Control method and device based on charging pile V2G, equipment and medium

Abstract

The invention provides a control method and device based on charging pile V2G, equipment and a medium. The method comprises the steps that power grid dynamic supply and demand parameters, vehicle state information and use plan information are collected in real time; constructing a multi-objective optimization model according to the dynamic supply and demand parameters of the power grid, the vehicle state information and the use plan information; setting constraint conditions in the multi-objective optimization model; wherein the constraint conditions comprise a vehicle state constraint, a time constraint, an electric quantity constraint, an equipment constraint and a power grid constraint; and solving the multi-objective optimization model according to a multi-objective optimization algorithm, generating an optimal charging and discharging strategy meeting constraint conditions, and issuing the optimal charging and discharging strategy to the V2G charging pile for execution. According to the invention, power grid peak and valley load distribution can be obviously improved, user charging cost is reduced, the service life of a battery is prolonged, stable operation of the power distribution network is ensured, efficient response and safe controllability of vehicle-network cooperative scheduling are realized, and bidirectional requirements of the power grid and the user are considered while energy management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and in particular to a control method, device, equipment and medium based on V2G charging piles. Background Art

[0002] As the number of new energy vehicles exceeds 20 million, the one-way charging mode of traditional charging piles can no longer meet energy management needs. V2G (vehicle-to-grid) technology can convert electric vehicles into distributed energy storage units through two-way energy flow, showing great potential in scenarios such as grid peak regulation, renewable energy consumption and emergency power supply. It is estimated that large-scale V2G application can reduce the peak-to-valley difference of the grid by 15%-20%, saving more than one trillion yuan in electricity supply costs.

[0003] The current grid management model faces the following two technical challenges when faced with a large number of electric vehicles randomly connected for charging: 1. Load shock problem: Random charging of vehicles can easily cause a sudden increase in load on the grid during local periods, forming peak loads and increasing the difficulty of peak regulation; 2. Lack of coordinated scheduling: The lack of precise vehicle-grid interaction strategies makes it difficult to balance the grid stability needs and user charging habits, resulting in inefficient power scheduling.

[0004] Therefore, the existing power grid management model has problems such as insufficient load regulation capability and lack of coordinated dispatching mechanism. Summary of the Invention

[0005] The embodiments of the present invention provide a control method, device, equipment and medium based on charging pile V2G, aiming to solve the problems of insufficient load regulation capability and lack of coordinated scheduling mechanism in the existing power grid management model.

[0006] In a first aspect, an embodiment of the present invention provides a control method based on a charging pile V2G, the method comprising: Real-time collection of dynamic grid supply and demand parameters, vehicle status information, and usage plan information; Constructing a multi-objective optimization model based on the dynamic supply and demand parameters of the power grid, the vehicle status information and the usage plan information; Setting constraints in the multi-objective optimization model; wherein the constraints include vehicle state constraints, time constraints, power constraints, equipment constraints, and power grid constraints; The multi-objective optimization model is solved according to a multi-objective optimization algorithm to generate an optimal charging and discharging strategy that meets the constraints, and the strategy is sent to the V2G charging pile for execution.

[0007] In a second aspect, an embodiment of the present invention further provides a control device based on a charging pile V2G, the device comprising: The acquisition unit is used to collect dynamic grid supply and demand parameters, vehicle status information, and usage plan information in real time; A construction unit, configured to construct a multi-objective optimization model based on the dynamic supply and demand parameters of the power grid, the vehicle status information, and the usage plan information; a setting unit, configured to set constraints in the multi-objective optimization model; wherein the constraints include vehicle state constraints, time constraints, power constraints, equipment constraints, and power grid constraints; A generation unit is used to solve the multi-objective optimization model according to a multi-objective optimization algorithm, generate an optimal charging and discharging strategy that meets the constraints, and send it to the V2G charging pile for execution.

[0008] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0009] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the method described in the first aspect can be implemented.

[0010] The present invention provides a control method, device, equipment and medium based on a V2G charging pile. The method includes: real-time collection of dynamic power grid supply and demand parameters, vehicle status information and usage plan information; constructing a multi-objective optimization model based on the dynamic power grid supply and demand parameters, the vehicle status information and the usage plan information; setting constraints in the multi-objective optimization model; wherein the constraints include vehicle status constraints, time constraints, power constraints, equipment constraints and power grid constraints; solving the multi-objective optimization model according to a multi-objective optimization algorithm, generating an optimal charging and discharging strategy that meets the constraints, and sending it to the V2G charging pile for execution. The embodiment of the present invention solves the multi-objective optimization model according to the multi-objective optimization algorithm, generates an optimal charging and discharging strategy that meets the constraints, and sends it to the V2G charging pile for execution, which can significantly improve the peak and valley load distribution of the power grid, enhance the renewable energy absorption capacity, reduce user charging costs and extend battery life, while ensuring the stable operation of the distribution network, realizing efficient response and safe control of vehicle-grid coordinated scheduling, and taking into account the two-way needs of the power grid and users while improving energy management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 A flow chart of a control method based on a charging pile V2G according to an embodiment of the present invention; Figure 2 A schematic block diagram of a V2G charging pile control device according to an embodiment of the present invention; Figure 3 A schematic block diagram of an electronic device provided in an embodiment of the present invention; Figure 4 Schematic diagram of the application environment of the control method based on charging pile V2G provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0014] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0015] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0016] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. The embodiment of the present invention provides a control method, device, equipment and medium based on charging pile V2G. The control method based on charging pile V2G can be found in Figure 4 , Figure 4The application environment schematic diagram of the control method based on the charging pile V2G provided by the embodiment of the application is shown. The control method based on the charging pile V2G is applied to a charging pile V2G control system, and the charging pile V2G control system comprises a bidirectional power conversion module, an intelligent scheduling control module, a communication protocol adaptation module, a battery health management module, a user interaction interface module and a safety protection module. The bidirectional power conversion module is used to achieve AC / DC and DC / AC bidirectional energy conversion to realize vehicle-to-grid energy interaction. The intelligent scheduling control module is used to dynamically adjust the charging and discharging strategy by means of a multi-objective optimization algorithm and coordinate the operation of each module. The communication protocol adaptation module is used to compatible different communication protocols to realize data interaction and instruction transmission of the power grid, vehicles and users. The battery health management module is used to estimate the SOC (state of charge) and SOH (state of health) in real time and monitor the battery state. The user interaction interface module is used to provide charging state display, parameter setting and man-machine interaction function. The safety protection module is used to implement overvoltage and overcurrent protection, leakage detection and thermal runaway early warning to ensure safe operation of the system. The application will be described in detail through specific embodiments.

[0017] Figure 1 The flowchart of the control method based on the charging pile V2G provided by the embodiment of the application is shown. As shown in the figure, the method comprises the following steps S110-S140. Figure 1

[0018] S110, real-time collection of power grid dynamic supply and demand parameters, vehicle state information and use plan information.

[0019] In this embodiment, the communication protocol adaptation module is used to compatible different communication protocols to realize data interaction and instruction transmission of the power grid, vehicles and users. The collected data can be converted into data that can be recognized by the system through the communication protocol adaptation module. The use plan information is sent to the communication protocol conversion module after being customized by the user on the man-machine interface.

[0020] In an embodiment, step S110 comprises: real-time collection of initial power grid dynamic supply and demand parameters, initial vehicle state information and initial use plan information; conversion of the initial power grid dynamic supply and demand parameters, the initial vehicle state information and the initial use plan information into power grid dynamic supply and demand parameters, vehicle state information and use plan information that can be recognized by the system according to a preset mass protocol conversion rule.

[0021] ​In this embodiment, the initial power grid dynamic supply and demand parameters, initial vehicle status information and initial usage plan information are collected in real time; through the preset massive protocol conversion rules and intelligent algorithms, the parsing, encapsulation and other functions of different protocols are encapsulated into a communication protocol adaptation module, so that the communication protocol adaptation module can accurately adapt to various protocols, whether it is the charging needs from different car companies or the data transmission interacting with the power grid, it can be smoothly undertaken; using the preset massive protocol conversion rules, the initial power grid dynamic supply and demand parameters, the initial vehicle status information and the initial usage plan information are converted into power grid dynamic supply and demand parameters, vehicle status information and usage plan information that can be recognized by the system. At the same time, intelligent algorithms (such as national secret algorithms SM2 and SM3) are introduced to strictly verify the identities of the communicating parties to prevent security risks such as illegal access and data tampering, thereby ensuring the purity and security of the communication link.

[0022] The embodiment of the present invention utilizes massive protocol conversion rules and intelligent algorithms to accurately translate data in one protocol format into the format required by another protocol in a very short time, ensuring smooth transmission of data between different devices and systems.

[0023] S120: Construct a multi-objective optimization model based on the dynamic supply and demand parameters of the power grid, the vehicle status information, and the usage plan information.

[0024] In this embodiment, the intelligent dispatching control module constructs a multi-objective optimization model based on the model predictive control (MPC) framework according to the dynamic supply and demand parameters of the power grid, the vehicle status information and the usage plan information. The multi-objective optimization model comprehensively considers the multi-dimensional key factors of the dynamic supply and demand parameters of the power grid, the vehicle status information and the usage plan information, guides vehicles to charge in a concentrated manner (valley charging) during periods of low electricity prices (such as at night) to reduce the charging load during peak periods (such as daytime); and dispatches vehicles to discharge (peak discharge) during peak periods of the power grid (such as peak electricity consumption) to alleviate the pressure on the power grid.

[0025] Specifically, typical goals include: minimizing electricity costs: charging during low-price periods and discharging during high-price periods; grid load balancing: smoothing the load curve and reducing peak-to-valley differences; battery health management: avoiding overcharging and over-discharging, and extending battery life; and meeting user needs: recommending the optimal charging time based on travel plans.

[0026] S130. Setting constraints in the multi-objective optimization model; wherein the constraints include vehicle state constraints, time constraints, power constraints, equipment constraints, and power grid constraints.

[0027] In this embodiment, the intelligent scheduling control module achieves safe and efficient scheduling of vehicle-grid collaboration by setting constraints in the multi-objective optimization model; wherein the constraints include vehicle status constraints, time constraints, power constraints, equipment constraints and grid constraints.

[0028] The vehicle status constraints may be battery safety boundary constraints, charge and discharge power constraints, and battery temperature constraints. Battery safety boundary constraints may prevent the impact of over-discharge or over-charging on battery life. Charge and discharge power constraints may be dynamically adjusted according to the battery health status. Battery temperature constraints may prevent high temperatures from accelerating battery aging. Time constraints may be user travel plan constraints, for example, the charging completion time must be no later than the user's planned travel time minus the reserved buffer time. Power constraints may be constraints on reaching the target SOC and emergency power reserve constraints. Equipment constraints may be constraints on the upper power limit of the charging pile. Grid constraints may be node voltage constraints and line capacity constraints.

[0029] S140: Solve the multi-objective optimization model according to the multi-objective optimization algorithm, generate an optimal charging and discharging strategy that meets the constraints, and send it to the V2G charging pile for execution.

[0030] In this embodiment, the intelligent scheduling control module has a built-in multi-objective optimization algorithm, solves the multi-objective optimization model according to the multi-objective optimization algorithm, generates an optimal charging and discharging strategy that meets the constraints, and sends it to the V2G charging pile for execution.

[0031] The core of multi-objective optimization algorithms lies in simultaneously addressing multiple conflicting objectives (such as electricity costs, battery health, and grid stability) and finding a balance through mathematical modeling and intelligent algorithms. Common algorithms include: 1. Genetic Algorithm (GA): This simulates the natural selection process and generates a set of non-inferior solutions (Pareto optimal solutions) through iterative optimization. 2. Particle Swarm Optimization (PSO): This searches for optimal solutions through group collaboration and is suitable for continuous variable optimization. 3. NSGA-II Algorithm: Based on non-dominated sorting and crowding distance calculation, it maintains population diversity and promotes convergence, and is widely used in the field of energy management. These algorithms dynamically adjust charging and discharging strategies based on real-time data (dynamic grid supply and demand parameters, vehicle status information, and usage plan information).

[0032] Specifically, the algorithm generates charging and discharging instructions (such as power and time) based on the optimal charging and discharging strategy, and issues the instructions to the V2G charging pile for execution through the bidirectional power conversion module. The bidirectional power conversion module uses a full-bridge inverter circuit architecture. Based on this architecture, the IGBT and SiC high-performance power devices are precisely integrated. The IGBT is responsible for basic power conversion control, and the SiC helps to improve overall performance with its higher electron mobility and lower on-resistance. The combination of the two opens up the channel for AC / DC and DC / AC bidirectional energy conversion, allowing the energy to be flexibly redirected as needed. The bidirectional power conversion module significantly reduces switching loss by cleverly using the energy storage and release characteristics of inductors and capacitors. After repeated testing and optimization, the conversion efficiency exceeds 98%, achieving high-efficiency energy conversion.

[0033] In an embodiment, after step S140, further comprising: during the peak period of power grid load, obtaining vehicle terminals satisfying the constraint condition according to the optimal charging and discharging strategy; and screening the vehicle terminals satisfying the constraint condition according to the vehicle state information, and preferentially scheduling vehicle terminals with high SOC and high SOH to discharge to the power grid.

[0034] In this embodiment, during the peak period of power grid load, vehicle terminals satisfying the constraint condition are obtained according to the optimal charging and discharging strategy; and the vehicle terminals satisfying the constraint condition are screened according to the vehicle state information, and 100 vehicle terminals with high SOC (SOC>80%), high SOH (SOH>90%) and no temporary travel plan are preferentially scheduled to discharge to the power grid (such as 5 degrees per vehicle, a total of 500 degrees), ensuring the total discharge amount while reserving ≥30% SOC safety bottom line for each vehicle.

[0035] In an embodiment, after step S140, during the low price valley period, vehicle terminals satisfying the constraint condition are obtained according to the optimal charging and discharging strategy; and the vehicle terminals satisfying the constraint condition are screened according to the vehicle state information, and vehicle terminals with SOC less than a preset SOC threshold are preferentially scheduled to access the V2G charging pile for charging; and the remaining vehicle terminals with SOC greater than or equal to the preset SOC threshold are delayed to perform charging in the non-peak power consumption interval within the low price valley period according to the use plan information and the power grid load prediction.

[0036] In this embodiment, during the low price period, the vehicle terminal satisfying the constraint condition is obtained according to the optimal charging and discharging strategy; the vehicle terminal satisfying the constraint condition is screened according to the vehicle state information, and the vehicle terminal with SOC less than a preset SOC threshold (the SOC threshold can be set to 50%) is preferentially scheduled to access the V2G charging pile for charging; for the remaining vehicle terminals with SOC greater than or equal to the preset SOC threshold, the charging is delayed to the non-peak electricity consumption interval within the low price period according to the use plan information and the power grid load prediction.

[0037] For example, 200 vehicles with SOC < 50% are preferentially scheduled to charge in the 23:00-3:00 low price period (to avoid the possible load peak of the charging pile in the late night); the remaining 100 vehicles with SOC ≥ 50% are delayed to charge in the 4:00-7:00 period, to balance the load of the charging pile.

[0038] In an embodiment, after step S140, the voltage conditions of each single battery in the battery pack are detected to obtain real-time voltage detection data; whether the single voltage difference exceeds a preset voltage difference threshold is determined according to the real-time voltage detection data; wherein the single voltage difference is the voltage difference between any two single batteries; if the single voltage difference exceeds the voltage difference threshold, a compensation mechanism is started to adjust the voltage of the abnormal single battery.

[0039] In this embodiment, the battery health management module captures the subtle changes inside the battery in real time, accurately estimates the remaining capacity of the battery by means of the data collected by the high-precision sensor, controls the error in a very small range, and accurately judges the health status of the battery, to provide a key basis for the subsequent battery management strategy.

[0040] The battery health management module detects the voltage conditions of each single battery in the battery pack to obtain real-time voltage detection data; whether the single voltage difference exceeds a preset voltage difference threshold (which can be customized according to actual application) is determined according to the real-time voltage detection data; wherein the single voltage difference is the voltage difference between any two single batteries; if the single voltage difference exceeds the voltage difference threshold, a compensation mechanism is started to adjust the voltage of the abnormal single battery; for example, the battery pack includes single battery A (voltage 3.8V, too high) and single battery B (voltage 3.1V, too low), the switch S1 is closed, the capacitor C is connected to the single battery A and charged to 3.8V; then the switch S1 is opened and the switch S2 is closed, the capacitor C is connected to the single battery B and releases energy to it until the voltages of the two are close to balance (e.g., both are 3.45V).

[0041] In an embodiment, after determining whether the single-cell voltage difference exceeds the preset voltage difference threshold based on the real-time voltage detection data, the method further comprises: if the single-cell voltage difference exceeds the voltage difference threshold, collecting multi-dimensional state parameters of the battery pack, the multi-dimensional state parameters at least including temperature field distribution data and electrical performance parameters; analyzing the gas production characteristics inside the battery based on the multi-dimensional state parameters through a preset physical model to obtain a gas production characteristic analysis result; determining a battery thermal runaway risk level based on the temperature field distribution data and the gas production characteristic analysis result; and triggering a pre-warning response mechanism within a preset time window before the thermal runaway occurs when the risk level reaches a preset risk threshold.

[0042] In the embodiment, if the single-cell voltage difference exceeds the voltage difference threshold, the battery health management module collects multi-dimensional state parameters of the battery pack, the multi-dimensional state parameters at least including temperature field distribution data and electrical performance parameters; analyzes the gas production characteristics inside the battery based on the multi-dimensional state parameters through a preset physical model to obtain a gas production characteristic analysis result; determines a battery thermal runaway risk level based on the temperature field distribution data and the gas production characteristic analysis result; and sends a protection signal to the safety protection module when the risk level reaches a preset risk threshold, so that the safety protection module triggers a pre-warning response mechanism within a preset time window (15 minutes) before the thermal runaway occurs.

[0043] Specifically, the safety protection module is composed of a hierarchical protection system and a physical protection architecture. The hierarchical linkage protection system: at the hardware level, high-sensitivity Hall current sensors and optical fiber voltage sensors are deployed to monitor circuit parameters in real time with nanosecond-level response speed, and when the current exceeds the rated value, the fast fuse and solid-state relay linkage can be triggered within microseconds to instantly cut off the main circuit and block the overcurrent hazard; at the software level, the state machine-based fault diagnosis algorithm continuously analyzes the device operating state data, which can complete fault feature recognition and positioning within 10 milliseconds, and according to the fault level, a graded response is started to trigger an audible and light warning for slight abnormalities, and for serious faults, the charging pile stops working and uploads fault logs to the operation and maintenance platform to ensure system safety closed loop; the physical protection reinforcement architecture: IP67 high protection level design is adopted to adapt to complex outdoor environments; in terms of electrical safety, 1000VDC / AC voltage resistance performance is realized through strict material selection and process design, and the measured insulation resistance is above 100MΩ.

[0044] In an embodiment, after step S140, if it is detected that the user changes the plan temporarily, the corresponding target vehicle is controlled to skip the discharging plan, and it is determined whether the power of the target vehicle before use meets the use demand; if the power of the target vehicle before use cannot meet the use demand, a high-power charging pile is preferentially dispatched to charge the target vehicle.

[0045] In the embodiment, the user can customize the travel plan on the human-computer interface, the system monitors the plan change in real time, if it is detected that the user uses the plan to change temporarily, the corresponding target vehicle skips the original discharge plan, and it is judged whether the power of the target vehicle before use meets the use demand; if the power of the target vehicle before use can meet the use demand (i.e. in the normal charging state, the power of the target vehicle meets the use demand), the vehicle is kept on standby; if the power of the target vehicle before use cannot meet the use demand, the high-power charging pile is preferentially dispatched to charge the target vehicle.

[0046] The user interaction interface module constructs an intelligent interaction system of "double-end cooperation + technology empowerment", and comprehensively improves the user experience: specifically, through the deep cooperation of the vehicle-mounted HMI human-computer interface and the mobile terminal APP, a seamless interactive experience is created; based on the historical charging data of the user (including charging time preference, common route energy consumption, battery attenuation trend, etc.), combined with real-time electricity price, power grid dynamic supply and demand parameters and vehicle state information, intelligent algorithm is used to customize personalized charging strategy for each user; for example, for high-frequency short-distance travel users, the valley electricity period fast charging scheme is preferentially recommended; for long-distance travel demand users, a phased power supplement strategy is planned to reduce the cost while ensuring the safety of the cruising range.

[0047] The user interaction interface module supports AR real scene navigation, after the user starts the APP camera, the charging pile navigation guide superimposed in the real scene can be obtained in real time, the route to the charging pile, the specific operation step key information of the charging pile are clearly presented, and the user is guided to complete the charging operation, at the same time, the blockchain storage technology is introduced to encrypt and store each charging record in the distributed ledger, so that the transaction information cannot be tampered; further, the user interaction interface module controls the interface operation response time to be less than 500ms through the lightweight interface architecture design and edge computing technology; 12 kinds of mainstream languages such as Chinese, English and Japanese can be switched in real time, which meets the use demand of domestic and foreign users and helps the international expansion of charging service.

[0048] In summary, the embodiment of the application solves the multi-objective optimization model according to the multi-objective optimization algorithm, generates the optimal charging and discharging strategy meeting the constraint condition, and issues it to the V2G charging pile for execution, which can significantly improve the peak and valley load distribution of the power grid, improve the renewable energy consumption capacity, reduce the user charging cost and prolong the battery service life, while ensuring the stable operation of the power distribution network, realizing the efficient response and safe controllability of the vehicle-network collaborative scheduling, improving the energy management efficiency while considering the two-way demand of the power grid and the user.

[0049] Figure 2 The schematic block diagram of the control device based on the charging pile V2G provided by the embodiment of the application is shown in the figure. Figure 2Corresponding to the above charging pile V2G control method, the application also provides a charging pile V2G control device. The device is configured in a charging pile V2G control system, and the charging pile V2G control system includes a bidirectional power conversion module, an intelligent scheduling control module, a communication protocol adaptation module, a battery health management module, a user interaction interface module, and a safety protection module. The bidirectional power conversion module is used to achieve AC / DC and DC / AC bidirectional energy conversion to realize vehicle-to-grid energy interaction. The intelligent scheduling control module is used to dynamically adjust the charging and discharging strategy by means of a multi-objective optimization algorithm and coordinate the operation of each module. The communication protocol adaptation module is used to compatible different communication protocols to realize data interaction and instruction transmission of the power grid, vehicles and users. The battery health management module is used to estimate SOC (remaining capacity) and SOH (health status) in real time and monitor the battery state. The user interaction interface module is used to provide charging state display, parameter setting and man-machine interaction function. The safety protection module is used to implement overvoltage and overcurrent protection, leakage detection and thermal runaway early warning to ensure safe operation of the system. Specifically, please refer to Figure 2 The charging pile V2G control device 700 includes: An acquisition unit 701 is configured to acquire power grid dynamic supply and demand parameters, vehicle state information and use plan information in real time. A construction unit 702 is configured to construct a multi-objective optimization model according to the power grid dynamic supply and demand parameters, the vehicle state information and the use plan information. A setting unit 703 is configured to set constraint conditions in the multi-objective optimization model. The constraint conditions include vehicle state constraints, time constraints, power constraints, device constraints and power grid constraints. A generation unit 704 is configured to solve the multi-objective optimization model according to a multi-objective optimization algorithm, generate an optimal charging and discharging strategy that meets the constraint conditions, and issue the optimal charging and discharging strategy to the V2G charging pile for execution.

[0050] In some embodiments, after the step of executing the multi-objective optimization algorithm to solve the multi-objective optimization model, generating an optimal charging and discharging strategy that meets the constraint conditions, and issuing the optimal charging and discharging strategy to the V2G charging pile for execution, the generation unit 704 is further configured to: During the peak load period of the power grid, a vehicle terminal that meets the constraint conditions is obtained according to the optimal charging and discharging strategy. The vehicle terminal that meets the constraint conditions is screened according to the vehicle state information, and the vehicle terminal with high SOC and high SOH is preferentially scheduled to discharge to the power grid.

[0051] In some embodiments, after the step of executing the multi-objective optimization algorithm to solve the multi-objective optimization model, generating an optimal charging and discharging strategy that meets the constraint conditions, and issuing the optimal charging and discharging strategy to the V2G charging pile for execution, the generation unit 704 is further configured to: In a low electricity price period, a vehicle terminal satisfying the constraint condition is obtained according to the optimal charging and discharging strategy; the vehicle terminal satisfying the constraint condition is screened according to the vehicle state information, and the vehicle terminal with an SOC less than a preset SOC threshold is preferentially scheduled to access a V2G charging pile for charging; and for the remaining vehicle terminals with an SOC greater than or equal to the preset SOC threshold, charging is performed in a non-peak electricity consumption interval within the low electricity price period according to the use plan information and the power grid load prediction.

[0052] In some embodiments, after the generating unit 704 performs solving the multi-objective optimization model according to the multi-objective optimization algorithm, generates the optimal charging and discharging strategy satisfying the constraint condition, and delivers the optimal charging and discharging strategy to the V2G charging pile for execution, the generating unit 704 is further configured to: detect voltages of the single batteries in the battery pack to obtain real-time voltage detection data; determine whether a single battery voltage difference exceeds a preset voltage difference threshold according to the real-time voltage detection data, wherein the single battery voltage difference is a voltage difference between any two single batteries; and if the single battery voltage difference exceeds the voltage difference threshold, start a compensation mechanism to adjust the voltage of the abnormal single battery.

[0053] In some embodiments, after the generating unit 704 performs determining whether a single battery voltage difference exceeds a preset voltage difference threshold according to the real-time voltage detection data, the generating unit 704 is further configured to: if the single battery voltage difference exceeds the voltage difference threshold, collect multi-dimensional state parameters of the battery pack, wherein the multi-dimensional state parameters at least include temperature field distribution data and electrical performance parameters; analyze the gas production characteristics inside the battery based on the multi-dimensional state parameters through a preset physical model to obtain a gas production characteristic analysis result; determine a battery thermal runaway risk level according to the temperature field distribution data and the gas production characteristic analysis result; and when the risk level reaches a preset risk threshold, trigger a pre-warning response mechanism within a preset time window before thermal runaway occurs.

[0054] In some embodiments, after the generating unit 704 performs solving the multi-objective optimization model according to the multi-objective optimization algorithm, generates the optimal charging and discharging strategy satisfying the constraint condition, and delivers the optimal charging and discharging strategy to the V2G charging pile for execution, the generating unit 704 is further configured to: if it is detected that the user use plan is temporarily changed, control the corresponding target vehicle to skip the discharging plan, and determine whether the power of the target vehicle before use satisfies the use demand; if the power of the target vehicle before use cannot satisfy the use demand, preferentially schedule a high-power charging pile to charge the target vehicle.

[0055] In some embodiments, when the collecting unit 701 performs real-time collection of power grid dynamic supply and demand parameters, vehicle state information, and use plan information, the collecting unit 701 is specifically configured to: Collect initial power grid dynamic supply and demand parameters, initial vehicle state information and initial use plan information in real time; according to the preset mass protocol conversion rule, the initial power grid dynamic supply and demand parameters, the initial vehicle state information and the initial use plan information are converted into power grid dynamic supply and demand parameters, vehicle state information and use plan information that can be recognized by the system.

[0056] It should be noted that the specific implementation process of the above-mentioned control device based on charging pile V2G and each unit can be clearly understood by those skilled in the art, which can be referred to the corresponding description in the foregoing method embodiment. For the convenience and brevity of description, it will not be repeated here.

[0057] The above-mentioned control device based on charging pile V2G can be realized in the form of a computer program, which can run on an electronic device as shown in the figure. Figure 3

[0058] Please refer to Figure 3 , Figure 3 is a schematic block diagram of an electronic device provided by an embodiment of the application. The electronic device 800 can be a terminal or a server, wherein the terminal can be an electronic device with communication function. The server can be a stand-alone server or a server cluster composed of multiple servers.

[0059] Refer to Figure 3 , the electronic device 800 includes a processor 802, a memory and a network interface 805 connected through a system bus 801, wherein the memory can include a non-volatile storage medium 803 and an internal memory 804.

[0060] The non-volatile storage medium 803 can store an operating system 8031 and a computer program 8032. The computer program 8032 includes program instructions which, when executed, can cause the processor 802 to perform a control method based on charging pile V2G.

[0061] The processor 802 is configured to provide computing and control capabilities to support the operation of the entire electronic device 800.

[0062] The internal memory 804 provides an environment for the running of the computer program 8032 in the non-volatile storage medium 803, which, when executed by the processor 802, can cause the processor 802 to perform a control method based on charging pile V2G.

[0063] The network interface 805 is configured to perform network communication with other devices. Those skilled in the art can understand that Figure 3 ​The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the electronic device 800 to which the scheme of the present application is applied. The specific electronic device 800 can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0064] The processor 802 is configured to run the computer program 8032 stored in the memory to implement the following steps: Real-time collection of power grid dynamic supply and demand parameters, vehicle state information and use plan information; construction of a multi-objective optimization model according to the power grid dynamic supply and demand parameters, the vehicle state information and the use plan information; setting of constraint conditions in the multi-objective optimization model; wherein the constraint conditions include vehicle state constraints, time constraints, power constraints, device constraints and power grid constraints; solving the multi-objective optimization model according to a multi-objective optimization algorithm, generating an optimal charging and discharging strategy that satisfies the constraint conditions, and issuing it to a V2G charging pile for execution.

[0065] In some embodiments, after the processor 802 implements the step of solving the multi-objective optimization model according to a multi-objective optimization algorithm, generating an optimal charging and discharging strategy that satisfies the constraint conditions, and issuing it to a V2G charging pile for execution, it further implements the following steps: During a power grid load peak period, obtaining vehicle terminals that satisfy the constraint conditions according to the optimal charging and discharging strategy; screening the vehicle terminals that satisfy the constraint conditions according to the vehicle state information, and preferentially scheduling vehicle terminals with high SOC and high SOH to discharge to the power grid.

[0066] In some embodiments, after the processor 802 implements the step of solving the multi-objective optimization model according to a multi-objective optimization algorithm, generating an optimal charging and discharging strategy that satisfies the constraint conditions, and issuing it to a V2G charging pile for execution, it further implements the following steps: During a power price trough period, obtaining vehicle terminals that satisfy the constraint conditions according to the optimal charging and discharging strategy; screening the vehicle terminals that satisfy the constraint conditions according to the vehicle state information, and preferentially scheduling vehicle terminals with SOC less than a preset SOC threshold to access a V2G charging pile for charging; for the remaining vehicle terminals with SOC greater than or equal to the preset SOC threshold, delaying charging to a non-peak power consumption interval within the trough period according to the use plan information and power grid load prediction.

[0067] In some embodiments, after the processor 802 implements the step of solving the multi-objective optimization model according to a multi-objective optimization algorithm, generating an optimal charging and discharging strategy that satisfies the constraint conditions, and issuing it to a V2G charging pile for execution, it further implements the following steps: The voltage of each single battery in the battery pack is detected to obtain real-time voltage detection data; whether the voltage difference between any two single batteries exceeds a preset voltage difference threshold is determined according to the real-time voltage detection data; wherein the voltage difference between any two single batteries is the single battery voltage difference; if the single battery voltage difference exceeds the voltage difference threshold, a compensation mechanism is started to adjust the voltage of the abnormal single battery.

[0068] In some embodiments, the processor 802, after implementing the step of determining whether the single battery voltage difference exceeds the preset voltage difference threshold according to the real-time voltage detection data, further implements the following steps: If the single battery voltage difference exceeds the voltage difference threshold, the multi-dimensional state parameters of the battery pack are collected, and the multi-dimensional state parameters at least include temperature field distribution data and electrical performance parameters; based on the multi-dimensional state parameters, the gas production characteristics inside the battery are analyzed through a preset physical model to obtain a gas production characteristic analysis result; according to the temperature field distribution data and the gas production characteristic analysis result, the risk level of battery thermal runaway is determined; when the risk level reaches a preset risk threshold, a pre-warning response mechanism is triggered within a preset time window before thermal runaway occurs.

[0069] In some embodiments, the processor 802, after implementing the step of solving the multi-objective optimization model according to the multi-objective optimization algorithm to generate an optimal charging and discharging strategy that satisfies the constraint conditions and issuing the optimal charging and discharging strategy to the V2G charging pile for execution, further implements the following steps: If it is detected that the user's use plan is temporarily changed, the corresponding target vehicle is controlled to skip the discharging plan, and whether the power of the target vehicle before use meets the use demand is determined; if the power of the target vehicle before use cannot meet the use demand, a high-power charging pile is preferentially dispatched to charge the target vehicle.

[0070] In some embodiments, the processor 802, when implementing the step of collecting real-time power grid dynamic supply and demand parameters, vehicle state information and use plan information, specifically implements the following steps: The initial power grid dynamic supply and demand parameters, the initial vehicle state information and the initial use plan information are collected in real time; the initial power grid dynamic supply and demand parameters, the initial vehicle state information and the initial use plan information are converted into power grid dynamic supply and demand parameters, vehicle state information and use plan information that can be recognized by the system according to a preset mass protocol conversion rule.

[0071] It should be understood that in the embodiment of the present invention, the processor 802 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0072] Those skilled in the art will appreciate that all or part of the steps in the method of the above-described embodiment can be implemented by instructing the relevant hardware through a computer program. The computer program includes program instructions, which can be stored in a storage medium that is computer-readable. The program instructions are executed by at least one processor in the computer system to implement the steps in the method of the above-described embodiment.

[0073] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor performs the following steps: Real-time collection of grid dynamic supply and demand parameters, vehicle status information, and usage plan information; building a multi-objective optimization model based on the grid dynamic supply and demand parameters, the vehicle status information, and the usage plan information; setting constraints in the multi-objective optimization model; wherein the constraints include vehicle status constraints, time constraints, power constraints, equipment constraints, and grid constraints; solving the multi-objective optimization model according to a multi-objective optimization algorithm, generating an optimal charging and discharging strategy that meets the constraints, and sending the strategy to the V2G charging pile for execution.

[0074] In one embodiment, after the processor executes the program instructions to solve the multi-objective optimization model according to the multi-objective optimization algorithm, generates the optimal charging and discharging strategy that satisfies the constraints, and sends it to the V2G charging pile for execution, it further implements the following steps: During the peak load period of the power grid, vehicle terminals that meet the constraints are obtained according to the optimal charging and discharging strategy; vehicle terminals that meet the constraints are screened according to the vehicle status information, and vehicle terminals with high SOC and high SOH are preferentially scheduled to discharge to the power grid.

[0075] In an embodiment, after the processor executes the program instructions to implement solving the multi-objective optimization model according to the multi-objective optimization algorithm, generating the optimal charging and discharging strategy satisfying the constraint condition, and issuing the optimal charging and discharging strategy to the V2G charging pile for execution, the processor further implements the following steps: In the low valley period of electricity price, the vehicle terminal satisfying the constraint condition is obtained according to the optimal charging and discharging strategy; the vehicle terminal satisfying the constraint condition is screened according to the vehicle state information, and the vehicle terminal with SOC less than a preset SOC threshold is preferentially scheduled to access the V2G charging pile for charging; for the remaining vehicle terminals with SOC greater than or equal to the preset SOC threshold, the charging is delayed to a non-peak electricity consumption interval within the low valley period according to the use plan information and the power grid load prediction.

[0076] In an embodiment, after the processor executes the program instructions to implement solving the multi-objective optimization model according to the multi-objective optimization algorithm, generating the optimal charging and discharging strategy satisfying the constraint condition, and issuing the optimal charging and discharging strategy to the V2G charging pile for execution, the processor further implements the following steps: The voltage conditions of each single battery in the battery pack are detected to obtain real-time voltage detection data; it is determined whether the single voltage difference exceeds a preset voltage difference threshold according to the real-time voltage detection data; wherein the single voltage difference is the voltage difference between any two single batteries; if the single voltage difference exceeds the voltage difference threshold, a compensation mechanism is started to adjust the voltage of the abnormal single battery.

[0077] In an embodiment, after the processor executes the program instructions to implement determining whether the single voltage difference exceeds the preset voltage difference threshold according to the real-time voltage detection data, the processor further implements the following steps: If the single voltage difference exceeds the voltage difference threshold, the multi-dimensional state parameters of the battery pack are collected, and the multi-dimensional state parameters at least include temperature field distribution data and electrical performance parameters; based on the multi-dimensional state parameters, the gas production characteristics inside the battery are analyzed through a preset physical model to obtain a gas production characteristic analysis result; the risk level of battery thermal runaway is determined according to the temperature field distribution data and the gas production characteristic analysis result; when the risk level reaches a preset risk threshold, a pre-warning response mechanism is triggered within a preset time window before the thermal runaway occurs.

[0078] In an embodiment, after the processor executes the program instructions to implement solving the multi-objective optimization model according to the multi-objective optimization algorithm, generating the optimal charging and discharging strategy satisfying the constraint condition, and issuing the optimal charging and discharging strategy to the V2G charging pile for execution, the processor further implements the following steps: If it is detected that the user uses a temporary change plan, the corresponding target vehicle skips the discharge plan, and it is judged whether the power of the target vehicle before use meets the use demand; if the power of the target vehicle before use cannot meet the use demand, the high-power charging pile is preferentially dispatched to charge the target vehicle.

[0079] In an embodiment, when the processor executes the program instructions to realize the step of collecting power grid dynamic supply and demand parameters, vehicle state information and use plan information in real time, the following steps are realized: The initial power grid dynamic supply and demand parameters, the initial vehicle state information and the initial use plan information are collected in real time, and the initial power grid dynamic supply and demand parameters, the initial vehicle state information and the initial use plan information are converted into power grid dynamic supply and demand parameters, vehicle state information and use plan information that can be recognized by the system according to a preset mass protocol conversion rule.

[0080] The storage medium can be a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk, and various computer readable storage media that can store program codes.

[0081] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0082] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and actual implementation can have another division manner. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0083] The steps in the method of the embodiments of the present application can be adjusted, combined and deleted in sequence according to actual needs. The units in the device of the embodiments of the present application can be combined, divided and deleted according to actual needs. In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0084] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a storage medium. Based on such an understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art, or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing an electronic device (which can be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application.

[0085] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A control method based on charging pile V2G, characterized in that: The method comprises: Real-time collection of dynamic grid supply and demand parameters, vehicle status information, and usage plan information; Constructing a multi-objective optimization model based on the dynamic supply and demand parameters of the power grid, the vehicle status information and the usage plan information; Setting constraints in the multi-objective optimization model; wherein the constraints include vehicle state constraints, time constraints, power constraints, equipment constraints, and power grid constraints; The multi-objective optimization model is solved according to a multi-objective optimization algorithm to generate an optimal charging and discharging strategy that meets the constraints, and the strategy is sent to the V2G charging pile for execution.

2. The control method based on charging pile V2G according to claim 1, characterized in that: After solving the multi-objective optimization model according to the multi-objective optimization algorithm, generating an optimal charging and discharging strategy that satisfies the constraint conditions, and sending the strategy to the V2G charging pile for execution, the method further includes: During the peak load period of the power grid, obtaining a vehicle terminal that meets the constraint conditions according to the optimal charging and discharging strategy; Vehicle terminals that meet the constraint conditions are screened according to the vehicle status information, and vehicle terminals with high SOC and high SOH are preferentially scheduled to discharge to the power grid.

3. The control method based on charging pile V2G according to claim 1, characterized in that: After solving the multi-objective optimization model according to the multi-objective optimization algorithm, generating an optimal charging and discharging strategy that satisfies the constraint conditions, and sending the strategy to the V2G charging pile for execution, the method further includes: During the low electricity price period, obtaining a vehicle terminal that meets the constraint conditions according to the optimal charging and discharging strategy; Filtering vehicle terminals that meet the constraint conditions according to the vehicle status information, and preferentially scheduling vehicle terminals whose SOC is less than a preset SOC threshold to connect to the V2G charging pile for charging; For the remaining vehicle terminals whose SOC is greater than or equal to the preset SOC threshold, charging is postponed to the off-peak power consumption period during the off-peak period based on the usage plan information and grid load forecast.

4. The control method based on charging pile V2G according to claim 1, characterized in that: After solving the multi-objective optimization model according to the multi-objective optimization algorithm, generating an optimal charging and discharging strategy that satisfies the constraint conditions, and sending the strategy to the V2G charging pile for execution, the method further includes: Detect the voltage of each single cell in the battery pack and obtain real-time voltage detection data; Determining whether a cell voltage difference exceeds a preset voltage difference threshold value based on the real-time voltage detection data; wherein the cell voltage difference is the voltage difference between any two cells; If the voltage difference of a single cell exceeds the voltage difference threshold, the compensation mechanism is activated to adjust the voltage of the abnormal single cell.

5. The control method based on charging pile V2G according to claim 4, characterized in that: After determining whether a cell voltage difference exceeds a preset voltage difference threshold value according to the real-time voltage detection data, the method further includes: If the voltage difference of any cell exceeds the voltage difference threshold, then collecting multi-dimensional state parameters of the battery pack, the multi-dimensional state parameters at least including temperature field distribution data and electrical performance parameters; Based on the multi-dimensional state parameters, analyzing the gas generation characteristics inside the battery through a preset physical model to obtain a gas generation characteristic analysis result; Determining a battery thermal runaway risk level based on the temperature field distribution data and the gas production characteristic analysis results; When the risk level reaches a preset risk threshold, an early warning response mechanism is triggered within a preset time window before thermal runaway occurs.

6. The control method based on charging pile V2G according to claim 1, characterized in that: After solving the multi-objective optimization model according to the multi-objective optimization algorithm, generating an optimal charging and discharging strategy that satisfies the constraint conditions, and sending the strategy to the V2G charging pile for execution, the method further includes: If a temporary change in the user's usage plan is detected, the corresponding target vehicle is controlled to skip the discharge plan and determine whether the target vehicle's power level meets the vehicle usage requirements before use; If the target vehicle's power level cannot meet the demand before use, a high-power charging pile will be dispatched first to charge the target vehicle.

7. The control method based on charging pile V2G according to claim 1, characterized in that: The real-time collection of dynamic grid supply and demand parameters, vehicle status information, and usage plan information includes: Real-time collection of initial grid dynamic supply and demand parameters, initial vehicle status information, and initial usage plan information; According to the preset massive protocol conversion rules, the initial power grid dynamic supply and demand parameters, the initial vehicle status information and the initial usage plan information are converted into power grid dynamic supply and demand parameters, vehicle status information and usage plan information that can be recognized by the system.

8. A control device based on charging pile V2G, characterized in that: The device comprises: The acquisition unit is used to collect dynamic grid supply and demand parameters, vehicle status information, and usage plan information in real time; A construction unit, configured to construct a multi-objective optimization model based on the dynamic supply and demand parameters of the power grid, the vehicle status information, and the usage plan information; a setting unit, configured to set constraints in the multi-objective optimization model; wherein the constraints include vehicle state constraints, time constraints, power constraints, equipment constraints, and power grid constraints; A generation unit is used to solve the multi-objective optimization model according to a multi-objective optimization algorithm, generate an optimal charging and discharging strategy that meets the constraints, and send it to the V2G charging pile for execution.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the charging pile V2G-based control method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by the processor, the processor executes the control method based on the charging pile V2G according to any one of claims 1 to 7.

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