Electric vehicle charging and discharging control methods, devices and systems

Abstract

This invention discloses a charging and discharging control method, device, and system for electric vehicles. The electric vehicle receives collaborative control data sent by a virtual power plant and acquires its own vehicle operation data. The collaborative control data includes grid demand information, changes in the electric vehicle's electrical energy, and adjustment parameters. Based on the collaborative control data and vehicle operation data, the electric vehicle's power adjustment operation is determined, and finally, the electric vehicle is controlled to execute the power adjustment operation. Using this invention, the electric vehicle autonomously determines its power adjustment operation by acquiring its own operation data and combining it with the collaborative control data issued by the virtual power plant. This eliminates the need for real-time calculations by the virtual power plant server, improving the flexibility and efficiency of responding to grid demands and significantly enhancing the timeliness and adjustment effect of the virtual power plant's response to grid demands. This strongly supports the flexible regulation requirements of the grid and provides a reliable technical path for the large-scale participation of new energy vehicles in energy internet dispatch.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to a method, apparatus and system for controlling the charging and discharging of electric vehicles. Background Technology

[0002] With the increasing urgency of the power grid's demand for flexible regulation resources, research on technologies that aggregate the drive batteries of multiple electric vehicles to form a virtual power plant (VPP) and participate in grid regulation through demand response has become a hot topic in the industry. In existing electric vehicle-based virtual power plant systems, the drive batteries of ordinary passenger electric vehicles are regarded as the core adjustable resources of the virtual power plant. The system monitors the power supply and demand information of the power grid in real time within a specified period. When the power grid experiences a power shortage, the system coordinates the drive batteries of electric vehicles in the virtual power plant to discharge to the power grid to supplement the power gap; when the power grid has a power surplus, the system guides the drive batteries of electric vehicles to absorb the excess power and store the electrical energy in the batteries.

[0003] However, as the number of electric vehicles participating in virtual power plants continues to increase, the current mainstream model relies on the virtual power plant operator's server to uniformly generate charging and discharging instructions for each vehicle and send them one by one to the corresponding electric vehicle. In this model, the server needs to simultaneously process massive amounts of vehicle status data (such as remaining battery power, current location, and usage status) and grid demand data, and also needs to calculate personalized instructions for each vehicle. This leads to a sharp increase in server data processing load and a significant decrease in the efficiency of instruction generation and distribution. In extreme cases, instruction delays or even loss may occur, preventing electric vehicles from performing charging and discharging operations in a timely manner during grid demand periods, ultimately affecting the timeliness and regulation effectiveness of the virtual power plant in responding to grid demand. Summary of the Invention

[0004] The purpose of this invention is to provide a charging and discharging control method, device, and system for electric vehicles. The electric vehicle obtains operating data locally and autonomously determines the power adjustment operation by combining it with the collaborative control data issued by the virtual power plant. It does not rely on the real-time calculation of the virtual power plant server, which not only improves the flexibility and efficiency of responding to grid demand, but also significantly improves the timeliness and regulation effect of the virtual power plant in responding to grid demand. This strongly supports the flexible regulation requirements of the grid and provides a reliable technical path for the large-scale participation of new energy vehicles in energy internet dispatch.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for controlling the charging and discharging of an electric vehicle, comprising:

[0006] Receive collaborative control data sent by the virtual power plant; wherein, the collaborative control data includes grid demand information, changes in the electric power of electric vehicles, and adjustment parameters;

[0007] Acquire vehicle operation data of the electric vehicle; wherein, the vehicle operation data includes status data and remaining battery power;

[0008] Based on the collaborative control data and the vehicle operation data, the power adjustment operation of the electric vehicle is determined; wherein, the power adjustment operation is a charging operation or a discharging operation.

[0009] Control the electric vehicle to perform the battery adjustment operation.

[0010] As an improvement to the above solution, after controlling the electric vehicle to perform the power adjustment operation, the method further includes:

[0011] The vehicle operation data and charging / discharging execution results are sent to the virtual power plant, so that the virtual power plant can generate adjustment parameters for the next charging / discharging time based on the collaborative control data, the vehicle operation data, and the charging / discharging execution results through the first network model.

[0012] As an improvement to the above scheme, the adjustment parameters include the weighting coefficient and bias value of the power grid demand information, the change in power energy, the status data, and the remaining power energy.

[0013] As an improvement to the above scheme, the power grid demand information includes the demand response time and target charging and discharging amount that the current electric vehicles need to respond to, determined by the virtual power plant based on a first parameter; wherein, the first parameter includes the power supply and demand information of the power grid during a specified period, the total number of electric vehicles, and the battery capacity of the electric vehicles.

[0014] As an improvement to the above scheme, the change in electrical energy is the change in electrical energy of the electric vehicle from the current moment to the start of the demand response moment, predicted by the virtual power plant based on the second parameter; wherein, the second parameter includes the current location of the electric vehicle, driving habits, and environmental information.

[0015] As an improvement to the above solution, the status data includes at least one of the following: the current charge / discharge state of the battery, the vehicle status, the battery health, and the maximum charge / discharge capacity.

[0016] As an improvement to the above solution, the step of determining the electric vehicle's battery adjustment operation based on the collaborative control data and the vehicle operation data includes:

[0017] The collaborative control data and the vehicle operation data are preprocessed.

[0018] The preprocessed collaborative control data and vehicle operation data are input into the second network model, and the power adjustment operation output by the second network model is obtained.

[0019] As an improvement to the above solution, controlling the electric vehicle to perform the battery adjustment operation includes:

[0020] Control the electric vehicle to travel to the designated power grid connection point;

[0021] After detecting that the electric vehicle is connected to the power grid, the battery is controlled to perform a power adjustment operation according to the target charge and discharge amount in the power grid demand information.

[0022] To achieve the above objectives, embodiments of the present invention also provide an electric vehicle charging and discharging control device, comprising:

[0023] The collaborative control data receiving module is used to receive collaborative control data sent by the virtual power plant; wherein, the collaborative control data includes grid demand information, changes in the electric power of electric vehicles, and adjustment parameters;

[0024] The vehicle operation data acquisition module is used to acquire vehicle operation data of the electric vehicle; wherein, the vehicle operation data includes status data and remaining battery power;

[0025] A power adjustment operation determination module is used to determine the power adjustment operation of the electric vehicle based on the coordinated control data and the vehicle operation data; wherein the power adjustment operation is a charging operation or a discharging operation.

[0026] The battery adjustment operation execution module is used to control the electric vehicle to perform the battery adjustment operation.

[0027] To achieve the above objectives, embodiments of the present invention also provide an electric vehicle charging and discharging control system, comprising:

[0028] Virtual power plants are used to send collaborative control data to electric vehicles;

[0029] An energy management system is used to monitor the power grid's operating status and send power supply and demand information to the virtual power plant.

[0030] The power grid connection point serves as the physical interface between the electric vehicle and the power grid.

[0031] An electric vehicle is used to perform the electric vehicle charging and discharging control method described in any of the above embodiments.

[0032] Compared to existing technologies, the electric vehicle charging and discharging control method, device, and system disclosed in this invention address the problems of high processing load, low command efficiency, and poor response timeliness caused by the reliance on servers to uniformly generate and issue charging and discharging commands when the scale of vehicles in existing electric vehicle-based virtual power plant systems expand. This is achieved through multi-dimensional optimization via cloud-based collaborative control and a local data decision-making architecture. On one hand, the virtual power plant issues collaborative control data containing grid demand information, power changes, and adjustment parameters to the electric vehicles, replacing the personalized charging and discharging commands generated by traditional servers. This significantly reduces the massive amount of data and computational pressure that servers need to process, avoids the risk of command delays or loss, and ensures the timeliness of command transmission during grid demand periods. On the other hand, the electric vehicle obtains operating data locally and autonomously determines power adjustment operations based on the collaborative control data, without relying on real-time server calculations and decisions. This improves the flexibility and efficiency of a single vehicle in responding to grid demands and ensures accurate matching between local decisions and grid demands and the actual capabilities of the vehicle through parameter adjustment. This invention achieves efficient collaborative scheduling of large-scale electric vehicles participating in virtual power plants without increasing server load, significantly improving the timeliness and regulation effect of virtual power plants in response to grid demand, strongly supporting the grid's flexible regulation needs, and providing a reliable technical path for the large-scale participation of new energy vehicles in energy internet scheduling. Attached Figure Description

[0033] Figure 1 This is an architecture diagram of the electric vehicle charging and discharging control system provided in an embodiment of the present invention;

[0034] Figure 2 This is a flowchart of an electric vehicle charging and discharging control method provided in an embodiment of the present invention;

[0035] Figure 3 This is a flowchart of the power adjustment determination operation provided in an embodiment of the present invention;

[0036] Figure 4 This is a flowchart of the power adjustment operation provided in an embodiment of the present invention;

[0037] Figure 5 This is another flowchart of an electric vehicle charging and discharging control method provided in an embodiment of the present invention;

[0038] Figure 6 This is a structural block diagram of an electric vehicle charging and discharging control device provided in an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] See Figure 1 , Figure 1 This is an architectural diagram of an electric vehicle charging and discharging control system provided in an embodiment of the present invention. The electric vehicle charging and discharging control system includes:

[0041] Virtual power plant (VPP) is used to send collaborative control data to electric vehicles;

[0042] An Energy Management System (EMS) is used to monitor the power grid's operating status and send power supply and demand information to the virtual power plant.

[0043] The power grid connection point serves as the physical interface between the electric vehicle and the power grid.

[0044] Electric vehicles are used to perform battery adjustment operations.

[0045] For example, the virtual power plant (VPP) occupies the core coordinating level of the architecture, responsible for aggregating the drive battery resources of multiple electric vehicles to form a virtual adjustable resource pool that can flexibly respond to grid demands. It serves as the central hub connecting distributed electric vehicles with the grid and the EMS (Electrical Management System), playing a crucial role in receiving EMS commands and coordinating the charging and discharging behavior of electric vehicles. Each electric vehicle (number n) acts as an adjustable resource unit within the VPP, with its drive battery possessing bidirectional charging and discharging capabilities, allowing it to discharge to and from the grid. Multiple electric vehicles, aggregated through the VPP, become a virtual power source / load with scalable adjustment capabilities. The grid connection point serves as the interface for physical connection between electric vehicles and the grid, enabling energy exchange. Electric vehicles in different regions can connect to the grid through the nearest connection point to complete charging and discharging operations, ensuring the geographical scope and flexibility of energy exchange. The Energy Management System (EMS) is located at the upper control level of the architecture and is responsible for the overall energy management of the power grid. This includes real-time monitoring of power supply and demand information (such as power shortage / surplus), generating demand response commands, and transmitting the commands to the Virtual Power Plant (VPP). At the same time, the EMS also receives electric vehicle charging and discharging execution data fed back by the VPP to achieve closed-loop management of the coordinated scheduling of the power grid and the virtual power plant.

[0046] In existing technologies, if the server directly issues personalized charging and discharging commands to each electric vehicle, the large amount of data can lead to high server load and command delays / losses. This architecture, however, uses a layered logic—EMS coordinating grid-level needs, VPP aggregating distributed electric vehicles and issuing lightweight collaborative control data, and electric vehicles executing local decisions—to reduce the real-time computation and command issuance pressure on the upper layers (EMS, VPP) while ensuring the timeliness and flexibility of electric vehicle responses. Simultaneously, the distributed layout of grid connection points solves the physical connection problem for electric vehicles in multiple regions to access the grid, making it more feasible for large-scale electric vehicles to participate in grid regulation.

[0047] See Figure 2 , Figure 2 This is a flowchart of an electric vehicle charging and discharging control method provided by an embodiment of the present invention. The electric vehicle charging and discharging control method is applied to an electric vehicle and includes steps S1 to S4.

[0048] S1. Receive collaborative control data sent by the virtual power plant; wherein, the collaborative control data includes grid demand information, changes in the electric vehicle's power consumption, and adjustment parameters.

[0049] For example, the grid demand information is used to instruct the virtual power plant on the power supply and demand information of the grid for a specified period, such as power shortage or power surplus. The power change amount is used to indicate the change in power of the electric vehicle's drive battery due to driving power consumption or charging replenishment from the current time to the start of the demand response time, providing a predictive basis for the electric vehicle to determine whether it has the ability to meet the target charging and discharging capacity. The adjustment parameters are used to adapt to the individual characteristics of the electric vehicle (such as battery aging level, driving frequency) and the priority of grid demand, optimize the estimation accuracy of local power adjustment operations, and avoid response deviations caused by parameter weight imbalances.

[0050] Specifically, the grid demand information includes the demand response time and target charging / discharging amount that the current electric vehicles need to respond to, determined by the virtual power plant based on a first parameter; wherein, the first parameter includes the power supply and demand information of the grid during a specified period, the total number of electric vehicles, and the battery capacity of the electric vehicles.

[0051] For example, power supply and demand information for a specified period of time in the power grid can be obtained by monitoring the Energy Management System (EMS). It should be noted that the process of predicting the power load for a specified period in the future based on historical power load data can refer to existing technologies; similarly, the process of predicting the power generation output for a specified period in the future based on historical power generation output data can refer to existing technologies and will not be elaborated upon here. For instance, the EMS obtains predicted power load and predicted power generation output by collecting real-time power generation output data (including real-time power generation from various power sources such as thermal, hydro, and new energy sources) and user power load data (including real-time power consumption from various sectors such as industry, commerce, and residential) across the entire power grid, combined with a pre-set power supply and demand balance model (such as neural networks or temporal convolutional networks). Furthermore, power generation output prediction and power load prediction share similarities in principle and method; many power load prediction techniques and models can also be applied to power generation output prediction after adjustment and optimization.

[0052] For example, when the predicted electricity load and predicted generation output for a specified time period (e.g., 12:00-14:00) are obtained, these two values ​​are compared. If the predicted electricity load is higher than the predicted generation output, the specified time period is determined to be a power shortage state; if the predicted electricity load is lower than the predicted generation output, the specified time period is determined to be a power surplus state. This specified time period is the demand response time of the power grid. Simultaneously, based on the above monitoring results, the EMS quantifies the power supply and demand deviation at the demand response time, such as a shortage of 10,000 kWh or a surplus of 50,000 kWh. Finally, the EMS sends this power supply and demand status (shortage / surplus), deviation magnitude, and demand response time as power supply and demand information to the Virtual Power Plant (VPP). After receiving the data, the Virtual Power Plant (VPP) combines the pre-aggregated total number of electric vehicles (e.g., 1000 vehicles) and the battery capacity of each vehicle (e.g., maximum charge / discharge capacity of 80kWh per vehicle) to decompose the power supply and demand information into specific tasks for each electric vehicle. Based on the total number of electric vehicles and their battery capacities, the VPP determines the target charge / discharge amount for each vehicle. For example, if there is a power shortage of 10,000kWh, and there are 1000 electric vehicles, each vehicle needs to discharge an average of 10kWh. It should be noted that the actual discharge amount of each electric vehicle can be fine-tuned according to its individual battery capacity, ultimately forming the grid demand information.

[0053] Specifically, the change in electrical energy is the change in electrical energy of the electric vehicle predicted by the virtual power plant based on a second parameter from the current moment to before the start of the demand response moment; wherein, the second parameter includes the current location of the electric vehicle, driving habits, and environmental information.

[0054] For example, current location, driving habits, and environmental information are all fed back in real time by the electric vehicle. Driving habits include data such as historical destinations and typical travel times (e.g., commuting from 8:00 to 9:00 on weekdays), which are continuously recorded by the electric vehicle's onboard system and periodically uploaded to the virtual power plant. Environmental information includes current and future ambient temperatures (affecting vehicle energy consumption) and road condition information (traffic jams also affect vehicle energy consumption), which are obtained in real time by the electric vehicle through meteorological and traffic service interfaces associated with the positioning module and fed back to the virtual power plant in real time.

[0055] For example, after receiving the second parameter from the electric vehicle, the virtual power plant predicts the change in the electric vehicle's electrical energy from the current time to before the start of the demand response time. An embodiment of the present invention provides a calculation example, including the following process:

[0056] 1) Based on the electric vehicle's current location and the planned driving route before the start of the demand response time (combined with historical destinations and typical travel times in driving habits, such as a fixed route from the company to the residence), the GPS coordinates of the route are obtained through the positioning module to obtain the estimated driving mileage;

[0057] 2) Based on the estimated driving mileage obtained in step 1) and the environmental correction factor determined according to environmental information, calculate the total driving energy consumption;

[0058] First, the environmental correction factor is calculated based on the environmental information. The environmental correction factor satisfies the following formula:

[0059] (1);

[0060] in, This is an environmental correction factor; This is a temperature correction factor; for example, it is 0.15 when the ambient temperature is >30℃ or <5℃, and 0 otherwise. For the first The road condition correction coefficient for a road segment requires pre-dividing the planned travel route into n segments. The value of n can be set based on experience, and n is directly proportional to the length of the planned travel route. Then, the congestion situation of each segment is predicted (if a segment has both congested and smooth sections, it is classified as a congested segment). The value for congested segments is 0.1, and the value for smooth sections is 0. The road condition correction coefficient for the entire planned travel route is obtained by summing them up. It should be noted that the process of predicting whether a segment will be congested at a certain time period can refer to existing technologies, which will not be elaborated here.

[0061] Then, based on the estimated mileage, environmental correction factor, and preset base energy consumption per unit mileage, the total mileage energy consumption is calculated, satisfying the following formula:

[0062] (2);

[0063] in, Total driving energy consumption, in kWh; The estimated mileage is in km. This is the basic energy consumption per unit mileage, expressed in kWh / km, which is the standard energy consumption value set by the vehicle at the factory.

[0064] 3) By combining the current battery level of the electric vehicle, it can be determined whether there is a charging plan. If the battery level is too low (below a certain threshold), there is a charging plan. In this case, the change in energy is the difference between the charging amount (in kWh) and the total driving energy consumption. The charging amount can be calculated from the difference between the full battery level and the current battery level. Conversely, if the battery level is sufficient, there is no charging plan. In this case, the change in energy is the total driving energy consumption.

[0065] Specifically, the adjustment parameters include the power grid demand information, the change in electrical energy, the status data, and the weighted coefficient and bias value of the remaining power.

[0066] For example, the weighting coefficients are used to quantify the priority of the four types of input parameters—grid demand information, power change, status data, and remaining power—on the estimated charging and discharging results of electric vehicles. That is, the larger the weighting coefficient of a certain parameter, the higher its proportion in the decision-making logic for whether to perform charging and discharging. This adapts to the adjustment priorities in different scenarios. For example, when there is an emergency power shortage, the weighting coefficient of grid demand information is increased to prioritize responding to grid demand; when the vehicle's remaining power is too low, the weighting coefficient of the remaining power is increased to prioritize ensuring the vehicle's driving needs. The bias value is used to correct the estimation deviation caused by individual differences in electric vehicles (such as battery capacity of different models, battery aging degree, and user driving habits) or environmental fluctuations (such as the impact of extreme temperatures on the actual charging and discharging capacity of the battery). It is a fine-tuning compensation item for the weighted calculation results. For example, an old electric vehicle with a battery health of only 70% has a lower actual charging and discharging capacity than a new vehicle. By setting a positive bias value, the mismatch between the estimated results and the actual capacity due to deviations in basic parameters can be avoided.

[0067] For example, the weighting coefficients and bias values ​​of the grid demand information, energy change, status data, and remaining power are determined by training a pre-set first network model (a parameter optimization model based on a neural network) in the virtual power plant, combined with the historical response dataset of the current electric vehicle (including grid demand information, energy change, status data, remaining power, and final charging / discharging performance in previous charging / discharging scenarios). The first network model continuously iteratively optimizes the weighting coefficients of each parameter (making the weights of high-impact parameters more accurate) and bias values ​​(making individual difference compensation more suitable) through a backpropagation algorithm, ultimately generating personalized adjustment parameters adapted to the electric vehicle.

[0068] S2. Obtain the vehicle operation data of the electric vehicle; wherein, the vehicle operation data includes status data and remaining battery power.

[0069] For example, the status data includes at least one of the following: current battery charge / discharge state, vehicle status, battery health, and maximum charge / discharge capacity. The charge / discharge state refers to the current energy interaction state of the electric vehicle battery, specifically divided into charging, discharging, and non-charge / discharge states. The vehicle status refers to the overall operating state of the electric vehicle, including driving and stationary states. The battery health is an indicator used to characterize the difference between the current performance of the electric vehicle battery and its brand-new state. It is a relative value calculated by the Battery Management System (BMS) based on parameters such as the number of charge / discharge cycles, capacity decay, and internal resistance changes, and is usually presented as a percentage. For example, a battery health of 80% indicates that the current battery performance is 80% of its brand-new state. The maximum charge / discharge capacity is the maximum amount of charge / discharge that the electric vehicle battery can perform per unit time or in a single cycle under its current health state. It is determined by the BMS in conjunction with factors such as battery health and current temperature. For example, at 25℃ and with a battery health of 80%, the maximum charging capacity per unit time or in a single cycle is 80kW, and the maximum discharging capacity per unit time or in a single cycle is 70kW.

[0070] S3. Based on the collaborative control data and the vehicle operation data, determine the battery adjustment operation of the electric vehicle; wherein the battery adjustment operation is a charging operation or a discharging operation.

[0071] Further, see Figure 3 , Figure 3 This is a flowchart of the power adjustment operation provided in the embodiment of the present invention, wherein step S3 specifically includes steps S31 to S32.

[0072] S31. Perform data preprocessing on the collaborative control data and the vehicle operation data.

[0073] For example, outliers in grid demand information, energy change, and regulation parameters are removed, such as data exceeding reasonable ranges or with incorrect formats. Invalid data in status data and remaining power is also removed, such as garbled data caused by sensor malfunctions or values ​​that clearly do not conform to physical laws. The target charging / discharging capacity (if expressed in different units) and energy change in grid demand information from the collaborative control data are uniformly converted to kWh along with the remaining power in vehicle operation data. The demand response time in grid demand information and the time parameters involved in vehicle operation data are uniformly converted to time quantities in hours or minutes. Furthermore, using the current time as a benchmark, the time range of the demand response time in the collaborative control data is matched with the timestamp of vehicle operation data collection to ensure consistency of subsequent model input data in the time dimension, facilitating time-series correlation analysis and calculation.

[0074] S32. Input the preprocessed collaborative control data and the vehicle operation data into the second network model, and obtain the power adjustment operation output by the second network model.

[0075] For example, the second network model is a neural network model deployed locally on the electric vehicle. This model can be trained and generated by a virtual power plant server based on a large amount of historical charging and discharging data from electric vehicles (including charging and discharging operation results corresponding to different collaborative control data and vehicle operation data), and then distributed to the electric vehicle. When the preprocessed collaborative control data and vehicle operation data are input into the model, the second network model performs feature extraction, weighted calculation (using adjustment parameters), and logical judgment on these data. For example, if the second network model's judgment result satisfies: "remaining power + power change ≥ target charging / discharging amount in grid demand information" and "vehicle status allows (e.g., not driving, battery not in an abnormal state)," and the grid demand information indicates a power shortage, then the second network model outputs a power adjustment operation as a discharging operation; if the grid demand information indicates a power surplus, then the output power adjustment operation is a charging operation; if the above conditions are not met, the second network model will also output a result indicating that charging / discharging operations will not be performed temporarily.

[0076] S4. Control the electric vehicle to perform the power adjustment operation.

[0077] Further, see Figure 4 , Figure 4 This is a flowchart of the power adjustment operation provided in the embodiment of the present invention, wherein step S4 specifically includes steps S41 to S42.

[0078] S41. Control the electric vehicle to travel to the designated power grid connection point.

[0079] For example, the grid connection point is the power interface corresponding to a public charging pile, private charging pile, or transformer terminal unit (TTU) with bidirectional charging and discharging capabilities. Its location information (latitude, longitude, and address description) can be synchronously distributed by the virtual power plant in the collaborative control data, or distributed after determining that the vehicle needs to perform a power adjustment operation. The grid connection point must meet the secure communication protocol and power interaction standards with the power grid. If the vehicle is driverless, the electric vehicle's onboard navigation system will automatically receive and parse the location information of the grid connection point, plan the optimal driving route based on real-time traffic conditions, and control the vehicle to autonomously drive to the target connection point through the autonomous driving module. Upon arrival, it will automatically complete the parking and docking preparation actions. If the vehicle is driven, the onboard system will push the location of the grid connection point to the navigation interface, guide the driver to the location with voice and text prompts, and display the specific parking location and connection operation instructions when approaching the target point.

[0080] S42. After detecting that the electric vehicle is connected to the power grid, control the battery to perform a power adjustment operation according to the target charge and discharge amount in the power grid demand information.

[0081] For example, after an electric vehicle is connected to the power grid, the on-board battery management system (BMS) will first authenticate the vehicle with the control unit at the power grid connection point through the communication module to confirm that the vehicle has been included in the virtual power plant dispatch range and exchange safety protocol parameters (such as charging and discharging voltage and current thresholds). After the verification is successful, the BMS monitors the current status of the battery (temperature, voltage, and health) in real time. If the status is normal, the adjustment process is initiated based on the target charging and discharging amount in the power grid demand information. If the power adjustment operation is a charging operation, the BMS sends a charging request to the grid connection point, controls the battery to absorb electrical energy according to the target charging amount (e.g., 60kWh), and provides real-time feedback on the charging progress. When the actual charging amount reaches the target value or a battery abnormality is detected (e.g., excessively high temperature), the charging is automatically terminated and the connection is disconnected. If the power adjustment operation is a discharging operation, the BMS activates the battery reverse discharge function, outputs electrical energy to the grid according to the target discharge amount (e.g., 40kWh) and the grid's allowed discharge power (e.g., maximum 20kW), dynamically balances the discharge rate and battery health monitoring during the process, such as avoiding deep discharge of the battery to below 20% of its capacity. When the actual discharge amount reaches the target value or the demand response time ends, the discharging is automatically stopped and the actual charge and duration of this charging and discharging operation are recorded.

[0082] Further, see Figure 5 , Figure 5 This is another flowchart of an electric vehicle charging and discharging control method provided in an embodiment of the present invention. After step S4 is executed, the method further includes:

[0083] S5. Send the vehicle operation data and charging / discharging execution results to the virtual power plant, so that the virtual power plant can generate adjustment parameters for the next charging / discharging time estimation based on the collaborative control data, the vehicle operation data and the charging / discharging execution results through the first network model.

[0084] For example, the charging / discharging execution result includes one or more of the following: actual charging / discharging amount (reflecting the difference and deviation rate from the target charging / discharging amount), charging / discharging start and end time (reflecting whether it was completed within the demand response time), the highest / lowest battery temperature during the charging / discharging process, and whether charging / discharging interruption occurred (including the interruption reason and recovery time). After the vehicle completes a power adjustment operation, the acquired vehicle operation data and charging / discharging execution result are sent to the virtual power plant. The virtual power plant inputs the collaborative control data, the vehicle operation data, and the charging / discharging execution result into the first network model so that the first network model learns the correlation between various parameters and the charging / discharging execution effect under different scenarios, such as the matching relationship between the urgency of grid demand and the actual response speed, the influence law of battery status data and charging / discharging deviation, and the error correction logic between the predicted and actual values ​​of energy change. The model weights are iteratively optimized through the backpropagation algorithm, and finally the adjustment parameters used by the electric vehicle for the next charging / discharging prediction are output.

[0085] It should be noted that in the initial stage, the adjustment parameters are fixed values, such as each weighting coefficient being equally allocated to 0.25, and the bias value being set to 0. Furthermore, the virtual power plant can process data from multiple electric vehicles in parallel and output the corresponding adjustment parameters for each electric vehicle, which are then sent to the electric vehicles for the next charging / discharging time estimation.

[0086] In this embodiment of the invention, by constructing a closed-loop mechanism of data feedback, model optimization, and parameter iteration, continuous optimization capability is provided for the collaborative scheduling of virtual power plants and electric vehicles. This ensures that the virtual power plant has a dynamic perception of the scheduling effect of each electric vehicle, and the accuracy of subsequent charging and discharging estimation is improved through continuous parameter optimization. This avoids response deviations caused by parameter fixation, and further enhances the collaborative scheduling efficiency and grid demand response reliability when a large number of electric vehicles participate in the virtual power plant.

[0087] Compared to existing technologies, the electric vehicle charging and discharging control method disclosed in this invention addresses the problems of high processing load, low command efficiency, and poor response timeliness caused by the reliance on servers to uniformly generate and issue charging and discharging commands when the scale of existing electric vehicle-based virtual power plant systems expands. It achieves multi-dimensional optimization through cloud-based collaborative control and a local data decision-making architecture. On the one hand, the virtual power plant issues collaborative control data containing grid demand information, power changes, and adjustment parameters to the electric vehicles, replacing the personalized charging and discharging commands generated by traditional servers. This significantly reduces the massive amount of data and computational pressure that servers need to process, avoids the risk of command delays or loss, and ensures the timeliness of command transmission during grid demand periods. On the other hand, the electric vehicle obtains operating data locally and autonomously determines power adjustment operations based on the collaborative control data, without relying on real-time server calculations and decisions. This improves the flexibility and efficiency of a single vehicle in responding to grid demands and ensures accurate matching between local decisions and grid demands and the actual capabilities of the vehicle through parameter adjustment. This invention achieves efficient collaborative scheduling of large-scale electric vehicles participating in virtual power plants without increasing server load, significantly improving the timeliness and regulation effect of virtual power plants in response to grid demand, strongly supporting the grid's flexible regulation needs, and providing a reliable technical path for the large-scale participation of new energy vehicles in energy internet scheduling.

[0088] See Figure 6 , Figure 6 This is a structural block diagram of an electric vehicle charging and discharging control device 100 provided in an embodiment of the present invention. The electric vehicle charging and discharging control device 100 includes:

[0089] The collaborative control data receiving module 11 is used to receive collaborative control data sent by the virtual power plant; wherein, the collaborative control data includes grid demand information, the change in electric power of electric vehicles, and adjustment parameters;

[0090] The vehicle operation data acquisition module 12 is used to acquire vehicle operation data of the electric vehicle; wherein, the vehicle operation data includes status data and remaining battery power;

[0091] The battery adjustment operation determination module 13 is used to determine the battery adjustment operation of the electric vehicle based on the cooperative control data and the vehicle operation data; wherein the battery adjustment operation is a charging operation or a discharging operation.

[0092] The battery adjustment operation execution module 14 is used to control the electric vehicle to perform the battery adjustment operation.

[0093] Furthermore, the electric vehicle charging and discharging control device 100 also includes:

[0094] The data transmission module is used to send the vehicle operation data and the charging and discharging execution results to the virtual power plant, so that the virtual power plant can generate the adjustment parameters to be used for the next charging and discharging time through the first network model based on the collaborative control data, the vehicle operation data and the charging and discharging execution results.

[0095] It is worth noting that the working process of each module in the electric vehicle charging and discharging control device 100 described in the embodiments of the present invention can refer to the working process of the electric vehicle charging and discharging control method described in the above embodiments, and will not be repeated here.

[0096] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for controlling charging and discharging of an electric vehicle, characterized by, The method comprises: receiving cooperative control data sent by a virtual power plant; wherein the cooperative control data comprises power grid demand information, an electric energy change amount of an electric vehicle, and an adjustment parameter; obtaining vehicle operation data of the electric vehicle; wherein the vehicle operation data comprises state data and a remaining electric quantity; determining an electric quantity adjustment operation of the electric vehicle according to the cooperative control data and the vehicle operation data; wherein the electric quantity adjustment operation is a charging operation or a discharging operation; controlling the electric vehicle to perform the electric quantity adjustment operation; sending the vehicle operation data and a charging and discharging execution result to the virtual power plant, so that the virtual power plant generates an adjustment parameter used in the next charging and discharging estimation based on the cooperative control data, the vehicle operation data, and the charging and discharging execution result through a first network model; wherein the adjustment parameter comprises a weighting coefficient and a bias value of the power grid demand information, the electric energy change amount, the state data, and the remaining electric quantity; the power grid demand information comprises a demand response time point and a target charging and discharging quantity at which the electric vehicle needs to respond, which are determined by the virtual power plant based on a first parameter; wherein the first parameter comprises power supply and demand information of a power grid specified period, a total amount of electric vehicles, and a battery capacity of an electric vehicle.

2. The electric vehicle charge and discharge control method according to claim 1, wherein The electric energy change amount is an electric energy change amount of the electric vehicle from a current time to a time before the demand response time point, which is predicted by the virtual power plant based on a second parameter; wherein the second parameter comprises a current position, driving habits, and environmental information of the electric vehicle.

3. The electric vehicle charge and discharge control method according to claim 1, wherein The state data comprises at least one of a current charging and discharging state of a battery, a vehicle state, a battery health degree, and a maximum charging and discharging capability.

4. The electric vehicle charge and discharge control method according to claim 1, wherein The method further comprises: performing data preprocessing on the cooperative control data and the vehicle operation data; inputting the preprocessed cooperative control data and vehicle operation data into a second network model, and obtaining an electric quantity adjustment operation output by the second network model.

5. The electric vehicle charge and discharge control method according to claim 1, wherein The method further comprises: controlling the electric vehicle to travel to a specified power grid connection point; after detecting that the electric vehicle is connected to the power grid, controlling the battery to perform an electric quantity adjustment operation according to the target charging and discharging quantity in the power grid demand information.

6. An electric vehicle charge-discharge control device characterized by comprising: The device comprises: a cooperative control data receiving module, configured to receive cooperative control data sent by a virtual power plant; wherein the cooperative control data comprises power grid demand information, an electric energy change amount of an electric vehicle, and an adjustment parameter; a vehicle operation data obtaining module, configured to obtain vehicle operation data of the electric vehicle; wherein the vehicle operation data comprises state data and a remaining electric quantity; an electric quantity adjustment operation determining module, configured to determine an electric quantity adjustment operation of the electric vehicle according to the cooperative control data and the vehicle operation data; wherein the electric quantity adjustment operation is a charging operation or a discharging operation; an electric quantity adjustment operation performing module, configured to control the electric vehicle to perform the electric quantity adjustment operation.

7. A charging and discharging control system for an electric vehicle, characterized in that, The method comprises: A virtual power plant for sending cooperative control data to an electric vehicle; An energy management system for monitoring the operation state of a power grid and sending power supply and demand information to the virtual power plant; A power grid connection point for serving as a physical interface between the electric vehicle and the power grid; An electric vehicle for implementing the electric vehicle charging and discharging control method according to any one of claims 1-5.

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