power response speed of the grid converter during frequency change transients is determined. For aging batteries, excessive internal resistance can cause problems. This can cause a sharp drop in terminal voltage, leading to system instability. Therefore, this application achieves intelligent reduction of inertia through the following adaptive law: ; Among them, the self-discharge factor based on health status : This application uses a Sigmoid-type nonlinear function to describe the smooth evolution of inertial demand as the battery ages: ; in, To adjust the sensitivity coefficient, the rate at which the inertia deteriorates with aging is determined; This is the decay trigger threshold (usually a value of 0.7-0.8).

[0065] When the battery is in the initial stage of its entire life cycle (SOH is close to 1), Approaching 1, the system maintains a large inertia to support the grid frequency; as the battery ages, it enters an "accelerated degradation period". The nonlinearity decreases with the Sigmoid curve. The advantage of this nonlinear regulation is that it can maximize the inertial support when the battery is healthy, and sensitively reduce the transient power peak in the later stage of battery aging, protecting the fragile aging battery from the impact of large current.

[0066] Its energy coordination factor based on DC bus voltage feedback : To protect the DC-side energy balance in real time during dynamic processes, this application introduces a bus voltage collaborative feedback mechanism as a measure of virtual inertia. Second revision: ; in, Dead zone; This represents the change in the real-time value of the DC bus voltage. For index; This is the reference value for the DC bus voltage.

[0067] Therefore, it can be concluded that when the real-time value of the DC bus voltage is detected... Deviation from rated value exceeds dead zone This indicates that the front-end battery, limited by its aging internal resistance, is no longer able to provide real-time inertia support for the rear-end battery. Rapidly reduce, forcing the downstream VSG to reduce "virtual inertia requirements".

[0068] This factor achieves energy balance through "active adaptation of the downstream stage to the upstream stage." This is achieved by instantaneously reducing... The system actively released the power demand on the DC capacitor, effectively preventing system collapse caused by a "cliff-like" drop in DC voltage.

[0069] Based on the above embodiments, as a preferred embodiment, the expression corresponding to the virtual damping coefficient is: ; in, This is the virtual damping coefficient; The basic damping coefficient; To compensate for impedance reshaping gain; This is the real-time value of the ohmic internal resistance; The reference internal resistance is ohms.

[0070] In a specific embodiment, the ohmic internal resistance of the power battery The increase in internal resistance not only limits power but also causes "impedance mismatch" in two-stage cascaded systems. Studies have shown that the high internal resistance of aged batteries, under the feedback effect of the control loop, leads to a negative damping effect in the system within a specific frequency band (10Hz-500Hz), which is the main cause of broadband oscillations. Therefore, this application achieves dynamic reconstruction of the virtual damping coefficient by injecting a compensation component based on the internal resistance increment. ; Among them, the damping hedging logic: the ohmic internal resistance of the battery physical layer Increasing this is equivalent to introducing additional power dissipation or phase lag in the upstream stage of the system. This application addresses this by real-time adjustment of the virtual damping coefficient at the control level. An equivalent "positive damping" component is generated in the control closed loop to offset the "negative damping" trend caused by the aging of the physical layer.

[0071] In other words, by adjusting the damping coefficient in real time, the closed-loop poles of the system can be forcibly pushed deeper into the left half of the complex plane. This adaptive damping reshaping technology enables the grid-connected system to maintain positive damping characteristics throughout the entire battery life cycle, effectively eliminating the wide-frequency oscillations triggered by battery aging and ensuring the grid stability of the system under extremely weak power grid conditions.

[0072] Based on the above embodiments, as a preferred embodiment, a virtual synchronous machine oscillation equation is constructed according to the adaptive virtual inertia and virtual damping coefficient to enable dynamic frequency adjustment, including: Based on adaptive virtual inertia and combined with virtual damping coefficient, the swing equation of virtual synchronous machine is reconstructed, wherein the swing equation of virtual synchronous machine satisfies the frequency-active power adaptive adjustment logic. When the grid frequency fluctuates, frequency-active power adaptive adjustment is achieved through the virtual synchronous machine swing equation; The expression for the virtual synchronizer's oscillation equation is as follows: ; in, For adaptive virtual inertia; This is the rated angular frequency; This refers to the virtual rotational angular frequency. This is a reference value for active power. Electromagnetic output power; This is the virtual damping coefficient.

[0073] In a specific embodiment, this invention reconstructs the virtual synchronous machine swing equation (rotor motion equation), enabling the converter to sense the battery's physiological limits and spontaneously adjust its power throughput when the grid frequency fluctuates. Its mathematical expression and physical mechanism are as follows: ; in, It reflects the degree to which the system deviates from the synchronous speed; This refers to the electromagnetic output power, specifically the electromagnetic output power actually measured and fed back by the converter. This is the active power reference value, specifically the active power reference command issued by the upper-level energy management system.

[0074] Therefore, it can be concluded that when the power grid frequency decreases ( When the equation is on the left, compensation power is generated; energy output control driven by health state: if the battery SOH is high, adaptive virtual inertia is used. Maintaining a high voltage level, the converter exhibits strong inertia, capable of instantly releasing a large amount of charge to support grid frequency stability; if the battery ages (low state of equilibrium), it adapts to virtual inertia. The value is automatically reduced. At this time, although the system reduces the frequency support strength, it effectively limits the current peak of the aging battery during transient processes. Through the injection of this physical constraint, this application fundamentally prevents DC-side undervoltage oscillation caused by excessive power extraction, and achieves deep protection of the physical entity by the algorithm logic.

[0075] Its virtual damping coefficient The damping term plays a role in smoothing out power oscillations. This is due to the virtual damping coefficient. The compensation correction has been made based on the increase in battery internal resistance. The resulting damping torque can offset the negative damping component brought by the aging battery in the high frequency band, ensuring that the frequency adjustment process is smooth, without overshoot, and without inducing wideband resonance.

[0076] Based on the above embodiments, as a preferred embodiment, it determines the corresponding potential amplitude synchronization command according to the reactive power reference value in the virtual synchronous generator corresponding to the virtual synchronous machine swing equation, the actual output reactive power, the grid rated voltage, and the public grid point voltage, including: Based on the operating status of the virtual synchronous generator corresponding to the swing equation of the virtual synchronous machine, the reactive power reference value, actual output reactive power, grid rated voltage and public grid point voltage of the virtual synchronous generator are collected. A voltage control strategy incorporating droop characteristics is adopted to construct a synchronous command equation for potential amplitude. Substitute the reactive power reference value, actual output reactive power, grid rated voltage, and public grid point voltage into the potential amplitude synchronization command equation to obtain the potential amplitude synchronization command. The expression for the potential amplitude synchronization command equation is as follows: ; in, This is a command to synchronize the potential amplitude. Rated internal potential amplitude; This is the reactive power droop coefficient; This is a reference value for reactive power. This refers to the actual output reactive power. The voltage-supported gain coefficient; This is the rated voltage of the power grid; This refers to the voltage at the public network point.

[0077] In a specific embodiment, in order to achieve voltage support and reactive power sharing during multi-machine parallel operation, this application adopts a voltage control strategy that incorporates droop characteristics: ; Therefore, this equation simulates the excitation regulation characteristics of a generator. When the grid connection point voltage... Below the rated voltage of the power grid At that time, through voltage-supported gain coefficient The adjustment increases the voltage amplitude synchronization command. This allows the converter to inject inductive reactive power into the power grid, supporting local voltage stability.

[0078] In addition, this application obtains the virtual rotation angular frequency. Synchronization command with potential amplitude (which can also be understood as potential amplitude) Then, the final synthesis of the reference instructions is performed in this application.

[0079] By integrating the angular frequency calculated from the adaptive rotor motion equation over time, the internal electromagnetic angle (power angle) of the virtual synchronous machine can be obtained: ; The phase angle Includes dynamic corrections due to battery aging. This ensures that the phase change of the output voltage always remains within the dynamic safety envelope of the battery energy supply.

[0080] And utilize the potential amplitude and phase angle Synthesizing a three-phase balanced AC reference signal in a stationary coordinate system: ; The three-phase reference voltage vector command This serves as the high-precision tracking target for the inner-loop current control in subsequent steps. Through this closed-loop chain, this application successfully transforms the battery's "physical state" into the grid-connected inverter's "physical wave command," achieving the ultimate goal of source-grid coordination.

[0081] It is easy to understand that in a network-based control system, the reference voltage command generated by the outer-loop VSG algorithm ultimately needs to be accurately tracked by the underlying inner current loop. Considering that the DC bus voltage of an electric vehicle's power battery will exhibit nonlinear fluctuations under low state of equilibrium (SOH) conditions, and that two-stage converters suffer from complex parameter perturbations, this application also designs a non-singular fast terminal sliding mode control (NFTSMC) current inner loop controller. This controller aims to ensure that the system state converges to the reference value within a finite time and, by utilizing the high robustness of sliding mode control, completely eliminates the coupling interference of DC-side fluctuations on the AC output current quality.

[0082] First, in order to achieve zero steady-state error control and simplify controller design, the inverter-side output current of the LCL filter is first changed from the stationary coordinate system ( Transform to a rotating coordinate system According to Kirchhoff's laws, the system in The dynamic mathematical model under the axis is: ; in, and For inverters in Output voltage control quantity in the coordinate system; and For grid voltage at Components in the coordinate system (feedback quantity); and For the filter inductor current in Components in the coordinate system (system state variables); For filtering inductors; The equivalent series resistance of the filter inductor; This refers to the virtual rotational angular frequency. and This is a cross-coupling term introduced due to coordinate transformation.

[0083] Define the current tracking error vector: ; in, and This is a current reference value, generated by the outer loop control (such as VSG control or voltage control loop). and This represents the current tracking error.

[0084] Based on the above model and equations, it can be seen that this provides the foundation for the state equations of the subsequent sliding mode controller design, by using a three-phase stationary coordinate system ( ) Variable transformation to synchronous rotating coordinate system ( This can convert AC signals into DC signals, thereby simplifying controller design and enabling zero steady-state error tracking.

[0085] Secondly, while traditional linear sliding mode is robust, its tracking error only converges to zero when the time approaches infinity. Traditional terminal sliding mode (TSM), although achieving finite-time convergence, suffers from singularity issues near the origin. This application designs the following non-singular fast terminal sliding surface: : in, and A positive gain coefficient; and It is a positive odd number and satisfies .

[0086] When error When the linear term is large, It plays a leading role, ensuring that the system state approaches the equilibrium point at a high rate; when the error When the value is small, the nonlinear term It plays a leading role in ensuring that the system converges accurately within a finite time.

[0087] And it is not difficult to understand that, due to When deriving the control law by differentiating the sliding surface, the power term of the error term... It is always greater than zero. This mathematically avoids the abnormal operating condition where the denominator is zero, solves the calculation deadlock problem near zero in traditional sliding mode, and ensures the control continuity of the converter under any operating condition.

[0088] To further mitigate the chattering phenomenon inherent in sliding mode control and enhance its ability to suppress DC bus voltage disturbances, this application employs an exponential reaching law: ; in, Combining the system state equations, the sliding surface... Find the first time derivative, and let... Equal to the approach law, the inverter is derived. Shaft output voltage control command: ; ; in, for Axis coupling term matrix.

[0089] Therefore, its feedforward compensation term can be determined as follows: It achieves real-time cancellation of grid voltage and impedance voltage drop.

[0090] Robust control item: The sliding mode component within square brackets is responsible for compensating for losses due to battery aging. Identification deviation and real-time value of DC bus voltage Nonlinear disturbances caused by violent fluctuations.

[0091] Ultimately, due to the adoption of a T-type three-level topology, the SVPWM (Space Vector Pulse Width Modulation) module... The amplitude and phase of the voltage are determined, and the three nearest fundamental vectors are found in the three-dimensional voltage vector space. By adjusting the application time of the three vectors, the target voltage is accurately synthesized.

[0092] Technical Principle: When battery aging causes a momentary drop in DC bus voltage, traditional PI controllers often suffer from output current distortion due to their fixed gain. The NFTSMC controller designed in this application can sense this deviation and instantaneously adjust the pulse width through the nonlinear gain of the sliding mode surface, thereby maintaining a constant power output on the AC side. This "high dynamic response" characteristic, combined with the outer loop's SOH-based parameter adaptive logic, ultimately achieves a complete control closed loop encompassing "front-end battery life protection, intermediate energy coordination, and rear-end oscillation self-healing."

[0093] Therefore, the network-based adaptive virtual synchronization control method provided in this application generally includes five steps: Step 1: Constructing a full-dimensional time-domain mathematical model of the power battery-cascaded converter based on electrochemical characteristics: In order to achieve dynamic reshaping of the "source-grid" impedance characteristics at the control level and solve the energy supply deficit problem caused by battery aging, this application first establishes a refined full-dimensional model that can deeply reflect the electrochemical constraints of the physical layer and the dynamic response of high-frequency switching in the electrical layer. This step is divided into three sub-parts: hardware topology analysis, battery dynamic modeling, and cascaded system energy coupling modeling.

[0094] Step 2: Online identification of key battery state parameters and SOH assessment based on the FFRLS algorithm (Forgetting Factor Recursive Least Squares): Since the aging of electric vehicle power batteries is a slow, time-varying, nonlinear process influenced by multiple factors including cycle number, depth of discharge, and ambient temperature, the system must be able to sense the drift of internal battery parameters in real time. This application uses FFRLS to dynamically extract the core parameter characterizing battery aging—ohmic internal resistance—in real time. The value is used as a basis to construct a real-time input source for adaptive control parameters.

[0095] Step 3: This step is the core innovative link in realizing the "power battery life protection" and "dynamic synergy of front and rear stage energy" of this application. Its technical essence lies in utilizing the electrochemical physical characteristic quantities obtained in Step 2 ( , This involves real-time intervention of the rotor motion equation parameters of the grid-type controller, establishing a mapping mechanism of "source-end physical constraints driving logic-end control decisions." By dynamically reshaping the rotor motion characteristics of the virtual synchronous machine, the problem of power overshoot and DC voltage drop of aging batteries under grid fluctuation conditions is fundamentally solved.

[0096] Step 4: This step is the core execution step for realizing the grid-forming characteristic. Its technical essence lies in applying the battery-based approach from Step 3. Dynamic parameters for real-time correction of DC bus voltage status and The control algorithm injected into the Virtual Synchronous Generator (VSG) simulates the physical response characteristics of a traditional synchronous generator, enabling the electric vehicle converter to have "elastic" inertia support capabilities.

[0097] Step 5: In the network-type control system, the reference voltage command generated by the outer-loop VSG algorithm ultimately needs to be accurately tracked through the underlying inner current loop. Considering that the DC bus voltage of the electric vehicle's power battery will experience nonlinear fluctuations under low SOH conditions, and that the two-stage converter has complex parameter perturbations, this step designs a non-singular fast terminal sliding mode control (NFTSMC) current inner loop controller. This controller aims to ensure that the system state converges to the reference value within a finite time and, by utilizing the high robustness of sliding mode control, completely eliminates the coupling interference of DC-side fluctuations on the AC output current quality.

[0098] Figure 3 A structural diagram of a network-type adaptive virtual synchronization control device provided in another embodiment of this application is shown below. Figure 3 As shown, the network-type adaptive virtual synchronization control device includes: a memory 20 for storing computer programs; The processor 21 is used to implement the steps of the network-type adaptive virtual synchronization control method mentioned in the above embodiments when executing a computer program.

[0099] The network-based adaptive virtual synchronization control device provided in this embodiment can include, but is not limited to, smartphones, tablets, laptops, or desktop computers.

[0100] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0101] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 20 is used to store at least the following computer program 201, which, after being loaded and executed by the processor 21, is capable of implementing the relevant steps of the network-based adaptive virtual synchronization control device method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, and the storage method may be temporary storage or permanent storage. The operating system 202 may include Windows, Unix, Linux, etc.

[0102] In some embodiments, the network-based adaptive virtual synchronization control device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0103] Those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the network-type adaptive virtual synchronization control device and may include more or fewer components than shown.

[0104] The network-based adaptive virtual synchronization control device provided in this application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the above-mentioned network-based adaptive virtual synchronization control method and has the same beneficial effects.

[0105] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiments.

[0106] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0107] The foregoing has provided a detailed description of a network-based adaptive virtual synchronization control method and apparatus provided in this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0108] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A network-based adaptive virtual synchronization control method, characterized in that, Applied to a two-stage bidirectional power conversion system consisting of a front-stage interleaved parallel bidirectional DC / DC converter and a rear-stage T-type three-level inverter, the method includes: The battery terminal voltage equation is constructed based on the battery's electrical characteristic parameters, and the battery terminal voltage at the current moment is obtained. Based on the DC bus energy balance principle including the battery terminal voltage, a dynamic equation for the DC bus voltage is constructed, and the real-time value of the DC bus voltage at the current moment is obtained. The parameter vector is corrected using a recursive iterative algorithm, and the corresponding real-time value of the ohmic internal resistance is obtained. The battery health status parameters are determined based on the real-time value of the ohmic internal resistance, and the adaptive virtual inertia is determined by combining the rated virtual inertia and the real-time value of the DC bus voltage. The corresponding virtual damping coefficient is determined based on the basic damping coefficient, impedance reshaping compensation gain, the real-time value of the ohmic internal resistance, and the reference ohmic internal resistance. The virtual synchronous machine oscillation equation is constructed based on the adaptive virtual inertia and the virtual damping coefficient in order to perform dynamic frequency adjustment; The corresponding potential amplitude synchronization command is determined based on the reactive power reference value, actual output reactive power, grid rated voltage, and public grid point voltage in the virtual synchronous generator corresponding to the virtual synchronous machine swing equation.

2. The network-based adaptive virtual synchronization control method according to claim 1, characterized in that, The process of constructing the battery's terminal voltage equation based on the battery's electrical characteristic parameters and obtaining the battery's terminal voltage at the current moment includes: Construct a second-order RC broadband equivalent impedance model of the battery corresponding to the aforementioned electrical characteristic parameters; The terminal voltage equation is constructed based on the second-order RC broadband equivalent impedance model of the battery. The expression for the terminal voltage equation is as follows: ； It is a function of the battery terminal voltage; This is the open-circuit voltage; It is an open-circuit current function; The internal resistance is ohmic; It is an electrochemical polarization voltage function; This is the concentration polarization voltage function.

3. The network-based adaptive virtual synchronization control method according to claim 1, characterized in that, The expression for the dynamic equation of the DC bus voltage is: ； in, For DC bus capacitors; This refers to the real-time value of the DC bus voltage; The converter efficiency corresponding to the preceding interleaved parallel bidirectional DC / DC converter; This refers to the battery terminal voltage. Open circuit current; The electromagnetic power corresponding to the subsequent T-type three-level inverter; This refers to stray power loss.

4. The network-based adaptive virtual synchronization control method according to claim 1, characterized in that, The step of using a recursive iterative algorithm to correct the parameter vector and obtain the corresponding real-time value of the ohmic internal resistance includes: The second-order RC broadband equivalent impedance model of the battery is discretized, and the bilinear transformation method is used to map the second-order RC broadband equivalent impedance model from the s-domain to the z-domain. Construct the system transfer function and reconstruct the system transfer function into a linear regression equation; The parameter vector in the linear regression equation is iteratively corrected using a recursive least squares algorithm that incorporates a forgetting factor. Based on the parameter vector after iterative correction, the corresponding real-time value of the ohmic internal resistance is obtained by using the mapping relationship from the s-domain to the z-domain of the second-order RC broadband equivalent impedance model.

5. The network-based adaptive virtual synchronization control method according to claim 4, characterized in that, The iterative correction of the parameter vector in the linear regression equation using a recursive least squares algorithm incorporating a forgetting factor includes: The corresponding output observation value and data vector are determined based on the current battery charging / discharging current and the battery terminal voltage at the current moment. Based on the data vector, the covariance matrix corresponding to the previous time step, and the forgetting factor correction gain matrix; The parameter vector at the current time is corrected based on the corrected gain matrix, the output observation, and the parameter vector at the previous time step.

6. The network-based adaptive virtual synchronization control method according to claim 1, characterized in that, The step of determining the battery's health status parameters based on the real-time value of the ohmic internal resistance, and determining the adaptive virtual inertia in conjunction with the rated virtual inertia and the real-time value of the DC bus voltage, includes: Based on the real-time value of the ohmic internal resistance, combined with the reference ohmic internal resistance and the internal resistance threshold, the corresponding health state parameters are determined by the battery health state parameter equation constructed through the impedance increase rate. Based on the health status parameters and the rated virtual inertia as a benchmark, an adaptive virtual inertia equation is constructed by introducing a self-regulating attenuation factor based on health status and an energy coordination factor based on DC bus voltage feedback, and the corresponding adaptive virtual inertia is determined. The expression for the battery health state parameter equation is as follows: ； The expression for the adaptive virtual inertia equation is: ； in, The health status parameter; The internal resistance threshold is... This is the real-time value of the ohmic internal resistance; The reference ohmic internal resistance; The adaptive virtual inertia; The nominal virtual inertia; The self-regulating decay factor; The energy coordination factor is mentioned above.

7. The network-based adaptive virtual synchronization control method according to claim 1, characterized in that, The expression corresponding to the virtual damping coefficient is: ； in, The virtual damping coefficient; The basic damping coefficient is mentioned above; This is the impedance reshaping compensation gain; This is the real-time value of the ohmic internal resistance; The reference ohmic internal resistance is given.

8. The network-based adaptive virtual synchronization control method according to claim 1, characterized in that, The step of constructing the virtual synchronizer oscillation equation based on the adaptive virtual inertia and the virtual damping coefficient for dynamic frequency adjustment includes: Based on the adaptive virtual inertia and combined with the virtual damping coefficient, the virtual synchronous machine swing equation is reconstructed, wherein the virtual synchronous machine swing equation satisfies the frequency-active power adaptive adjustment logic. When the grid frequency fluctuates, frequency-active power adaptive adjustment is achieved through the virtual synchronous machine swing equation; The expression for the virtual synchronizer's oscillation equation is as follows: ； in, The adaptive virtual inertia; This is the rated angular frequency; This refers to the virtual rotational angular frequency. This is a reference value for active power. Electromagnetic output power; is the virtual damping coefficient.

9. The network-based adaptive virtual synchronization control method according to any one of claims 1-8, characterized in that, The step of determining the corresponding potential amplitude synchronization command based on the reactive power reference value, actual output reactive power, grid rated voltage, and public grid point voltage in the virtual synchronous generator corresponding to the virtual synchronous machine swing equation includes: Based on the operating state of the virtual synchronous generator corresponding to the swing equation of the virtual synchronous machine, the reactive power reference value, the actual output reactive power, the grid rated voltage, and the public grid point voltage of the virtual synchronous generator are collected. A voltage control strategy incorporating droop characteristics is adopted to construct a synchronous command equation for potential amplitude. Substitute the reactive power reference value, the actual output reactive power, the grid rated voltage, and the public grid point voltage into the potential amplitude synchronization command equation to obtain the potential amplitude synchronization command. The expression for the potential amplitude synchronization command equation is as follows: ； in, This is the voltage amplitude synchronization command; Rated internal potential amplitude; This is the reactive power droop coefficient; This is the reference value for reactive power; This refers to the actual output reactive power; This refers to the voltage-supported gain coefficient. The rated voltage of the power grid; The voltage at the public network point is [value].

10. A network-based adaptive virtual synchronization control device, characterized in that, Includes memory used to store computer programs; A processor, configured to implement the steps of the network-based adaptive virtual synchronization control method as described in any one of claims 1 to 9 when executing the computer program.

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