19941results about "Charging stations" patented technology

To provide a system and an apparatus for implementing a mobile energy system.SOLUTION: In an aspect, a mobile energy system includes a cabinet having an interior space defined by an outer surface of the cabinet, and the interior space can include a battery storage area configured to receive a battery energy storage system having at least 2 kWh of storage capacity. The mobile energy system can also include an item storage area configured to receive and store one or more other items, a connector configured to connect the battery energy storage system located in the battery storage area to an external power source, and a charging cable configured to connect the battery energy storage system to a charging port of an electric vehicle.SELECTED DRAWING: Figure 4

The invention relates to the technical field of unmanned aerial vehicles, and discloses an intelligent charging and endurance optimization method for an unmanned aerial vehicle nest, and the method comprises the steps: collecting flight, battery and environment data through an unmanned aerial vehicle, transmitting the data to a nest, and precisely evaluating the remaining endurance of the unmanned aerial vehicle through the nest in combination with historical tasks and geographic information; performing dynamic task planning based on reinforcement learning, and generating an optimal charging strategy by using a dynamic planning algorithm; by means of a multi-mode sensor and a self-adaptive charging control model, intelligent regulation and control of the charging process are achieved; and through solar auxiliary power supply and multi-machine cooperative charging, the endurance is enhanced. The problems of low charging efficiency, poor endurance management, insufficient system reliability and the like in a traditional method are effectively solved, the utilization rate of nest charging resources is remarkably improved, the service life of a battery is prolonged, the endurance of the unmanned aerial vehicle and the system stability are enhanced, and the method has important significance in promoting efficient application of the unmanned aerial vehicle in multiple fields.

The invention discloses a charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method and system, and relates to the technical field of liquid cooling control. The charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method comprises the steps that S1, liquid cooling monitoring data are periodically collected, and the liquid cooling monitoring data are preprocessed; s2, judging whether an abnormal period exists or not, and evaluating whether a cooling liquid leakage trend exists or not in combination with real-time liquid cooling monitoring data; s3, performing coolant leakage risk grade evaluation, judging the leakage risk grade in the current abnormal period, and executing a corresponding leakage prevention strategy; and S4, in the execution process of the anti-leakage strategy, the running state of the circulating pump is evaluated, and whether the pump obstacle prevention and control strategy is triggered or not is judged. The problem that thermal runaway is caused by the fact that cooling abnormity is not intervened in time due to the fact that leakage of trace cooling liquid in an existing liquid cooling system is difficult to recognize in time and pump obstacles are difficult to pre-judge accurately is solved.

The invention discloses a charging method and system based on battery dynamic health state evaluation and learning prediction, belongs to the technical field of electric vehicle charging, and aims to solve the technical problem of how to break through a traditional BMS information barrier, realize deep perception and accurate prediction of a microcosmic dynamic health state in a battery, and improve the charging efficiency. According to the technical scheme, when the electric vehicle is connected to a charging pile, the charging pile establishes a communication link with a BMS (battery management system) of the vehicle through a standard protocol; a current or voltage disturbance signal with a specific frequency spectrum is injected into the battery, a response signal of the battery is measured synchronously, and dynamic health state parameters of the battery of the electric vehicle are collected in real time; and analyzing the dynamic health state parameters of the battery of the electric vehicle by using a pre-trained deep learning model, predicting the health state evolution trend of the battery, and generating a multi-target charging parameter optimization scheme.

Various electric energy supply vehicles and electric energy supply vehicle control system; a to-be-replaced electric vehicle and an electric vehicle battery replacement control system; a robot, a robot driving system, a carrying robot and a carrying robot control system; a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of charging and replacing cabinets, a charging and replacing cabinet control system, a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of to-be-replaced electric vehicles with different structures, a constructed electric vehicle finding mode, a plurality of electric vehicles to be subjected to battery replacement, a construction battery swapping station link is omitted, one electric energy supply vehicle can provide replenishment for a plurality of electric vehicles, an electric energy supply vehicle is put according to the amount of electric vehicles, invalid investment is avoided, and a vehicle owner does not need to center the battery again.

The invention relates to a dynamic power regulation and control method and system for an electric vehicle charging pile, and the method comprises the following steps: S1, based on the real-time operation data of a charging pile cluster, employing a multi-source information fusion technology, collecting a power grid voltage fluctuation signal through a Hall sensor disposed at a charging pile end, and combining with the battery charge state data transmitted by a vehicle-mounted BMS, and eliminating noise interference by using an improved Kalman filtering algorithm, and realizing multi-dimensional data fusion by using a D-S evidence theory to generate a multi-source working condition feature set. The method has the advantages that the power grid voltage fluctuation signal and the battery charge state data are integrated through the multi-source information fusion technology, the improved Kalman filtering algorithm is adopted to eliminate noise interference, multi-dimensional data fusion is achieved in combination with the D-S evidence theory, the integrity and reliability of working condition feature extraction are remarkably improved, and the working condition feature extraction efficiency is improved. And the LSTM-ARIMA hybrid prediction model is utilized to synchronously process the nonlinear features and the periodic rules.

The invention relates to the technical field of vehicle charging station operation and maintenance management, and particularly discloses a new energy vehicle charging station operation and maintenance management system and method based on the Internet of Things, and the method comprises the steps: collecting high-frequency harmonic characteristic data in a charging process in real time, and generating harmonic-impedance spectrum combined monitoring data; extracting dynamic attenuation gradient data of the line characteristic impedance; when the dynamic attenuation gradient data is lower than a preset critical safety threshold value, triggering a charging mode switching instruction; the charging piles are controlled to be switched into a charging mode, the phase difference parameters of the multiple charging piles are dynamically optimized, and a phase difference dynamic adjustment instruction set is generated; and finally, self-adaptive correction of the impedance compensation amount is carried out through the MEC equipment, and by self-adaptive correction of the impedance compensation amount, the system can maintain that the overall impedance is constantly greater than a critical safety threshold value, and power loss and equipment damage caused by impedance mismatch are avoided. The real-time feedback mechanism is helpful for improving the energy efficiency of the system, reducing the operation cost and ensuring the long-term reliability of the charging infrastructure.

The invention discloses a new energy commercial vehicle fast charging working condition thermal management control method and system, and relates to the technical field of new energy vehicle thermal management and fast charging control, and the method comprises the following steps: collecting the temperature, voltage and current data of each subarea of a battery in a fast charging process, forming a time synchronization sequence, and based on the sequence, obtaining a new energy commercial vehicle fast charging condition; calculating internal thermal resistance and thermal capacity parameters of the battery in real time by using an online identification algorithm, updating a basic thermal model to obtain a corrected thermal dynamic model reflecting actual thermal characteristics of the current battery, importing the corrected model into a model prediction controller, constructing a multi-objective optimization function, and performing rolling solution in a prediction time domain to obtain a prediction model; and outputting an optimal target charging current instruction and a cooling total demand, adjusting the charging power according to the target current, and combining the temperature difference distribution of each partition. Accurate prediction and partition cooperative control of the thermal state of the battery in the fast charging process are achieved, the temperature rise and the temperature difference are effectively restrained while the charging efficiency is guaranteed, the service life of the battery is prolonged, and the system safety is improved.

The invention discloses a charging pile communication method and system based on multi-protocol adaptation, and solves the problem of compatibility between a charging pile and vehicles with different communication protocols. The method comprises the following steps: acquiring charging request data of a charging pile and a vehicle, identifying a vehicle communication protocol, and performing compatibility comparison with a charging pile protocol; and if the protocol is not matched, carrying out transcoding strategy matching by using a protocol converter, constructing a protocol adaptation scheme, and realizing communication link connection between the charging pile and the vehicle. And the system monitors the charging state of the vehicle in real time, evaluates the conversion performance of the protocol adaptation scheme based on the charging state, and obtains performance data. And finally, performing optimal configuration on the parameters of the protocol converter according to the performance data. According to the method, dynamic adaptation and optimization among multiple protocols are realized, the compatibility and efficiency of charging communication are improved, the intelligent level of charging management is enhanced, the method is suitable for various charging environments, and the charging experience and the system stability are improved.

The invention provides a split type direct-current charging pile liquid cooling heat dissipation system, and belongs to the technical field of direct-current charging piles, a closed liquid cooling circulation system is constructed through equipment such as a liquid cooling circulation pump, a radiator assembly and a temperature sensor array, and active temperature control is realized by adopting a temperature pre-adjusting module as a core. The system firstly collects power and temperature data to establish a power heat effect function to describe a power-temperature relationship, and extracts temperature time sequence characteristics through a heat situation sensing model by adopting an improved time sequence convolutional network to configure a self-adaptive receptive field adjustment mechanism to capture different time scale change modes; and constructing a double-layer optimization framework in combination with a temperature control game model to solve a Nash equilibrium solution, and calculating an optimal cooling adjustment vector including advanced cooling time and cooling power parameters. And a change curve is generated through real-time monitoring and dynamic correction and is converted into a control instruction, the frequency converter is controlled to adjust the rotating speed of the circulating pump and the opening of the flow valve, and advanced adjustment before temperature change is achieved to eliminate response lag.

The invention discloses a wind-solar complementary energy storage direct current charging method and system based on liquid cooling heat dissipation, and relates to the technical field of energy storage charging. Comprising the following steps: S1, collecting wind-solar complementary energy storage data in real time, and carrying out data preprocessing; s2, converting wind-solar power generation into direct current, judging the flow direction of wind-solar power generation energy, and performing corresponding wind-solar power generation energy transmission power supply; s3, judging the limit of the output power of the charging pile, and controlling and adjusting the output of the charging pile in stages; s4, quantifying the liquid cooling flow required by heat dissipation, and performing heat source heat dissipation on the energy storage system and the charging pile; and S5, carrying out coordinated regulation and control, strategy optimization and multi-dimensional visualization on the flow direction of wind and light power generation energy, charging pile output and heat source heat dissipation. The problems that an existing charging station is difficult to deploy in remote and power transmission difficult areas, high-power charging heat dissipation and energy efficiency bottlenecks are prominent, and the efficiency of traditional air cooling and alternating current conversion is low, so that efficient, safe and green energy supplementation is difficult to achieve are solved.

The invention discloses a thermal balance control method for a liquid cooling charging module, and relates to the technical field of power electronics, and the method comprises the steps: deploying a multi-dimensional distributed temperature sensing array in a key heating region of the liquid cooling charging module; original data are acquired through the temperature acquisition module and transmitted to the temperature field reconstruction module, a three-dimensional dynamic temperature field model is constructed by using a spatial interpolation algorithm, and temperature values, temperature gradients and temperature difference distribution of all regions are output in real time; the thermal balance control module compares the model output data with a preset temperature threshold value and a temperature difference threshold value, and if the threshold value exceeding condition exists, a regulation and control instruction is sent to a partition cooling execution unit; the subarea cooling execution unit comprises three independent branches (corresponding to key areas), and each branch realizes accurate adjustment through an independent electromagnetic flow valve and a variable frequency water pump; and the steps are circulated until the temperatures of all the key areas reach the standard and the temperature difference of the adjacent areas meets the standard, so that the heat balance precision and the operation safety of the liquid cooling charging module are effectively improved.

A modular, multi-directional, smart electric vehicle (EV) charging station. In an embodiment, the EV charging station has a field-serviceable, replaceable modular construction, a fully multi-directional power routing architecture, buffered EV-to-grid and EV-to-EV charging, multi-EV charging capability, and the ability to integrate multiple modular charging stations into a networked local and / or wide-area charging system.

The invention discloses a charging pile cooling control method and system based on AI prediction reinforcement learning, and relates to the technical field of cooling control. The charging pile cooling control method and system based on AI prediction reinforcement learning comprises the following steps: S1, collecting charging heat dissipation data of a charging pile, and preprocessing the charging heat dissipation data; s2, constructing a time sequence characteristic matrix, inputting the time sequence characteristic matrix into a temperature prediction model, outputting a temperature prediction sequence of a controlled target, evaluating a thermal runaway risk in a prediction stage, and constructing a thermal risk identification sequence; s3, constructing a cooling strategy optimization model, inputting the real-time state vector into the cooling strategy optimization model, outputting an adjustment instruction, and issuing and executing the adjustment instruction; and S4, the execution deviation of the adjustment instruction is evaluated, the cooling strategy is adjusted based on the evaluation result, and a cooling strategy feedback sample is generated. The problems of energy consumption waste and cooling imbalance caused by lack of real-time prediction and self-adaptive regulation and control capabilities in the cooling control process of the existing charging pile are solved.

The invention discloses a reinforcement learning-based mobile vehicle charging and storage dynamic scheduling method and system, and solves the problems of insufficient scheduling flexibility and low peak-valley electricity price utilization rate of a fixed charging facility of an existing parking lot. A dynamic environment model is constructed, the real-time SOC of the mobile charging and storage vehicle, the position topological relation and the charging demand space-time distribution are integrated, and a deep reinforcement learning algorithm is adopted to train an intelligent body to generate a multi-dimensional collaborative optimization strategy. According to the method, a charging / discharging time sequence, a task path and energy distribution are autonomously planned, a reward function mechanism fusing dynamic path cost and energy constraint is innovatively designed, a multi-vehicle asynchronous collaborative decision framework is established, and dual targets of charging demand response efficiency and operation cost optimization are achieved. According to the method, an MCSV hardware embedded system which supports an ROS2 communication protocol and has a real-time sensor data processing capability is deployed, so that effective transition from a theoretical strategy to actual application is realized.

The invention discloses a liquid cooling charging module health state prediction system, and relates to the technical field of electrochemical detection, and the system comprises a multi-parameter collection unit which collects the pressure and flow data of a cooling liquid, and is provided with an independent collection node at each branch of a double-gun charging system, and provides basic data for monitoring; the dynamic calibration unit communicates with the acquisition unit, compensates data drift based on a temperature-vibration interference model of machine learning training, executes baseline calibration by using an idle period to update a reference value, and guarantees data accuracy; the leakage detection unit is used for calculating a data change rate after compensation, outputting an early warning according to a preset condition and positioning a leakage branch; and the health state evaluation unit fuses the early warning signal and the historical operation data to generate a prediction result. The problems that tiny leakage detection is difficult and a sensor is prone to interference can be solved, manual inspection is reduced, the maintenance cost is reduced, and the requirement of a high-power charging scene is met.

The invention relates to the technical field of charging parking space guiding control, and particularly discloses a parking space charging reservation and intelligent guiding method, device, equipment and medium, and the method comprises the steps: obtaining vehicle battery state data and a user reservation time window through an Internet of Things terminal; synchronously acquiring charging pile state data, real-time traffic flow data and power grid load information in the parking lot; fusing the obtained multi-source heterogeneous data based on a federated learning framework, constructing a dynamic charging demand prediction model, and predicting charging pile occupancy rate distribution in a preset time period by using a space-time diagram convolutional network; through the combination of the dynamic charging demand prediction model and the space-time diagram convolutional network, the occupancy rate distribution of the charging piles is accurately predicted, the scheduling strategy of the charging piles is adjusted in real time, the problem of unreasonable resource allocation is avoided, and the waiting time of a user is shortened; the application of the quantum genetic algorithm optimizes the matching process of the charging pile through global optimal solution search, thereby ensuring the balance of the power grid load and the rationality of the charging strategy.

The invention discloses a dynamic load adaptive charging pile liquid cooling control method, relates to the technical field of thermal management, and solves the problems of temperature prediction deviation, local hot spot leak detection and protective supercooling caused by a simplified thermal model based on fixed parameters and key temperature points under rapid load fluctuation. Parameterization time-varying heat supply network parallel multi-rate observation is constructed, a heat exchange coefficient and node heat capacity are identified and calibrated through recursion with physical constraints, transmission time delay is merged into an extended state in an explicit mode, short-window high-frequency sampling and local temperature field reconstruction are triggered based on residual errors, and the time-varying heat supply network parallel multi-rate observation is achieved. A control sequence is issued in parallel by adopting hierarchical rolling type control with a second-order moment uncertainty constraint; based on the scheme, the short-term temperature field prediction precision and the local hot spot identification capability are improved, and the cooling resource directional utilization efficiency and the charging system operation continuity are remarkably improved.

The invention relates to the technical field of charging station management, and particularly discloses an intelligent cooperative charging station management system and method for an electric automobile, and the system comprises the steps: integrating real-time communication and honeycomb partition management through a cooperative decision module, constructing an efficient and state-aware charging network, and carrying out the state-aware management of the charging network; the decision model can accurately adapt to the actual operation state of the target charging station; the cooperative scheduling module optimizes resource allocation by using a state matrix and an aggregation space, so that the utilization efficiency of charging station resources is remarkably improved; and the intelligent charging scheduling decision module combines reinforcement learning rapid iteration and historical data driven prior knowledge and demand prediction, so that the generation of an optimal scheduling strategy is accelerated, the excessive dependence of decision on real-time data is reduced, and the response capability and operation stability of the system for responding to dynamic demands and complex scenes are greatly enhanced, and thus, the system is more intelligent. And finally, efficient, intelligent and networked collaborative management of the electric vehicle charging service is realized.

The invention belongs to the technical field of charging pile thermoelectric control, and particularly relates to a high-power liquid cooling charging pile charging power optimization scheduling method and system. The method comprises the following steps: collecting operation state parameters of a plurality of liquid cooling charging piles; identifying a target pile body in a charging state or a standby state; calculating a theoretical energy supply upper limit based on an electrothermal model; combining the vehicle charging demand, the priority, the battery state and the power grid load to construct a power scheduling model and execute distribution; power adjustment and redistribution are carried out when cooling abnormity or connection faults are detected; and finally, a scheduling control instruction is generated and executed in real time. The system comprises an information acquisition module, a state identification module, a model calculation module, a power distribution module, a dynamic adjustment module and an instruction issuing module. According to the invention, efficient, safe and dynamic power scheduling control of the high-power liquid cooling charging pile in a multi-device parallel connection scene is realized.

The invention belongs to the field of charging pile energy recovery, and particularly relates to a charging pile system based on an immersed liquid cooling technology and waste heat recovery. A power distribution unit is arranged on the inner side wall of a shell, a liquid cooling box is arranged in the shell, the liquid cooling box is filled with cooling liquid, and a charging module is arranged in the liquid cooling box; the charging module is completely immersed in cooling liquid in the liquid cooling box, two heat exchange assemblies are arranged on one side of the liquid cooling box and located in the shell, a water storage assembly is arranged on one side of the shell, and the liquid cooling box and the two heat exchange assemblies communicate with each other and form circulating heat exchange on the cooling liquid in the liquid cooling box. The two heat exchange assemblies and the water storage assembly communicate with each other, and circulating heat exchange of water in the heat preservation water tank is formed. Through the arrangement of the cooling liquid purity detection module and other assemblies, real-time purity detection can be conducted on cooling liquid flowing back into the liquid cooling box, and when the first plate heat exchanger breaks down, the second plate heat exchanger is switched to guarantee hot water supply.

The invention, which relates to the technical field of optical storage and charging, discloses an optical storage and charging cooperative control method for a transformer area, and the method comprises the steps: carrying out the intelligent and multivariate collection and prediction through intelligent equipment, constructing a function through collected data, solving an optimal solution, selecting an optimal optical storage and charging cooperative control strategy from the optimal solution, generating a corresponding control instruction, and carrying out the transmission of the corresponding control instruction. Cooperative regulation and control of a photovoltaic power generation system, an energy storage system and a charging pile system are achieved, a feedback and correction mechanism is further established, and it is ensured that the photovoltaic storage and charging system stably operates according to a preset strategy. According to the transformer area light storage and charging cooperative control method, a light storage and charging cooperative control mechanism is established by comprehensively considering photovoltaic power generation, an energy storage system and a charging pile system, power resource distribution in a transformer area is optimized, maximum consumption of photovoltaic power generation is achieved, the light abandoning phenomenon is reduced, meanwhile, unnecessary charging and discharging loss of the energy storage system is reduced, and the power utilization rate of the transformer area is improved. And the impact of disordered charging of the charging piles on a power grid system is relieved, so that the overall operation performance of a transformer area power system is improved.

The invention discloses a PDU module and a charging device, and relates to the technical field of power distribution, the PDU module comprises a control board, PCBs, a first control group and a first conductive assembly, the PCBs are connected to the control board at intervals, the first control group comprises a plurality of first relays forming an n * n triangular matrix, and the first relays are fixed on the PCBs. The first conductive assembly respectively realizes electrical communication and cross bridging of relays in the same row and in the same column through the first conductive member, the second conductive member and the bridging busbar. According to the scheme, through the triangular matrix layout, the total number of relays is remarkably reduced on the premise that the same control function as a traditional full matrix is achieved; and meanwhile, the relays are integrated on the PCB and are uniformly controlled by the control panel, so that a large number of connectors and wire harnesses are omitted, the module volume, the material cost and the assembly cost are effectively reduced, and a flexible power distribution function is realized.

The invention belongs to the technical field of vehicle charging, and particularly relates to a double-direct-current bus star ring matrix control system and method of a charging pile and a storage medium, and the system comprises at least one charging host, each charging host is connected with at least two charging terminals, each charging terminal comprises at least one charging gun, and each charging gun is numbered; each charging host comprises a contactor matrix, a control unit and a plurality of power module groups, and each power module group is composed of a plurality of charging modules; each charging terminal is correspondingly configured with one power module group; two direct current buses; the contactor matrix comprises a ring topology contactor, a bus contactor and a parallel operation contactor; by adopting the double-direct-current bus star ring matrix topological structure and the control method, the use number of contactors is reduced, and the charging facility has flexible expansibility. The ring topology and the double direct current buses are combined, so that the system structure is more concise, and the reliability and maintainability of the system are improved.

The invention discloses a heavy truck charging health degree evaluation method based on an optimized graph convolutional network, and particularly relates to the technical field of electric power operation and maintenance. Multi-source heterogeneous operation data of the charging pile are collected, and unified time window segmentation is carried out; constructing a dynamic multi-graph structure fusing space, electrical and behavior coupling relations; extracting a node local state evolution trend and a neighbor influence factor, and generating a joint feature vector; inputting the features under multiple time windows into a multi-scale attention enhanced graph convolutional network to obtain joint potential health degree representation; calculating a health score based on the potential representation, identifying a high-risk node, and constructing a potential failure propagation path map; generating a maintenance priority table in combination with the centrality index and the degradation rate, and outputting an optimized inspection path; according to the invention, time-space correlation modeling and intelligent maintenance path planning of the health state of the charging pile are realized, and the method has the advantages of high accuracy and strong applicability.

The invention provides a dynamic power adaptive charging control system and method based on a W algorithm, and relates to the technical field of electric two-wheeled vehicle charging control. The system comprises a battery state sensing module, a charging power standard calculation module, a regulation and control model construction module, a model dynamic optimization module and a deviation early warning and regulation and control module. The battery state sensing module generates battery charging basic data; the charging power standard calculation module calculates a charging operation power standard value; the regulation and control model construction module generates an initial power regulation and control curve; the model dynamic optimization module calculates a self-adaptive charging power regulation and control curve; and the deviation early warning and regulation module realizes self-adaptive regulation of the charging power of the electric two-wheeled vehicle. According to the method, the power grid adaptability correction model is constructed and the regional differentiation parameters are introduced, so that power grid fluctuation is flexibly coped, wide-voltage stable operation is guaranteed, and the adaptability, the safety and the intelligent level of the electric two-wheeled vehicle charging system are comprehensively enhanced.

The invention discloses a liquid cooling heat dissipation control method and device for a high-power charging pile, and the method comprises the steps: obtaining the temperature data of a heating element in the charging pile and external environment parameters, building a dynamic heat load distribution model through a pre-built heat conduction algorithm according to the incidence relation between the temperature data and the charging power, and achieving the heat dissipation of the charging pile. Obtaining heat distribution characteristics; according to the heat distribution characteristics, in combination with a preset heat dissipation efficiency threshold value and a power density requirement in the liquid cooling heat dissipation control system, the cooling liquid flow and the cooling liquid circulation rate under the current working condition are calculated, and a cooling liquid distribution scheme containing a flow distribution proportion and a circulation path is determined; and if it is detected that the external environment temperature in the external environment parameters exceeds a preset threshold range, corresponding environment challenge data are matched from a working condition database, the cooling liquid circulation rate is adjusted, an auxiliary heat dissipation mechanism is activated, and an environment adaptation type heat dissipation mode is generated. The heat dissipation efficiency of the charging pile is improved, precise temperature control is achieved, and therefore safe and stable operation of the charging pile is guaranteed.

The invention discloses a power grid peak-valley adjustment method considering vehicle-network interaction and dynamic floating electricity price, and aims to improve the balance capability of a power grid load by guiding an electric vehicle group to participate in power grid adjustment. The method comprises the following steps: firstly, collecting a power grid side load, renewable output and meteorological data, constructing a feature vector set, and performing multi-step prediction on a future net load by using a rolling time sequence prediction model; and according to the predicted load change rate, dividing a charging excitation period and a discharging compensation period, calculating excitation intensity, and dynamically generating a corresponding time-of-use electricity price signal. And in combination with the real-time operation state of the charging pile and the system response capability, a multi-target optimization strategy is adopted to formulate a charging and discharging power plan, and power grid peak regulation, user income and travel satisfaction are considered. Scheduling is executed, online updating is carried out on the model based on feedback deviation, and closed-loop control is formed. According to the method, scheduling optimization of the flexible load of the electric vehicle is effectively realized, and the economical efficiency and the safety of power grid operation are improved.