Multi-mode networking control method, device and equipment of vehicle-to-network system, and medium

Abstract

The application discloses a multi-mode network construction control method, device and equipment of a vehicle-to-grid system and a medium. The method reads vehicle state data, power grid electrical data and charging setting time through the vehicle-to-grid system; determines battery control parameters according to the vehicle state data, and sets a vehicle state of charge range based on the battery control parameters; selects a target network construction operation mode from multiple network construction operation modes according to the vehicle state of charge range, the charging setting time and the vehicle state data; generates a network construction control instruction according to the target network construction operation mode, the battery control parameters and the power grid electrical data, and controls the vehicle-to-grid system and the power supply network to interact with each other according to the network construction control instruction. The application can integrate the battery health state, the temperature state, the state of charge and the user charging demand into the network construction control process, provide power supply network construction support and protect the battery health at the same time.

Description

Technical Field

[0001] This application relates to the field of vehicle-to-grid control technology, and in particular to multi-mode network construction control methods, devices, equipment and media for vehicle-to-grid systems. Background Technology

[0002] Vehicle-to-the-grid (V2G) technology has emerged as a highly promising distributed flexible resource, enabling bidirectional energy flow between electric vehicle batteries and the power grid via chargers. However, most current mainstream V2G control strategies are based on grid-following technology, which is essentially a controlled current source, highly dependent on phase-locked loop (PLL) synchronization, exhibits poor stability in weak grid environments, and cannot provide independent voltage support in black-start scenarios such as power outages. In contrast, grid-based control can simulate the behavior of synchronous generators, controlling the charger as a controlled voltage source, thereby providing the necessary inertial response and frequency regulation capabilities.

[0003] Currently, most mainstream vehicle chargers adopt grid-based retrofitting schemes based on grid-following control. A few, while possessing basic grid-building capabilities, lack integrated battery health adaptive mechanisms. The core idea of ​​these schemes is to introduce a virtual synchronous generator algorithm into the bidirectional charger, enabling the charger to simulate the swing equations of a synchronous generator, thereby providing inertial response and primary frequency regulation capability, and achieving basic grid-based control.

[0004] Despite the significant advantages of grid-based technologies, existing virtual synchronous generator (VSR) grid-based strategies often overlook the physical constraints of electric vehicle (EV) batteries, including state-of-charge (SOC) limitations and actual user charging needs. Frequent participation in grid transient frequency support can exacerbate battery cycling. If the control strategy fails to detect the battery's health status and real-time temperature, forcibly outputting power under high internal resistance or abnormal temperature conditions will lead to a sharp decline in battery life and may even trigger safety protection mechanisms. Summary of the Invention

[0005] This application provides a multi-mode grid construction control method, device, equipment, and medium for vehicle-to-grid systems. During the process of vehicle-to-grid systems participating in power grid construction support and bidirectional energy interaction, the battery health status of the vehicle energy storage unit is incorporated into the grid construction control decision, thereby protecting battery health while maintaining grid support capabilities.

[0006] To achieve the above objectives, a first aspect of this application provides a multi-mode network construction control method for a vehicle-to-the-network system, comprising: The vehicle-to-grid system reads vehicle status data, grid electrical data, and charging set time. The vehicle-to-grid system is used to connect the vehicle energy storage unit and the power supply network to realize bidirectional energy interaction between the vehicle energy storage unit and the power supply network. Based on the vehicle status data, determine the battery control parameters, and set the vehicle charge range based on the battery control parameters; Based on the vehicle's charge range, the charging set time, and the vehicle status data, a target network operation mode is selected from a set of preset network operation modes; Based on the target grid connection operation mode, the battery control parameters, and the grid electrical data, a grid connection control command for controlling the vehicle-to-grid system is generated. The vehicle-to-grid system is controlled to perform bidirectional energy interaction with the power supply network according to the network construction control command.

[0007] This application determines battery control parameters, vehicle charge range, and target grid operation mode by reading vehicle status data, grid electrical data, and charging set time, and generates corresponding grid control commands. Compared with the existing technology that mainly controls vehicle energy storage units based on grid power demand or fixed charging and discharging strategies, this application can incorporate the vehicle's own status, power supply network status, and user charging needs into the grid control process, thereby protecting battery health while maintaining grid support capabilities.

[0008] Further, based on the vehicle status data, battery control parameters are determined, and the vehicle charge range is set based on the battery control parameters, including: Battery control parameters are obtained from the vehicle status data, including health status and temperature data. Based on the temperature data, the temperature sensitivity weighting factor is calculated. Based on the stated health status and the temperature-sensitive weighting factor, the preset charge range is corrected to obtain the vehicle charge range. This application corrects the preset charge range, preset damping coefficient, and charging power based on the vehicle's health status and the temperature-sensitive weighting factor, and determines the adaptive charging time. Compared to existing technologies that use fixed charge limits, fixed damping parameters, or fixed charging power, this approach enables the vehicle's energy storage unit's charge / discharge boundaries, power response intensity, and charging time to adaptively adjust according to the battery's health and temperature conditions. This reduces the risk of battery damage caused by overcharging, over-discharging, and high-intensity charging / discharging, thus protecting battery health.

[0009] Furthermore, based on the vehicle's charge range, the charging set time, and the vehicle status data, a target network operation mode is selected from a set of preset network operation modes, including: The current state of charge is obtained from the vehicle status data; The actual charging power is determined based on the health status and the temperature-sensitive weighting factor. The adaptive charging time is determined based on the vehicle status data and the actual charging power. The current state of charge is compared with the upper limit of the vehicle's charge range, and an overcharge status indicator is determined based on the comparison result. When the current state of charge is greater than or equal to the upper limit of the charge range, the overcharge status indicator is determined to be a triggered state; otherwise, the overcharge status indicator is determined to be a non-triggered state. The current state of charge is compared with the lower limit of the vehicle's charge range, and an over-discharge status identifier is determined based on the comparison result. When the current state of charge is less than or equal to the lower limit of charge, the over-discharge status identifier is determined to be a triggered state; otherwise, the over-discharge status identifier is determined to be a non-triggered state. The remaining charging time is determined according to the charging set time, and the remaining charging time is compared with the adaptive charging time. A forced charging status indicator is determined according to the comparison result. When the remaining charging time is less than or equal to the adaptive charging time, the forced charging status indicator is determined to be in a triggered state; otherwise, the forced charging status indicator is determined to be in a non-triggered state. Based on the combination of the overcharge status indicator, the over-discharge status indicator, and the forced charging status indicator, the target grid operation mode is selected from a set of preset grid operation modes. This application generates overcharge and over-discharge risk signals by comparing the current state of charge with the upper and lower limits of charge, and generates a forced charging signal by comparing the remaining charging time with the adaptive charging time. Compared to existing technologies that only determine charging and discharging based on the current state of charge or grid demand, this approach simultaneously identifies battery safety risks and user charging time risks, thus providing a basis for accurate switching of grid operation modes.

[0010] Further, based on the overcharge risk signal, the over-discharge risk signal, and the forced charging signal, the target network operation mode is selected from a set of preset network operation modes, including: When the overcharge status indicator, the over-discharge status indicator, and the forced charging status indicator are all in an untriggered state, a preset conventional network construction mode is selected as the target network construction operation mode. When the overcharge state is marked as triggered and the forced charging state is marked as not triggered, a preset charging-limited mode is selected as the target network operation mode. When the overcharge state is marked as non-triggered, the over-discharge state is marked as triggered, and the forced charging state is marked as non-triggered, a preset discharge-limited mode is selected as the target network operation mode. When the overcharge status is marked as non-triggered and the forced charging status is marked as triggered, a preset forced charging mode is selected as the target grid connection operation mode. Further, based on the target grid connection operation mode, the battery control parameters, and the grid electrical data, a grid connection control command for controlling the vehicle-to-grid system is generated, including: Based on the target network operation mode, determine the active power command, the lower limit of integration, and the upper limit of integration; The angular frequency command is obtained based on the lower integration limit, the upper integration limit, and the power grid electrical data; The preset damping coefficient is corrected based on the battery control parameters and the temperature-sensitive weighting factor to obtain the dynamic damping coefficient; The output voltage angular frequency is generated based on the active power command, the angular frequency command, the dynamic damping coefficient, and the power grid electrical data. Based on the output voltage angular frequency and the power grid electrical data, a current reference command is generated; The network control command is generated based on the current reference command.

[0011] This application determines the active power command, integral lower limit, and integral upper limit based on the target grid operation mode, and generates grid control commands by combining dynamic damping coefficients and grid electrical data. Compared with the existing technology where grid control parameters are fixed and not linked to battery health status and operation mode, this application can embed battery state constraints and mode selection results into the grid control parameter generation process, thereby improving the frequency response controllability and grid support stability of the vehicle-to-grid system under different battery states.

[0012] Furthermore, based on the target network operation mode, the active power command, the lower limit of integration, and the upper limit of integration are determined, including: When the target network operation mode is the conventional network mode, the active power command, the lower limit of integration, and the upper limit of integration are all zero; When the target network operation mode is the charging-restricted mode, the integral lower limit is a negative open limit, and the active power command and the integral upper limit are zero; When the target network operation mode is the discharge-limited mode, the active power command and the integral lower limit are zero, and the integral upper limit is the positive open limit. When the target network operation mode is the forced charging mode, the lower limit of integration is a negative open limit, the upper limit of integration is a positive open limit, and the actual executed charging power is used as the active power command.

[0013] This application sets active power commands, integral lower limits, and integral upper limits for conventional grid configuration mode, charging-limited mode, discharging-limited mode, and forced charging mode, respectively. Compared with the existing technology where the integral adjustment range is fixed or does not distinguish between charging and discharging safety boundaries, it can limit the power adjustment capability of the vehicle energy storage unit in adverse directions through differentiated integral limiting, thereby avoiding continued charging under the risk of overcharging and continued discharging under the risk of over-discharging, and prioritizing the completion of vehicle charging in forced charging mode.

[0014] Further, based on the output voltage angular frequency and the power grid electrical data, a current reference command is generated, including: The output voltage phase angle is generated based on the output voltage angular frequency; Generate voltage amplitude instructions based on the power grid electrical data; The current reference command is generated based on the voltage amplitude command, the output voltage phase angle, and the power grid electrical data.

[0015] This application generates the output voltage phase angle based on the output voltage angular frequency, generates the voltage amplitude command based on the power grid electrical data, and generates the current reference command accordingly. Compared with the existing technology where the current reference generation does not fully correlate the grid voltage phase, frequency and amplitude, this application enables the current reference of the vehicle-to-grid system to match the power supply network operation status and grid control objectives, thereby improving the accuracy of current command generation and the stability of the bidirectional energy interaction process.

[0016] Further, generating the network control command based on the current reference command includes: Obtain the measured current of the vehicle-to-grid system; Based on the current reference command and the measured current, a modulation control quantity is generated; A PWM drive instruction is generated based on the modulation control quantity, and the PWM drive instruction is used as the network configuration control instruction.

[0017] This application generates modulation control quantities based on current reference commands and measured current of the vehicle-to-grid system, and further generates PWM drive commands. Compared with the existing technology, which lacks a control method based on measured current feedback for closed-loop correction, this can improve the tracking capability of the actual output current to the current reference command, thereby reducing current tracking errors and power fluctuations, and improving the accuracy and reliability of the vehicle-to-grid system's network construction control command execution.

[0018] Secondly, this application provides a multi-mode network construction control device for a vehicle-to-the-network system, including: a data reading module, a parameter determination module, a mode selection module, an instruction generation module, and an energy interaction control module; The data reading module is used to read vehicle status data, power grid electrical data, and charging set time through the vehicle-to-grid system; The parameter determination module is used to determine battery control parameters based on the vehicle status data, and to set the vehicle charge range based on the battery control parameters. The mode selection module is used to select a target network operation mode from a plurality of preset network operation modes based on the vehicle charge range, the charging set time, and the vehicle status data. The instruction generation module is used to generate a grid connection control instruction for controlling the vehicle-to-grid system based on the target grid connection operation mode, the battery control parameters, and the grid electrical data. The energy interaction control module is used to control the vehicle-to-grid system and the power supply network to perform bidirectional energy interaction according to the grid construction control command.

[0019] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the multi-mode network construction control method for the vehicle-to-the-network system described in the above-mentioned application embodiments.

[0020] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it performs the multi-mode network construction control method for the vehicle-to-network system described in the above-mentioned application embodiments. Attached Figure Description

[0021] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. The drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a multi-mode network control method for a vehicle-to-the-network system provided in an embodiment of this application; Figure 2 This is a schematic diagram of the grid operation mode division based on state of charge and charging time provided in an embodiment of this application; Figure 3 This is a topology diagram of a vehicle-to-grid system network control strategy with integrated state of charge management provided in an embodiment of this application; Figure 4 This is a schematic diagram of the overall control flow of a multi-mode network construction control method for a vehicle-to-network system provided in an embodiment of this application; Figure 5This is a schematic diagram of the structure of a multi-mode network control device for a vehicle-to-network system provided in an embodiment of this application. Detailed Implementation

[0023] 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 skilled in the art without creative effort are within the scope of protection of this application.

[0024] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0025] To address the issue of battery life degradation caused by the failure of existing vehicle-to-grid control strategies to incorporate battery health status and real-time temperature for adaptive control, such as... Figure 1 As shown in the figure, this application provides a schematic flowchart of a multi-mode network construction control method for a vehicle-to-the-network system. The multi-mode network construction control method for a vehicle-to-the-network system in this embodiment includes steps S101 to S105, which are described in detail below: Step S101: Read vehicle status data, grid electrical data and charging set time through the vehicle-to-grid system. The vehicle-to-grid system is used to connect the vehicle energy storage unit and the power supply network to realize bidirectional energy interaction between the vehicle energy storage unit and the power supply network.

[0026] Preferably, in this embodiment, the vehicle-to-grid system is constructed based on a hardware topology of a three-phase bidirectional electric vehicle charger. This topology consists of a DC / DC converter stage and a DC / AC inverter stage, which are connected via an intermediate DC bus. The inverter side is connected to the AC grid through an LCL filter. After system startup, the controller establishes a data link with the vehicle battery management system to obtain the real-time state of charge (SoC), state of health (SOH), and real-time internal temperature (T) of the power battery. Simultaneously, it collects voltage and current signals from the grid to calculate the current active power (P) and reactive power (Q).

[0027] Specifically, in this preferred embodiment, the hardware core is built upon a three-phase bidirectional electric vehicle charger topology. This hardware topology consists of two main power conversion stages: a bidirectional DC / DC converter stage closer to the power battery and a bidirectional DC / AC inverter stage closer to the power grid. The two converter stages are electrically connected through an intermediate DC bus, the bus voltage of which is defined as... When the system enters the network-based operation mode, the power distribution logic is specifically adjusted to ensure the overall stability of the system: the DC / DC converter stage is controlled to regulate the intermediate DC bus voltage. The active power P and reactive power Q exchanged between the DC / AC inverter stage and the AC grid are constant, while the DC / AC inverter stage undertakes the core grid construction task, responsible for managing the active power P and reactive power Q exchanged between the DC / AC grid and the AC grid.

[0028] Preferably, a high-quality LCL power filter is configured at the output of the DC / AC inverter to filter out high-frequency switching harmonics and meet stringent grid harmonic distortion rate requirements. This filter has a sophisticated structure and includes an inductor on the converter side. Grid-side inductance and filter capacitors To effectively suppress the filter's own resonance peak, a damping resistor was connected in series with the filter capacitor. In modeling, the AC power grid is equivalent to a system consisting of ideal voltage sources. With equivalent series impedance The Thevenin equivalent circuit, in which Contains emotional components With resistive components Under the grid-based strategy, the DC / AC level no longer passively tracks the grid phase, but instead acts as an independent controlled voltage source supporting voltage and frequency. Its internal control logic is implemented using a synchronously rotating dq coordinate system, ensuring the DC characteristics of the control variables in steady state.

[0029] Preferably, the user's gun-drawing time is obtained through the vehicle-to-network system. The time when the user removes the charging gun is used as the charging time setting.

[0030] Step S102: Determine battery control parameters based on the vehicle status data, and set the vehicle charge range based on the battery control parameters.

[0031] Furthermore, battery control parameters are obtained from the vehicle status data, including health status and temperature data; Based on the temperature data, the temperature sensitivity weighting factor is calculated. The preset charge range is corrected based on the health status and the temperature-sensitive weighting factor to obtain the vehicle charge range.

[0032] Preferably, the controller calculates a temperature-sensitive weighting factor based on the collected temperature T. The calculation formula is as follows: in and This represents the boundary value of the preset optimal operating temperature range for the battery. Subsequently, SOH and... The initial safe operating boundary is dynamically contracted to obtain the corrected upper limit of the state of charge. and lower limit .

[0033] Understandably, considering the Arrhenius equation characteristic of the internal chemical reaction rate of lithium batteries, when the battery deviates from its optimal operating range, such as from 15°C to 35°C, its internal resistance increases significantly and its power handling capability decreases non-linearly. To quantify this effect, this application defines a temperature-sensitive weighting factor. It remains at 1 within the optimal temperature range, but when the temperature T is below the lower limit... or above the upper limit At that time, an exponential decay function is used for smooth correction. The introduction of this weight ensures that the system can perceive the physical limits of the battery in real time, providing a basis for subsequent boundary scaling.

[0034] In previous network-based control systems, the upper limit of operation was... and lower limit These are typically static values ​​based on a new battery design. However, as the number of battery cycles increases... As energy decreases, the actual usable energy window of the battery shrinks. This application calculates the corrected boundary value by introducing a correction factor to scale the boundary in real time. and As battery health declines or ambient temperature shifts towards extremes, the correction factor decreases, causing the previously wide operating range to shrink symmetrically towards the central equilibrium point. This can be described by the formula: Understandably, the evolutionary logic is as follows: as the battery ages or the ambient temperature becomes extreme, the originally wide operating range will shrink towards the intermediate equilibrium point. This improvement allows batteries in a sub-healthy state to enter the "charge-limited mode" or "discharge-limited mode" earlier, thereby avoiding further strain on the remaining battery life due to deep charge-discharge cycles.

[0035] Preferably, the rated charging power required by the user is set as follows: The system is based on the current and temperature factor Calculate the actual charging power To reduce heat loss under high internal resistance conditions: Preferably, since the charging power becomes a dynamic variable, the system synchronously updates the adaptive charging time Δ. t ch, adapt To ensure that the user draws their gun at the time of the attack. The target power level can be reached before This time calculation takes into full account the battery capacity. and conversion efficiency : This time parameter will serve as the criterion for determining the forced charging signal, thus achieving deep coupling between the charging plan and the battery health status.

[0036] Simultaneously, the dynamic damping coefficient in the power synchronization loop is adaptively adjusted based on the battery health status. : Wherein is the damping enhancement coefficient, which is increased to smooth the power output of a high internal resistance battery under frequency fluctuations. This is the damping reference value, with a reference value of 0.08 pu.

[0037] Implementing this preferred scheme enables the vehicle energy storage unit to adaptively adjust its charge / discharge boundaries, power response intensity, and charging time judgment according to the battery health and temperature conditions, thereby reducing the risk of battery damage caused by overcharging, over-discharging, and high-intensity charging and discharging, and protecting battery health.

[0038] Step S103: Select the target network operation mode from a set of preset network operation modes based on the vehicle charge range, the charging setting time, and the vehicle status data.

[0039] Further, the step of selecting a target network operation mode from a preset set of multiple network operation modes based on the vehicle's charge range, the charging set time, and the vehicle status data includes: The current state of charge is obtained from the vehicle status data; The actual charging power is determined based on the health status and the temperature-sensitive weighting factor. The adaptive charging time is determined based on the vehicle status data and the actual charging power. The current state of charge is compared with the upper limit of the vehicle's charge range, and an overcharge status indicator is determined based on the comparison result. When the current state of charge is greater than or equal to the upper limit of the charge range, the overcharge status indicator is determined to be a triggered state; otherwise, the overcharge status indicator is determined to be a non-triggered state. The current state of charge is compared with the lower limit of the vehicle's charge range, and an over-discharge status identifier is determined based on the comparison result. When the current state of charge is less than or equal to the lower limit of charge, the over-discharge status identifier is determined to be a triggered state; otherwise, the over-discharge status identifier is determined to be a non-triggered state. The remaining charging time is determined according to the charging set time, and the remaining charging time is compared with the adaptive charging time. A forced charging status indicator is determined according to the comparison result. When the remaining charging time is less than or equal to the adaptive charging time, the forced charging status indicator is determined to be in a triggered state; otherwise, the forced charging status indicator is determined to be in a non-triggered state. Based on the combination of the overcharge status indicator, the over-discharge status indicator, and the forced charging status indicator, the target network operation mode is selected from a plurality of preset network operation modes.

[0040] Preferably, in this embodiment, Boolean variables are generated using logic tables. , and The three Boolean variables correspond to the overcharge risk signal, the over-discharge risk signal, and the forced charging signal, respectively.

[0041] Specifically, the overcharge trigger bit To determine whether an overcharge risk zone has been entered, if... ,but ,otherwise Over-discharge trigger bit Determine whether to enter the over-exposure risk zone. ,but ,otherwise Forced charging trigger bit Determine if the remaining time meets the forced charging requirement. ,but ,otherwise .

[0042] Implementing this preferred scheme can simultaneously identify battery safety risks and user charging time risks, thereby providing a basis for accurate switching of network operation modes.

[0043] Furthermore, when the overcharge status indicator, the over-discharge status indicator, and the forced charging status indicator are all in an untriggered state, a preset conventional network construction mode is selected as the target network construction operation mode. When the overcharge state is marked as triggered and the forced charging state is marked as not triggered, a preset charging-limited mode is selected as the target network operation mode. When the overcharge state is marked as non-triggered, the over-discharge state is marked as triggered, and the forced charging state is marked as non-triggered, a preset discharge-limited mode is selected as the target network operation mode. When the overcharge status is marked as not triggered and the forced charging status is marked as triggered, a preset forced charging mode is selected as the target network operation mode.

[0044] Preferably, in this embodiment, based on the combination of Boolean values, the system automatically switches to the conventional network configuration mode B-GFM, the discharge-limited mode DL-GFM, the charge-limited mode CL-GFM, or the forced charging mode C-GFM, and determines the dynamic saturation upper and lower limits of the SoC control integrator accordingly. and Specifically: : Corresponding to B-GFM, integration limit , Power command ; : Corresponding to CL-GFM, integration limit Power command ; : Corresponding to DL-GFM, integration limit Power command ; or : Corresponding to C-GFM, integration limit Power command .

[0045] Specifically, based on the combination of the above Boolean conditions, this application defines in detail four complementary working modes, see [link to relevant documentation]. Figure 2 , Figure 2 A schematic diagram illustrating the division of grid operation modes based on state of charge and charging time, as shown below. Figure 2 As shown, when the vehicle's energy storage unit's state of charge (SOC) is between the corrected upper and lower SOC limits, and there is sufficient remaining charging time, the vehicle-to-grid system operates in the conventional grid connection mode (B-GFM). When the SOC approaches or exceeds the corrected upper SOC limit, the system enters the charge-limited mode (CL-GFM) to reduce the risk of overcharging caused by continued charging. When the SOC approaches or falls below the corrected lower SOC limit, the system enters the discharge-limited mode (DL-GFM) to reduce the risk of over-discharging caused by continued discharging. The system operates in the adaptive charging time when the current time is less than or equal to the user-set charging completion time. When this happens, the system enters the forced charging mode C-GFM to prioritize ensuring that the vehicle's energy storage unit reaches the target state of charge before the user-set time.

[0046] Parameters corrected by the adaptive layer ( Boundary and It is seamlessly injected into the underlying SoC controller logic, and the execution logic is shown in the table below: This logic table not only considers users' travel needs and state of charge, but also... Dynamic thresholds based on three decision conditions precisely control the activation and deactivation of the integral stage. This deep decoupling of hardware and software ensures that the V2G charger, while providing inertia simulation and voltage support, maintains its transient response speed within the physically tolerable range of the battery. This adaptive mechanism transforms the charger from a simple terminal executing grid commands into an intelligent node with self-health diagnosis and protection capabilities.

[0047] Implementing this preferred scheme can limit the power regulation capability of the vehicle's energy storage unit in adverse directions through differentiated integral limiting, thereby avoiding continued charging under the risk of overcharging and continued discharging under the risk of over-discharging, and prioritizing the completion of vehicle charging in forced charging mode.

[0048] It is understandable that the conventional network configuration mode B-GFM: when the following conditions are met... This indicates that the battery is within its ideal healthy operating window and has ample charging time. In this mode, the integral circuit of the SoC controller is completely disabled, i.e., the saturation limit L is reached. H , L L All values ​​are set to 0, and the charger operates as a standard grid-type voltage source. At this point, the system provides complete inertia simulation and data based on the static damping coefficient D. p With its primary frequency regulation capability, the charger can spontaneously respond to grid frequency fluctuations and maintain frequency stability by absorbing or releasing active power.

[0049] Discharge-Limited Mode (DL-GFM): Activated when the battery enters a low-charge range (S2=1). In this mode, to prevent the battery from deep discharge due to excessive grid connection, the SoC controller limits the integrator's output direction through a dynamic saturation mechanism. Specifically, the system only activates the integration stage when the grid is under-frequency, offsetting D through integration. p The contribution of this term enables the steady-state active power support to return to the command value P. * However, during the initial transient of a frequency disturbance (within approximately 0.8 seconds), the system can still provide necessary transient inertia support due to the physical characteristics of the virtual inertia H, thus achieving a balance between protecting the battery and supporting the power grid. In overfrequency (charging) situations, this mode retains full primary frequency regulation capability.

[0050] CL-GFM (Charge-Limited Mode): Logically symmetrical with DL-GFM, this mode aims to prevent overcharging when the battery is near full charge. In this mode, to address grid frequency over-frequency fluctuations—where the charger needs to increase charging power to absorb excess energy—the SoC controller's integral component is activated. This integral action removes the extra charging power contribution from primary frequency regulation. This nonlinear control characteristic allows CL-GFM to exhibit "transient support and steady-state retreat" when grid frequency rises, while still providing reliable backup power when frequency drops.

[0051] Forced charging mode C-GFM: This mode has the highest priority and will charge when the following conditions are met. Forced triggering ensures the vehicle reaches the expected charge level when the user disconnects the charging gun. In C-GFM mode, the SoC controller's integral action remains active for all frequency fluctuations, meaning the system's primary frequency regulation capability is logically locked, and the steady-state active power strictly adheres to the charging power setpoint P. * Operation. At this point, the system can only provide an extremely short frequency response through the inertia characteristics of the power synchronization loop. It is worth noting that in this mode, the adaptive damping coefficient described in this application... It can significantly alleviate the internal stress impact of frequent small-amplitude frequency fluctuations on the battery under high-power charging conditions.

[0052] Implementing this preferred scheme allows for differentiated control modes to be adopted for different battery states and charging time constraints, thereby maintaining the grid support capability of the power supply network while reducing the risk of battery overcharging and over-discharging and ensuring users' charging needs.

[0053] Step S104: Generate a grid connection control command for controlling the vehicle-to-grid system based on the target grid connection operation mode, the battery control parameters, and the grid electrical data.

[0054] Further, based on the target grid connection operation mode, the battery control parameters, and the grid electrical data, a grid connection control command for controlling the vehicle-to-grid system is generated, including: Based on the target network operation mode, the active power command, the lower limit of integration, and the upper limit of integration are determined; based on the lower limit of integration, the upper limit of integration, and the power grid electrical data, the angular frequency command is obtained. The preset damping coefficient is corrected based on the battery control parameters and the temperature-sensitive weighting factor to obtain the dynamic damping coefficient; The output voltage angular frequency is generated based on the active power command, the angular frequency command, the dynamic damping coefficient, and the power grid electrical data. Based on the output voltage angular frequency and the power grid electrical data, a current reference command is generated; The network control command is generated based on the current reference command.

[0055] Furthermore, based on the target network operation mode, the active power command, the lower limit of integration, and the upper limit of integration are determined, including: Both the lower limit of integration and the upper limit of integration are zero; When the target network operation mode is the charging-restricted mode, the integral lower limit is a negative open limit, and the active power command and the integral upper limit are zero; When the target network operation mode is the discharge-limited mode, the active power command and the integral lower limit are zero, and the integral upper limit is the positive open limit. When the target network operation mode is the forced charging mode, the lower limit of integration is a negative open limit, the upper limit of integration is a positive open limit, and the actual executed charging power is used as the active power command.

[0056] Further, based on the output voltage angular frequency and the power grid electrical data, a current reference command is generated, including: The output voltage phase angle is generated based on the output voltage angular frequency; Generate voltage amplitude instructions based on the power grid electrical data; The current reference command is generated based on the voltage amplitude command, the output voltage phase angle, and the power grid electrical data.

[0057] Further, generating the network control command based on the current reference command includes: Obtain the measured current of the vehicle-to-grid system; Based on the current reference command and the measured current, a modulation control quantity is generated; A PWM drive instruction is generated based on the modulation control quantity, and the PWM drive instruction is used as the network configuration control instruction.

[0058] Step S105: Control the vehicle-to-grid system and the power supply network to perform bidirectional energy interaction according to the network construction control command.

[0059] Preferably, in this embodiment, the system's control algorithm consists of four control blocks: a power synchronization loop (PSL), a reactive power control module (RPC), a virtual admittance, and a current controller. The PSL and RPC control loops integrate the SoC management control module proposed in this application. See also... Figure 3 , Figure 3 For the topology diagram of the grid-type control strategy for vehicle-to-grid systems integrating state of charge management, such as... Figure 3As shown, this topology includes a vehicle energy storage unit, a bidirectional DC / DC converter, a DC bus, a bidirectional DC / AC inverter, an LCL filter, a power supply network, and a controller. The vehicle energy storage unit is connected to the DC bus via the bidirectional DC / DC converter, and the bidirectional DC / AC inverter is connected to the power supply network via the filter. The controller obtains active and reactive power through a power calculation module and inputs them into the power synchronization loop and the reactive power control loop.

[0060] The power synchronization loop generates the output voltage angular frequency based on the active power command, measured active power, frequency deviation, and dynamic damping coefficient. The state-of-charge (SOC) control module limits the integral component in the power synchronization loop based on the SOC, health status, temperature data, and charging time constraints of the vehicle's energy storage unit. The reactive power control loop generates the voltage amplitude command based on the reactive power and voltage reference. The virtual admittance loop generates the current reference command based on the voltage amplitude command and grid electrical data. The underlying current controller generates a PWM drive signal based on the current reference command and measured current, thereby controlling the inverter output voltage and current to support the power supply network.

[0061] Preferably, the power synchronization loop determines the angular frequency ω of the inverter output voltage. r With phase angle θ r Unlike traditional grid-connected inverters that rely on phase-locked loops (PLLs) to obtain grid phase, this system's PSL is based on the Virtual Synchronous Generator (VSG) algorithm, achieving autonomous synchronization by simulating the swing equation of a synchronous generator. The derivation logic of this equation is as follows: In the above equation, H represents the virtual inertia constant, used to provide dynamic frequency support; D p P is the static damping coefficient, the reciprocal of the droop coefficient, which determines the frequency modulation characteristics under steady state; * P represents the active power command, while P represents the measured active power. Specifically, This is the angular frequency command value after correction by the subsequent SoC and SOH control modules. To further optimize the transient characteristics of the active power loop and eliminate potential power oscillations, a dynamic damping term P based on the power derivative is introduced into the equation. d : The dynamic damping term passes through a time constant τ d The low-pass filter is typically implemented with a cutoff frequency set around 20Hz to provide damping in the 1-3Hz range while avoiding synchronous oscillation frequencies near 50Hz, thus ensuring system stability. This is a dynamic damping term generated based on the rate of change of active power. This is the dynamic damping coefficient. is the time constant of the low-pass filter, and s is the Laplace operator.

[0062] Preferably, a reactive power control module runs parallel to the active power control module. Its goal is to simulate the voltage droop characteristics of a synchronous generator and generate the inverter's voltage amplitude command E. The control law of the RPC is described as follows: Among them, v * With Q * These are the reference values ​​for voltage and reactive power, respectively, m q This represents the reactive power droop gain. A first-order low-pass filter is introduced, with a time constant of τ. q The system can smooth out changes in reactive power and adjust the response speed of the voltage loop.

[0063] Preferably, the underlying current controller is responsible for accurately executing the current command generated by the virtual admittance module. This controller employs a PI regulator and integrates cross-coupling term compensation in the dq coordinate system and capacitor voltage feedforward to eliminate phase shifts caused by the rotating coordinate system and improve dynamic tracking accuracy. Its control law is expressed as: in, This is the voltage control quantity output by the current controller. For current reference command, Let be the measured current vector in the dq coordinate system. and Here, represents the proportional gain and integral gain of the PI controller, respectively; s is the Laplace operator; and j is the imaginary unit. The output voltage angular frequency, For the converter-side inductance, This represents the measured capacitor voltage vector in the dq coordinate system. To ensure dynamic coordination of each control loop, the bandwidth of the current loop is typically set to 1 / 20 of the switching frequency. In this embodiment, 500Hz can be used, which is much higher than the bandwidth of PSL and RPC. This allows the dynamic characteristics to be simplified to a quasi-steady-state process during analysis.

[0064] Implementing this preferred scheme enables the current reference of the vehicle-to-grid system to match the operating status of the power supply network and the network construction control objectives, thereby improving the accuracy of current command generation and the stability of the bidirectional energy interaction process.

[0065] Preferably, the control algorithm described in the above preferred embodiment is supplemented with a State of Health (SOH) and temperature adaptive module. This adaptive module acquires the battery's State of Health (SOH) and real-time temperature through the Battery Management System (BMS) and uses them as pre-constraints. The data output of this module is directly fed back to the D in the PSL loop. dThis is reflected in the decision logic of the computing unit and the SoC controller. This means that when the battery temperature is detected to be too high or the State of Harm (SOH) drops significantly, the system will automatically increase D. d The gain is increased and the operating window of the SoC is tightened. The damping ratio and operating mode of the grid-type controller are changed from the underlying physical characteristics. Through adaptive switching of multi-mode operating states, the technology has been transformed from "grid demand-oriented" to "dual optimization of grid demand and battery life".

[0066] Preferably, the battery state sensing layer first constructs a temperature-sensitive weighting function. The formula is described as follows: Preferably, in obtaining Subsequently, the adaptive module dynamically shrinks the safe operating boundaries in the power synchronization loop and the SoC controller, as described by the formula: Preferably, based on the current SOH and temperature factor K T Calculate the actual charging power performed P final * To reduce heat loss under high internal resistance conditions: Preferably, the system synchronously updates the adaptive charging time. : Preferably, the control core of this module is an integrator based on frequency deviation error, whose output directly acts on the angular frequency reference of the power synchronization loop. Its mathematical execution process is as follows: The SoC controller processes three key Boolean inputs (S1, S2, S3) through a logic judgment matrix. These inputs determine the operating state of the integrator and the dynamic saturation limit (L). H , L L In this application, the logic threshold of the Boolean decision condition is no longer a fixed value, but rather a SoC generated by the aforementioned adaptive module. OPmax,new and SoC OPmin, new It is dynamically determined. Specifically, the decision variable S1 is used to identify whether the battery is in the overcharge risk zone (SoC≥SoC). OPmax, new ); Variable S2 is used to identify whether the system is in the over-discharge risk zone (SoC≤SoC). OPmin, new ); while variable S3 combines the current time t and the time t it takes to draw the gun. out Determine if the remaining time has reached the necessary forced charging threshold Δt. ch .

[0067] Preferably, the adaptive module adjusts the dynamic damping coefficient in the active power loop. Collaborative optimization is performed. Based on small-signal analysis, the dynamic damping coefficient... This is crucial for ensuring a smooth response of the grid-connected inverter under frequency fluctuations. For batteries with low state of equilibrium (SOH) or abnormal operating temperatures, their equivalent internal resistance is high, and a drastic power step response can lead to a significant drop in battery terminal voltage, potentially even triggering the undervoltage protection logic of the battery management system (BMS). To mitigate this impact, this application proposes a damping enhancement algorithm based on battery characteristics: Where λ is the preset damping enhancement coefficient, and the reference value is... The reference value is 0.08 pu. The physical meaning of this logic is that when the physical characteristics of the battery deteriorate, the system automatically increases the dynamic damping. According to the pole configuration principle, this will significantly improve the damping ratio ξ of the motor poles, thereby slowing down the rise rate of active power and making the charger's response to grid frequency disturbances more robust.

[0068] Implementing this preferred scheme can embed battery state constraints and mode selection results into the network control parameter generation process, thereby improving the frequency response controllability and network support stability of the vehicle-to-grid system under different battery states.

[0069] For a clearer illustration of this embodiment, see [link to example]. Figure 4 , Figure 4 The overall flow of the multi-mode grid-connected control method for the vehicle-to-grid system provided in this application embodiment is as follows: First, the method collects real-time data on the state of charge, state of health, and real-time temperature of the power battery, as well as voltage and current feedback signals from the grid. Then, it calculates a temperature-sensitive weighting factor based on the battery's state of health and temperature data, and adaptively corrects the safe operating boundary and dynamic damping coefficient of the vehicle's energy storage unit accordingly. Subsequently, based on the corrected charge boundary and user charging time, it generates a state combination for mode determination, identifies the current grid-connected operating mode, and allocates an integral saturation limit and power reference. Next, it performs grid-connected power loop logic operations to generate frequency commands and calculates the voltage amplitude command after reactive power compensation. Finally, through virtual admittance decoupling and underlying PWM modulation control, it drives the power conversion hardware of the vehicle-to-grid system, achieving bidirectional energy interaction that balances battery protection and grid support.

[0070] Implementing this preferred embodiment, compared to existing technologies that can only passively respond to grid frequencies or rigidly execute charging tasks, allows for controlled voltage source behavior through local measurements alone, without the need for external communication. By integrating dynamically corrected physical boundaries with user charging time constraints, the system can smoothly switch between four modes: conventional grid configuration, charging-limited, discharging-limited, and forced charging, intelligently withdrawing steady-state frequency regulation power using an integral stage. This causal linkage control logic not only ensures operational stability under weak grid or islanded conditions but also balances grid regulation needs, user energy replenishment guarantees, and battery long-term service life.

[0071] Based on the above method embodiments, this application provides corresponding apparatus embodiments.

[0072] like Figure 5 As shown, one embodiment of this application provides a multi-mode network construction control device for a vehicle-to-the-grid system, including: a data reading module 100, a parameter determination module 200, a mode selection module 300, an instruction generation module 400, and an energy interaction control module 500; The data reading module 100 is used to read vehicle status data, power grid electrical data, and charging set time through the vehicle-to-grid system. The parameter determination module 200 is used to determine battery control parameters based on the vehicle status data, and to set the vehicle charge range based on the battery control parameters. Furthermore, the parameter determination module 200 includes: a current state acquisition unit, a temperature weight calculation unit, a charge range correction unit, a charge range determination unit, a damping coefficient correction unit, a charging power determination unit, and a charging time determination unit.

[0073] The current state acquisition unit is used to obtain the current state of charge, target state of charge, health status and temperature data from the vehicle state data; The temperature weight calculation unit is used to calculate the temperature sensitivity weight factor based on the temperature data. The charge range correction unit is used to correct the preset charge range according to the health status and the temperature sensitivity weighting factor to obtain the upper charge limit and the lower charge limit. The charge range determination unit is used to determine the upper charge limit and the lower charge limit as the vehicle charge range; The damping coefficient correction unit is used to correct the preset damping coefficient according to the health status and the temperature-sensitive weighting factor to obtain the dynamic damping coefficient. The charging power determination unit is used to determine the actual charging power based on the health status and the temperature-sensitive weighting factor. The charging time determination unit is used to determine the adaptive charging time based on the vehicle status data and the actual charging power.

[0074] The mode selection module 300 is used to select a target network operation mode from a plurality of preset network operation modes based on the vehicle charge range, the charging set time, and the vehicle status data. Furthermore, the mode selection module 300 includes: an overcharge risk generation unit, an over-discharge risk generation unit, a forced charging generation unit, and an operating mode selection unit.

[0075] The overcharge risk generation unit is used to compare the current state of charge with the upper limit of charge, and generate an overcharge risk signal when the current state of charge is greater than or equal to the upper limit of charge. The over-discharge risk generation unit is used to compare the current state of charge with the lower charge limit, and generate an over-discharge risk signal when the current state of charge is less than or equal to the lower charge limit; The forced charging generation unit is used to determine the remaining charging time according to the charging set time, compare the remaining charging time with the adaptive charging time, and generate a forced charging signal when the remaining charging time is less than or equal to the adaptive charging time. The operation mode selection unit is used to select the target network operation mode from a plurality of preset network operation modes based on the overcharge risk signal, the over-discharge risk signal and the forced charging signal.

[0076] The instruction generation module 400 is used to generate a grid connection control instruction for controlling the vehicle-to-grid system based on the target grid connection operation mode, the battery control parameters, and the grid electrical data. Furthermore, the instruction generation module 400 includes: a mode parameter determination unit, an integral correction unit, an angular frequency generation unit, a current reference generation unit, and a control instruction generation unit.

[0077] The mode parameter determination unit is used to determine the active power command, the lower limit of integration, and the upper limit of integration according to the target network operation mode. The integral correction unit is used to perform integral correction on the frequency deviation according to the lower integral limit and the upper integral limit to obtain the angular frequency command. The angular frequency generation unit is used to generate the output voltage angular frequency based on the active power command, the angular frequency command, the dynamic damping coefficient, and the power grid electrical data. The current reference generation unit is used to generate a current reference command based on the output voltage angular frequency and the power grid electrical data; The control command generation unit is used to generate the network control command based on the current reference command.

[0078] The energy interaction control module 500 is used to control the vehicle-to-grid system and the power supply network to perform bidirectional energy interaction according to the grid construction control command.

[0079] It is understood that the above-described device embodiments correspond to the method embodiments of this application, and can implement the multi-mode network construction control method for vehicle-to-network systems provided by any of the above-described method embodiments of this application.

[0080] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0081] Based on the above-described embodiment of a multi-mode network construction control method for a vehicle-to-the-network system, another embodiment of this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a multi-mode network construction control method for a vehicle-to-the-network system according to any embodiment of this application.

[0082] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more module units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0083] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0084] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0085] Based on the above-described method embodiments, another embodiment of this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a multi-mode network construction control method for a vehicle-to-the-network system as described in any of the above-described method embodiments of this application.

[0086] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0087] Based on the above-described method embodiments, another embodiment of this application provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a multi-mode network construction control method for a vehicle-to-the-network system.

[0088] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, or improvements made by those skilled in the art within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A multi-mode network construction control method for a vehicle-to-the-network system, characterized in that, include: The vehicle-to-grid system reads vehicle status data, grid electrical data, and charging set time. The vehicle-to-grid system is used to connect the vehicle energy storage unit and the power supply network to realize bidirectional energy interaction between the vehicle energy storage unit and the power supply network. Based on the vehicle status data, determine the battery control parameters, and set the vehicle charge range based on the battery control parameters; Based on the vehicle's charge range, the charging set time, and the vehicle status data, a target network operation mode is selected from a set of preset network operation modes; Based on the target grid connection operation mode, the battery control parameters, and the grid electrical data, a grid connection control command for controlling the vehicle-to-grid system is generated. The vehicle-to-grid system is controlled to perform bidirectional energy interaction with the power supply network according to the network construction control command.

2. The multi-mode network construction control method for the vehicle-to-grid system according to claim 1, characterized in that, The step of determining battery control parameters based on the vehicle status data and setting the vehicle charge range based on the battery control parameters includes: Battery control parameters are obtained from the vehicle status data, including health status and temperature data. Based on the temperature data, the temperature sensitivity weighting factor is calculated. The preset charge range is corrected based on the health status and the temperature-sensitive weighting factor to obtain the vehicle charge range.

3. The multi-mode network construction control method for the vehicle-to-grid system according to claim 2, characterized in that, The step of selecting a target network operation mode from a set of preset network operation modes based on the vehicle's charge range, the charging set time, and the vehicle status data includes: The current state of charge is obtained from the vehicle status data; The actual charging power is determined based on the health status and the temperature-sensitive weighting factor. The adaptive charging time is determined based on the vehicle status data and the actual charging power. The current state of charge is compared with the upper limit of the vehicle's charge range, and an overcharge status indicator is determined based on the comparison result. When the current state of charge is greater than or equal to the upper limit of the charge range, the overcharge status indicator is determined to be a triggered state; otherwise, the overcharge status indicator is determined to be a non-triggered state. The current state of charge is compared with the lower limit of the vehicle's charge range, and an over-discharge status identifier is determined based on the comparison result. When the current state of charge is less than or equal to the lower limit of charge, the over-discharge status identifier is determined to be a triggered state; otherwise, the over-discharge status identifier is determined to be a non-triggered state. The remaining charging time is determined according to the charging set time, and the remaining charging time is compared with the adaptive charging time. A forced charging status indicator is determined according to the comparison result. When the remaining charging time is less than or equal to the adaptive charging time, the forced charging status indicator is determined to be in a triggered state; otherwise, the forced charging status indicator is determined to be in a non-triggered state. Based on the combination of the overcharge status indicator, the over-discharge status indicator, and the forced charging status indicator, the target network operation mode is selected from a plurality of preset network operation modes.

4. The multi-mode network construction control method for the vehicle-to-grid system according to claim 3, characterized in that, The step of selecting the target network operation mode from a set of preset network operation modes based on the combination of the overcharge status indicator, the over-discharge status indicator, and the forced charging status indicator includes: When the overcharge status indicator, the over-discharge status indicator, and the forced charging status indicator are all in an untriggered state, a preset conventional network construction mode is selected as the target network construction operation mode. When the overcharge state is marked as triggered and the forced charging state is marked as not triggered, a preset charging-limited mode is selected as the target network operation mode. When the overcharge state is marked as non-triggered, the over-discharge state is marked as triggered, and the forced charging state is marked as non-triggered, a preset discharge-limited mode is selected as the target network operation mode. When the overcharge status is marked as not triggered and the forced charging status is marked as triggered, a preset forced charging mode is selected as the target network operation mode.

5. The multi-mode network construction control method for the vehicle-to-grid system according to claim 4, characterized in that, The step of generating grid connection control commands for controlling the vehicle-to-grid system based on the target grid connection operation mode, the battery control parameters, and the grid electrical data includes: Based on the target network operation mode, the active power command, the lower limit of integration, and the upper limit of integration are determined; based on the lower limit of integration, the upper limit of integration, and the power grid electrical data, the angular frequency command is obtained. The preset damping coefficient is corrected based on the battery control parameters and the temperature-sensitive weighting factor to obtain the dynamic damping coefficient; The output voltage angular frequency is generated based on the active power command, the angular frequency command, the dynamic damping coefficient, and the power grid electrical data. Based on the output voltage angular frequency and the power grid electrical data, a current reference command is generated; The network control command is generated based on the current reference command.

6. The multi-mode network construction control method for a vehicle-to-grid system according to claim 5, characterized in that, The step of determining the active power command, integral lower limit, and integral upper limit based on the target network operation mode includes: When the target network operation mode is the conventional network mode, the active power command, the lower limit of integration, and the upper limit of integration are all zero; When the target network operation mode is the charging-restricted mode, the integral lower limit is a negative open limit, and the active power command and the integral upper limit are zero; When the target network operation mode is the discharge-limited mode, the active power command and the integral lower limit are zero, and the integral upper limit is the positive open limit. When the target network operation mode is the forced charging mode, the lower limit of integration is a negative open limit, the upper limit of integration is a positive open limit, and the actual executed charging power is used as the active power command.

7. The multi-mode network construction control method for a vehicle-to-grid system according to claim 5, characterized in that, The step of generating a current reference command based on the output voltage angular frequency and the power grid electrical data includes: The output voltage phase angle is generated based on the output voltage angular frequency; Generate voltage amplitude instructions based on the power grid electrical data; The current reference command is generated based on the voltage amplitude command, the output voltage phase angle, and the power grid electrical data.

8. The multi-mode network construction control method for a vehicle-to-grid system according to claim 5, characterized in that, The step of generating the network control command based on the current reference command includes: Obtain the measured current of the vehicle-to-grid system; Based on the current reference command and the measured current, a modulation control quantity is generated; A PWM drive instruction is generated based on the modulation control quantity, and the PWM drive instruction is used as the network configuration control instruction.

9. A multi-mode network construction control device for a vehicle-to-grid system, characterized in that, include: The module includes a data reading module, a parameter determination module, a mode selection module, a command generation module, and an energy interaction control module. The data reading module is used to read vehicle status data, power grid electrical data, and charging set time through the vehicle-to-grid system; The parameter determination module is used to determine battery control parameters based on the vehicle status data, and to set the vehicle charge range based on the battery control parameters. The mode selection module is used to select a target network operation mode from a plurality of preset network operation modes based on the vehicle charge range, the charging set time, and the vehicle status data. The instruction generation module is used to generate a grid connection control instruction for controlling the vehicle-to-grid system based on the target grid connection operation mode, the battery control parameters, and the grid electrical data. The energy interaction control module is used to control the vehicle-to-grid system and the power supply network to perform bidirectional energy interaction according to the grid construction control command.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the multi-mode network construction control method for the vehicle-to-network system as described in any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-mode network construction control method for the vehicle-to-network system as described in any one of claims 1 to 8.

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