Bidirectional OBC intelligent charging and discharging scheduling method and system capable of supporting V2X function

Abstract

The invention discloses a bidirectional OBC intelligent charging and discharging scheduling method and system capable of supporting a V2X function, and the method comprises the steps: carrying out data collection, which comprises the collection of power grid demand information and the collection of user charging habits; s2, power grid load prediction is carried out, and the system predicts the load change trend of the power grid in a future time period by using historical load data and real-time monitoring data of the power grid and combining a prediction model of machine learning; s3, optimizing a charging and discharging strategy, and generating an optimal charging and discharging plan by the system through a scheduling algorithm according to a load prediction result; and S4, the system has a dynamic response capability, monitors the load fluctuation of the power grid and the user demand change in real time through a V2X module, and adjusts the charging and discharging plan in time. According to the invention, by introducing the load prediction and dynamic response algorithm, the system can perform intelligent scheduling according to the real-time load of the power grid, thereby greatly improving the intelligence and real-time response capability of the system.

Description

Technical Field

[0001] The present invention belongs to the technical field of on-vehicle charging, and specifically relates to a two-way OBC intelligent charging and discharging scheduling method and system for new energy vehicles that support the V2X function. Background Art

[0002] The two-way on-vehicle charger is one of the core components that support the V2X (Vehicle-to-Everything) technology. The V2X technology can achieve two-way communication and energy exchange between vehicles and the power grid, power facilities, other vehicles, and even smart homes, and supports energy management and intelligent scheduling. The current charging scheduling algorithms cannot well combine the grid demand and the user's charging habits for intelligent optimization, resulting in a lag in system response and an inability to balance the power supply and demand of the grid and the user's charging behavior in real time.

[0003] However, the traditional OBC system mainly focuses on one-way charging and lacks the dynamic response ability to grid demand and user behavior. At the same time, the existing charging scheduling algorithms are relatively simple and cannot optimize the user's charging habits and dynamically respond to the grid load, resulting in power waste or excessive load. Summary of the Invention

[0004] The purpose of the present invention is to provide a two-way OBC intelligent charging and discharging scheduling method and system for new energy vehicles that support the V2X function in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: A two-way OBC intelligent charging and discharging scheduling method that supports the V2X function, the method includes the following steps: S1: Perform data collection, including collecting grid demand information and user charging habits; S2: Perform grid load prediction. The system uses the historical load data and real-time monitoring data of the grid, combined with the prediction model of machine learning, to predict the load change trend of the grid in the future time period; S3: Optimize the charging and discharging strategy. According to the load prediction result, the system generates the optimal charging and discharging plan through the scheduling algorithm; S4: The system has the dynamic response ability. Through the V2X module, it can monitor the load fluctuations of the grid and the changes in user needs in real time, and adjust the charging and discharging plan in a timely manner; when the grid load exceeds the expectation or the user needs change, the system can automatically adjust the charging and discharging power to ensure the user experience and the balance of the grid.

[0006] Preferably, in the step S2, the prediction model formula is as follows: ; Where: is the grid load predicted by the system at time t, that is, the future power demand of the grid.

[0007] Represents historical load data, reflecting the past power grid load situation.

[0008] Is the real-time power grid load data at the current moment.

[0009] And Are weight coefficients, respectively used to adjust the contributions of historical data and real-time data in power grid load forecasting. The system can dynamically adjust these two coefficients according to the actual states of the power grid and vehicles.

[0010] Preferably, in step S2, it specifically includes the following steps: The system collects the historical load data of the power grid through the V2X module, and combines it with the real-time data to predict the future load situation of the power grid through this formula.

[0011] The historical load data can be obtained through the data interface of the power company, and the real-time load data is obtained by monitoring the current state of the power grid. The system dynamically adjusts the values of α and β according to the current load of the power grid to make the prediction more accurate.

[0012] When the system detects a sharp fluctuation in the power grid load, it may increase The value to enhance the weight of real-time data and ensure that the prediction result is more in line with the current situation.

[0013] Preferably, in step S3, according to the load forecasting result, the system generates an optimal charge and discharge plan through a scheduling algorithm. The charging strategy takes into account the remaining power demand of the user and the available charging time. The charging power calculation formula is: ; Where: Is the charging power set by the system.

[0014] Represents the maximum allowable charging power of the system, which is determined by the rated power of the battery and the charging device.

[0015] Is the power required by the user, such as the power still needed to fully charge the vehicle.

[0016] Represents the current available charging time, that is, the time period estimated by the system during which charging can be carried out; During high load periods of the power grid, the system will automatically start the discharge mode to feed the electrical energy of the vehicle back to the power grid. The discharge power calculation formula is:; ; Where: Is the discharge power executed by the vehicle at time t.

[0017] is the grid load power demand obtained through the aforementioned load prediction formula.

[0018] is the available electric quantity in the vehicle battery.

[0019] represents the time period allowed for discharging set by the user.

[0020] Preferably, in step S3, the system obtains the user's charging demand (such as the driving mileage or departure time set by the user) and the electricity price information of the power grid through V2X, and calculates the required charging amount in combination with the current state of the vehicle battery (SOC); the system then calculates the required charging power according to the predicted available charging time, and sets a reasonable charging speed under the maximum charging power limit. If the user demand is high (i.e., large) and the charging time is short, the system may charge close to the maximum power, otherwise a lower power is selected; · The system detects the load condition of the power grid in real time through V2X communication, and performs a discharging operation during high load periods in combination with the available electric quantity in the battery and the discharging strategy set by the user. The function can be a multi-variable function, comprehensively considering the power grid load and user demand. When the power grid load reaches a certain threshold, the system starts to discharge, but at the same time considers the time period allowed for discharging by the user to ensure that the discharging does not affect the user's subsequent vehicle use requirements. The system will dynamically adjust the discharging power according to the SOC state of the battery and the current power grid load condition.

[0021] Preferably, a two-way OBC intelligent charging and discharging scheduling system for a new energy vehicle supporting V2X function includes: A V2X communication unit for performing two-way communication with the power grid, power facilities and other vehicles through a vehicle networking protocol, obtaining power grid load data, electricity price fluctuation information and user charging demand in real time, and transmitting the data to the scheduling controller unit; A scheduling controller unit for receiving the power grid data and user demand from the V2X communication unit, generating a charging and discharging strategy in combination with a load prediction algorithm and a user behavior model, and sending a control instruction to the two-way OBC control unit; A two-way OBC control unit for controlling the charging and discharging power and direction of the on-vehicle charger according to the instruction of the scheduling controller unit, realizing the two-way energy flow between the vehicle battery and the power grid, and at the same time monitoring the battery state and returning the feedback data to the scheduling controller unit; A power grid monitoring and feedback unit for collecting the load fluctuation, renewable energy power generation amount and power supply and demand information of the power grid in real time, synchronizing the data to the scheduling controller unit through the V2X communication unit, and dynamically adjusting the power grid interaction strategy according to the scheduling strategy.

[0022] Preferably, a two-way OBC intelligent charging and discharging scheduling system for new energy vehicles supporting V2X function further includes a user behavior modeling unit for analyzing the user's historical charging records, travel time preferences, and charging priority settings to generate a user behavior model; The scheduling controller unit is also used to optimize the charging and discharging strategy according to the user behavior model, prioritize to meet the user's personalized needs, and send the optimized strategy to the two-way OBC control unit.

[0023] Preferably, a two-way OBC intelligent charging and discharging scheduling system for new energy vehicles supporting V2X function further includes a user interaction unit for displaying the charging and discharging plan, grid electricity price information, and revenue data to the user through a mobile terminal or in-vehicle display screen; The user interaction unit is also used to receive the charging priority settings input by the user (such as fast charging or energy-saving mode) and transmit the setting information to the scheduling controller unit.

[0024] Preferably, the two-way OBC control unit includes a battery management system (BMS) and a power conversion module; The battery management system is used to monitor the state of charge (SoC), state of health (SoH), and temperature data of the battery and feedback the data to the scheduling controller unit; The power conversion module is used to adjust the power direction of charging or discharging according to the scheduling instruction, connect to the vehicle battery and the grid through the CAN bus, and realize the bidirectional flow of energy.

[0025] Preferably, the grid monitoring and feedback unit is connected to the cloud platform through an intelligent electricity meter to collect the dynamic data of the grid in real time; The scheduling controller unit is also used to trigger the two-way OBC control unit to perform reverse discharging operation when the grid load exceeds the preset threshold, feedback electric energy to the grid, and synchronize the discharging revenue information to the user interaction unit.

[0026] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are: 1. In the present invention, by deeply integrating V2X communication technology with dynamic optimization algorithms, the collaborative interaction ability between vehicles and the power grid is significantly improved. The system can real-time sense the power grid load fluctuations, the status of renewable energy generation, and the user charging demand, and dynamically adjust the charging and discharging strategies based on intelligent algorithms. During the peak load period of the power grid, vehicles can discharge power in reverse to relieve the power supply pressure, and during the low load period, they can preferentially charge to utilize low-cost electric energy, thus optimizing the energy distribution efficiency. This two-way energy flow mechanism not only enhances the stability and flexibility of the power grid but also reduces the need for traditional power grid expansion and upgrading, providing technical support for the large-scale access of renewable energy. At the same time, the system realizes personalized scheduling through user behavior modeling, which not only guarantees the user travel demand but also creates additional economic benefits through intelligent discharging strategies, truly transforming the vehicle into a flexible energy storage node in the smart grid.

[0027] 2. In the present invention, the automation and intelligence characteristics of the system greatly simplify the operation complexity of charging management. Users do not need to manually intervene in the charging and discharging time or power setting, and the system can automatically optimize the scheme according to the real-time electricity price, power grid status, and personal habits. For example, before the preset travel time of the user, the system will first ensure that the battery meets the endurance demand; during the non-vehicle use period, it will dynamically adjust the charging and discharging behavior in combination with the power grid load, which not only avoids the power grid congestion caused by centralized charging but also obtains benefits through off-peak discharging. In addition, the built-in safety protection mechanism of the system continuously monitors the battery health status and the power grid interaction process, and actively cuts off the energy flow in case of abnormalities to ensure the safety of equipment and personnel. This design that takes into account both efficient energy management and user-friendly experience promotes the transformation of new energy vehicles from a single means of transportation to an integrated energy service terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0029] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0030] Referring to Figure 1 , A two-way OBC intelligent charging and discharging scheduling method for a new energy vehicle supporting V2X function, the method includes the steps of: S1: Perform data collection. First, collect power grid demand information: Through the V2X communication module, real-time obtain the supply and demand load data of the current power grid, including information such as the peak load period of the power grid, the renewable energy power generation, and the electricity price fluctuation. These data are used for subsequent optimization of the charging and discharging strategies.

[0031] Collect user charging habits: Record and analyze the user's historical charging behaviors, including charging frequency, charging duration, preferences (such as fast charging, slow charging), travel time, etc., to predict the user's future charging needs. S2: Conduct power grid load prediction. The system uses the historical load data and real-time monitoring data of the power grid, combines with the prediction model of machine learning, and predicts the load change trend of the power grid in the future time period. This prediction model can dynamically adjust the prediction results to ensure that the charging and discharging decisions can adapt to the actual needs of the power grid. In step S2, the prediction model formula is as follows: ; Where: is the power grid load predicted by the system at time t, that is, the future power demand of the power grid.

[0032] represents the historical load data, reflecting the past power grid load situation.

[0033] is the real-time power grid load data at the current moment.

[0034] and are the weight coefficients, which are used to adjust the contributions of historical data and real-time data in the power grid load prediction respectively. The system can dynamically adjust these two coefficients according to the actual states of the power grid and the vehicle.

[0035] In the said step S2, it specifically includes the following steps: The system collects the historical load data of the power grid through the V2X module, combines with the real-time data, and predicts the future load situation of the power grid through this formula.

[0036] The historical load data can be obtained through the data interface of the power company, and the real-time load data is obtained by monitoring the current state of the power grid. The system dynamically adjusts the values of α and β according to the current load of the power grid to make the prediction more accurate.

[0037] When the system detects a drastic fluctuation in the power grid load, it may increase the value of to enhance the weight of real-time data and ensure that the prediction result is more in line with the current situation.

[0038] S3: Optimize the charging and discharging strategy. According to the load prediction result, the system generates the optimal charging and discharging plan through the scheduling algorithm. In step S3, according to the load prediction result, the system generates the optimal charging and discharging plan through the scheduling algorithm. The charging strategy considers the user's remaining power demand and available charging time, and the charging power calculation formula is:

[0039] Where: is the charging power set by the system.

[0040] Represents the maximum allowable charging power of the system, which is determined by the rated power of the battery and the charging device.

[0041] is the required power of the user, such as the remaining power required to fully charge the vehicle.

[0042] Represents the currently available charging time, that is, the time period estimated by the system for charging; During high grid load periods, the system will automatically start the discharge mode, feedback the electrical energy of the vehicle to the grid, and the discharge power calculation formula is:

[0043] Where: is the discharge power executed by the vehicle at time t.

[0044] is the grid load power demand obtained through the aforementioned load prediction formula.

[0045] is the available power in the vehicle battery.

[0046] Represents the time period allowed for discharging set by the user The system obtains the user's charging requirements (such as the set driving mileage or departure time) and the grid electricity price information through V2X, and combines the current state of the vehicle battery (SOC) to calculate the required charging amount; the system then calculates the required charging power based on the predicted available charging time and sets a reasonable charging speed under the maximum charging power limit. If the user demand is high (i.e., large) and the charging time is short, the system may charge close to the maximum power, otherwise a lower power is selected; · The system detects the grid load situation in real time through V2X communication, combines the available power in the battery and the discharge strategy set by the user, and performs discharge operations during high load periods. The function can be a multi-variable function, comprehensively considering the grid load and user demand. When the grid load reaches a certain threshold, the system starts to discharge, but at the same time considers the time period allowed for discharging by the user to ensure that the discharge does not affect the user's subsequent vehicle use requirements. The system will dynamically adjust the discharge power according to the SOC state of the battery and the current grid load situation.

[0047] S4: The system has dynamic response capabilities. It monitors the load fluctuations of the power grid and changes in user demand in real time through the V2X module, and adjusts the charging and discharging plan in a timely manner. When the power grid load exceeds expectations or user demand changes, the system can automatically adjust the charging and discharging power to ensure the user experience and the balance of the power grid.

[0048] The present invention also provides a bidirectional OBC intelligent charging and discharging scheduling system that supports V2X functions, including: A V2X communication unit for bidirectional communication with the power grid, power facilities, and other vehicles through vehicle-to-everything protocols, obtaining power grid load data, electricity price fluctuation information, and user charging demands in real time, and transmitting the data to the scheduling controller unit; A scheduling controller unit for receiving power grid data and user demands from the V2X communication unit, generating charging and discharging strategies by combining load prediction algorithms and user behavior models, and sending control instructions to the bidirectional OBC control unit; A bidirectional OBC control unit for controlling the charging and discharging power and direction of the on-vehicle charger according to the instructions of the scheduling controller unit, realizing bidirectional energy flow between the vehicle battery and the power grid, while monitoring the battery state and returning feedback data to the scheduling controller unit; A power grid monitoring and feedback unit for collecting power grid load fluctuations, renewable energy power generation, and power supply and demand information in real time, synchronizing the data to the scheduling controller unit through the V2X communication unit, and dynamically adjusting the power grid interaction strategy according to the scheduling strategy.

[0049] A bidirectional OBC intelligent charging and discharging scheduling system for new energy vehicles that supports V2X functions further includes a user behavior modeling unit for analyzing the user's historical charging records, travel time preferences, and charging priority settings to generate a user behavior model; The scheduling controller unit is also used to optimize the charging and discharging strategy according to the user behavior model, prioritize meeting the user's personalized needs, and send the optimized strategy to the bidirectional OBC control unit.

[0050] It further includes a user interaction unit for displaying the charging and discharging plan, power grid electricity price information, and revenue data to the user through a mobile terminal or an in-vehicle display screen; The user interaction unit is also used to receive the charging priority settings input by the user (such as fast charging or energy-saving mode) and transmit the setting information to the scheduling controller unit.

[0051] The bidirectional OBC control unit includes a battery management system (BMS) and a power conversion module; The battery management system is used to monitor the state of charge (SoC), state of health (SoH), and temperature data of the battery, and feedback the data to the scheduling controller unit; The power conversion module is used to adjust the power direction of charging or discharging according to the scheduling instruction, and is connected to the vehicle battery and the power grid through the CAN bus to achieve bidirectional energy flow.

[0052] The power grid monitoring and feedback unit is connected to the cloud platform through an intelligent electricity meter to collect the dynamic data of the power grid in real time; The scheduling controller unit is also used to trigger the bidirectional OBC control unit to perform reverse discharging operation and feed back electric energy to the power grid when the power grid load exceeds the preset threshold, and synchronize the discharging income information to the user interaction unit at the same time.

[0053] The bidirectional OBC intelligent charging and discharging scheduling system further includes a safety protection unit, which is used to monitor abnormal states (such as overvoltage, overcurrent or battery overheating) during the charging and discharging process and trigger an emergency stop mechanism; The safety protection unit is connected to the bidirectional OBC control unit through an independent circuit to ensure that the battery and power grid equipment can still be protected when the algorithm fails.

[0054] For the bidirectional OBC intelligent charging and discharging scheduling system, the V2X communication unit includes an on-vehicle unit (OBU) and a roadside unit (RSU); The on-vehicle unit is used to receive real-time instructions from the power grid scheduling center and transmit data to the scheduling controller unit through a wireless communication protocol (such as 5G or DSRC); The roadside unit is used to share the power grid status information with neighboring vehicles and power facilities to achieve coordinated charging and discharging scheduling within the area.

[0055] The scheduling controller unit also integrates edge computing capabilities, which are used to process power grid data and user requirements locally in real time to reduce cloud communication latency; The edge computing module is directly connected to the V2X communication unit and the bidirectional OBC control unit through a high-speed data bus to ensure fast response to scheduling instructions.

[0056] For the bidirectional OBC intelligent charging and discharging scheduling system, the system supports multi-vehicle coordinated scheduling. The charging and discharging plans of vehicles within the area are summarized to the scheduling controller unit through the V2X communication unit to optimize the load balance of the regional power grid; The scheduling controller unit is also used to allocate charging and discharging priorities according to the power distribution of the vehicle cluster and the power grid demand to maximize power grid stability and user benefits.

[0057] The present invention realizes: 1. Integration of Bidirectional OBC and V2X Function: The present invention deeply integrates the OBC system with V2X technology, enabling bidirectional energy flow. It can not only charge the vehicle but also feed back electrical energy to the power grid during peak grid loads to balance the grid load and enhance grid stability. This technology surpasses traditional unidirectional OBC systems and early V2G technologies and can better respond to the dual demands of the power grid and users.

[0058] 2. Dynamic Response and Intelligent Scheduling Algorithm: The scheduling algorithm in the patent can perform intelligent scheduling based on the real-time load of the power grid and the charging habits of users, dynamically adjusting the charge and discharge strategies. Through load prediction algorithms and behavior models, the system can reduce charging or discharge during peak grid loads and charge during low loads. This dynamic response ability greatly improves the intelligence level of the system, enabling it to operate efficiently in complex power grid environments.

[0059] 3. Load Prediction and User Behavior Modeling: Compared with previous static scheduling strategies based on electricity price fluctuations or schedules, the present invention introduces load prediction algorithms and user behavior modeling techniques, which can predict power grid demands based on real-time load changes and perform personalized scheduling according to users' charging habits. This makes the system more flexible and accurate in practical applications.

[0060] 4. Adaptive Control and Efficient Feedback Mechanism: The present invention ensures the dynamic balance of vehicle charging and discharging through adaptive control technology and feedback mechanisms, which can not only meet users' charging needs but also provide real-time support for the power grid. This intelligent control method enables the system to achieve optimal performance in different scenarios, greatly enhancing the efficiency and reliability of the system.

[0061] 5. User Experience Optimization: The automated and intelligent functions of the system in the present invention enable users to not manually adjust the charge and discharge time and strategies. The system can automatically schedule according to the needs of the power grid and users, reducing the operation complexity and enhancing the user experience. Additionally, an intelligent discharge mechanism creates additional benefits for users.

[0062] As can be seen from the above: The present invention proposes a bidirectional OBC (On-board Charger) system that supports V2X (Vehicle-to-Everything) function, enabling bidirectional energy flow between the vehicle and the power grid. It not only supports intelligent charging of the vehicle but also has an intelligent discharge function, allowing the vehicle to feed back power to the power grid during high grid load periods. This function combines V2X technology, making the vehicle an important node in the smart grid while supporting efficient interaction between the power grid and users.

[0063] The present invention proposes an algorithm that can dynamically adjust the charging and discharging strategies according to the real-time changes in the power grid load. Through the load prediction formula and user behavior model, the system can dynamically schedule the charging and discharging power to relieve the power grid load pressure or optimize charging using off-peak electricity prices. This intelligent scheduling can ensure a balance between the power grid demand and user demand.

[0064] The present invention integrates a V2X communication module, which can communicate with the power grid in real time and adjust the charging and discharging behavior of the vehicle in combination with the state of the power grid. This enables the vehicle to not only be an energy consumer of the power grid but also become an energy supplier when necessary, with flexible response capabilities.

[0065] The present invention uses a series of algorithms including load prediction, charging strategy optimization, and discharging strategy optimization, which can adjust the charging and discharging strategies in real time to ensure that the vehicle provides auxiliary services to the power grid while meeting user needs, improving the overall efficiency of the system.

[0066] The present invention improves the system intelligence and response capabilities: By introducing load prediction and dynamic response algorithms, the system can perform intelligent scheduling according to the real-time load of the power grid, thus greatly improving the system intelligence and real-time response capabilities. Previous OBC systems often relied on manual scheduling by users and could not effectively cope with the dynamic changes of the power grid. The system of the present invention can discharge during peak load periods and charge during off-peak periods, reducing the power grid pressure while also improving the economic benefits of users.

[0067] The present invention promotes the deep interaction between vehicles and the power grid: Through V2X technology, two-way communication between vehicles and the power grid is realized, promoting the development of V2G (Vehicle-to-Grid) technology, making the vehicle not only an energy consumer but also able to feedback electricity to the power grid according to the power grid demand. This way of deep interaction provides important support for the development of smart grids and also provides more sources of economic benefits for users.

[0068] Through intelligent discharging and load regulation strategies, the technology of the present invention can provide power support during peak power grid load periods, reduce the load fluctuation of the power grid, and thus improve the stability and reliability of the power grid. Compared with previous technologies, this improvement is particularly important for the efficient operation of modern smart grids.

[0069] By dynamically scheduling the charging power, the system can automatically select the off-peak period of the power grid load for charging, using off-peak electricity prices to save the charging cost for users. At the same time, users can also obtain economic compensation by feeding back the vehicle's electrical energy to the power grid during peak periods, further improving the economic benefits of users. Compared with the one-way charging method in previous technologies, the present invention greatly improves the user experience and charging efficiency.

[0070] The system in the present invention can perform adaptive optimization according to the real-time state of the power grid and the charging habits of users, and can handle different charging scenarios and load requirements. This optimization ability enables the system to continuously adjust its own parameters to ensure the best performance in different usage environments. Compared with the previous fixed-parameter optimization method, the flexibility of the system has been greatly enhanced.

[0071] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0072] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A two-way OBC intelligent charging and discharging scheduling method that supports V2X functions, characterized in that: The method includes the following steps: S1: Conduct data collection, including collecting power grid demand information and user charging habits; S2: Conduct power grid load prediction. The system uses the historical load data and real-time monitoring data of the power grid, combines with the prediction model of machine learning, and predicts the load change trend of the power grid in the future period; S3: Optimize the charging and discharging strategy. According to the load prediction result, the system generates the optimal charging and discharging plan through the scheduling algorithm; S4: The system has the ability of dynamic response. It monitors the load fluctuation of the power grid and the change of user demand in real time through the V2X module, and adjusts the charging and discharging plan in time; when the power grid load exceeds the expectation or the user demand changes, the system can automatically adjust the charging and discharging power to ensure the user experience and the balance of the power grid.

2. The two-way OBC intelligent charging and discharging scheduling method capable of supporting V2X functions according to claim 1, wherein: In step S2, the prediction model formula is as follows: ； Wherein: is the grid load predicted by the system at time t, i.e., the future power demand of the grid; Represents historical load data, reflecting the past power grid load situation; is the real-time grid load data at the current moment; and are weight coefficients, which are respectively used to adjust the contributions of historical data and real-time data in power grid load forecasting. The system can dynamically adjust these two coefficients according to the actual states of the power grid and vehicles.

3. The two-way OBC intelligent charge and discharge scheduling method capable of supporting V2X functions according to claim 1, characterized in that: In step S2, it specifically includes the following steps: The system collects the historical load data of the power grid through the V2X module, combines with the real-time data, and predicts the future load situation of the power grid through this formula; The historical load data can be obtained through the data interface of the power company, and the real-time load data is obtained by monitoring the current state of the power grid; the system dynamically adjusts the values of α and β according to the current load of the power grid to make the prediction more accurate; When the system detects a significant fluctuation in the grid load, it may increase the value to enhance the weight of real-time data and ensure that the prediction result is more in line with the current situation.

4. The two-way OBC intelligent charge and discharge scheduling method capable of supporting V2X functions according to claim 1, characterized in that: In step S3, according to the load prediction result, the system generates the optimal charging and discharging plan through the scheduling algorithm; the charging strategy considers the remaining power demand of the user and the available charging time, and the charging power calculation formula is: ； Wherein: is the charging power set by the system; Represents the maximum allowable charging power of the system, which is determined by the rated power of the battery and the charging device; is the amount of electricity required by the user, such as the amount of electricity still needed to fully charge the vehicle; Indicates the currently available charging time, that is, the time period estimated by the system during which charging can be carried out; In the high load period of the power grid, the system will automatically start the discharging mode, and feedback the electric energy of the vehicle to the power grid. The discharging power calculation formula is: ； Where: is the discharge power executed by the vehicle at time t; is the grid load power demand obtained by the aforementioned load forecasting formula; is the amount of electrical energy available in the vehicle battery; Indicates the time period set by the user for allowing discharge.

5. The two-way OBC intelligent charge and discharge scheduling method capable of supporting V2X functions as claimed in claim 1, wherein: In step S3, the system obtains the user's charging demand and the power grid price information through V2X, and combines the current state of the vehicle battery to calculate the required charging amount; the system then calculates the required charging power according to the predicted available charging time, and sets a reasonable charging speed under the maximum charging power limit; if the user demand is high and the charging time is short, the system may charge close to the maximum power, otherwise it selects a lower power; · The system detects the load situation of the power grid in real time through V2X communication, combines the available electric energy in the battery and the discharging strategy set by the user, and performs the discharging operation in the high load period; the function can be a multi-variable function, comprehensively considering the power grid load and user demand; when the power grid load reaches a certain threshold, the system starts to discharge, but at the same time considers the period when the user allows discharging to ensure that the discharging will not affect the user's subsequent vehicle use demand; the system will dynamically adjust the discharging power according to the SOC state of the battery and the current power grid load situation.

6. A bidirectional OBC intelligent charging and discharging scheduling system supporting V2X function, characterized in that: It includes: The V2X communication unit is used to conduct two-way communication with the power grid, power facilities and other vehicles through the vehicle networking protocol, obtain the power grid load data, electricity price fluctuation information and user charging demand in real time, and transmit the data to the scheduling controller unit; The scheduling controller unit is used to receive the power grid data and user demand from the V2X communication unit, generate the charging and discharging strategy by combining the load prediction algorithm and the user behavior model, and send the control instruction to the bidirectional OBC control unit; A bidirectional OBC control unit is used to control the charging and discharging power and direction of the on-vehicle charger according to the instructions of the scheduling controller unit, realize the bidirectional energy flow between the vehicle battery and the power grid, and at the same time monitor the battery status and return the feedback data to the scheduling controller unit; A power grid monitoring and feedback unit is used to collect the load fluctuations, renewable energy power generation and power supply and demand information of the power grid in real time, synchronize the data to the scheduling controller unit through the V2X communication unit, and dynamically adjust the power grid interaction strategy according to the scheduling strategy.

7. The two-way OBC intelligent charging and discharging scheduling system capable of supporting V2X functions according to claim 6, wherein: It also includes a user behavior modeling unit, which is used to analyze the user's historical charging records, travel time preferences and charging priority settings to generate a user behavior model; The scheduling controller unit is also used to optimize the charging and discharging strategy according to the user behavior model, give priority to meeting the personalized needs of users, and send the optimized strategy to the bidirectional OBC control unit.

8. The two-way OBC intelligent charging and discharging scheduling system capable of supporting V2X functions according to claim 6, characterized in that: It also includes a user interaction unit, which is used to display the charging and discharging plan, power grid electricity price information and revenue data to the user through a mobile terminal or an in-vehicle display screen; The user interaction unit is also used to receive the charging priority setting input by the user and transmit the setting information to the scheduling controller unit.

9. The bidirectional OBC intelligent charging and discharging scheduling system capable of supporting V2X functions according to claim 6, wherein: The bidirectional OBC control unit includes a battery management system and a power conversion module; The battery management system is used to monitor the state of charge, health status and temperature data of the battery, and feedback the data to the scheduling controller unit; The power conversion module is used to adjust the power direction of charging or discharging according to the scheduling instruction, connect to the vehicle battery and the power grid through the CAN bus, and realize the bidirectional energy flow.

10. The two-way OBC intelligent charging and discharging scheduling system capable of supporting V2X functions according to claim 6, characterized in that: The power grid monitoring and feedback unit is connected to the cloud platform through a smart meter to collect the dynamic data of the power grid in real time; The scheduling controller unit is also used to trigger the bidirectional OBC control unit to perform reverse discharging operation when the power grid load exceeds the preset threshold, feedback electric energy to the power grid, and at the same time synchronize the discharging revenue information to the user interaction unit.

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