6390 results about "Charging station" patented technology

The invention belongs to the technical field of charging station planning, and discloses a charging station planning method and system based on an automobile charging demand, and the method comprises the steps: building a space-time charging demand distribution map through multi-source data collection and fusion processing, carrying out the seasonal change analysis and future demand prediction, and obtaining a dynamic charging demand prediction model; site layout optimization under a multi-constraint condition is performed by combining an urban road network structure and traffic flow data to form a preliminary charging station layout scheme, and power grid load capacity evaluation and renewable energy access analysis are implemented to establish an energy collaborative supply guarantee system. And designing a peak-valley period charging price dynamic adjustment and appointment queuing mechanism to form an intelligent scheduling control strategy, and finally obtaining a diversified charging facility configuration scheme through different charging power requirements and vehicle type suitability evaluation. According to the method, the problems of inaccurate demand prediction, unreasonable layout, uneven resource allocation and the like in traditional planning are solved, and accurate matching of charging resources and user demands is realized.

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention provides an optical storage charging and discharging station aggregation control and optimization method based on a virtual power plant, and aims to solve the problems of multi-target collaborative optimization, dynamic resource response and uncertainty robustness. By introducing a Markov decision process and an adaptive clustering algorithm, the system can dynamically aggregate photovoltaic, energy storage and charging pile resources according to equipment characteristics, and power dispatching is optimized. A multi-objective optimization model is adopted, economical, technical and environmental objectives are combined, a dynamic weight factor is introduced, and optimal scheduling is generated in combination with a fuzzy decision theory. And real-time compensation is carried out by adopting a rolling time domain control framework and deep reinforcement learning, so that the scheduling precision and the response speed are improved. The edge computing and cloud collaboration mechanism reduces the communication load through a lightweight federated learning model, and improves the scheduling response efficiency. According to the invention, the scheduling efficiency of the optical storage charging station can be obviously improved, the operation cost is reduced, the system stability is improved, and the system has good adaptability and expandability.

A system and method for directing energy use within a facility comprising charging stations for charging a fleet of electric vehicles, using an energy optimisation engine. The energy optimisation engine receives energy supply characteristics and facility operational energy consumption characteristic of energy assets associated with the facility over a first time period, and electrical vehicle operational requirements for the electric vehicles during a second period; computes energy distribution instructions for the first time period and satisfying a designated energy condition corresponding at least in part with the energy supply characteristics; and directs operation of the charge stations or the energy assets in accordance with the energy distribution instructions.

The invention discloses a charging pile power regulation and control method and system, and particularly relates to the technical field of charging pile management, data is collected from a multi-dimensional data source and summarized to form a preliminary information set, and a standard information set is obtained through format standardization processing; feature data of the charging pile are extracted based on the standard information set, and charging task analysis and resource occupation analysis are carried out respectively; inputting the analysis result into a pre-trained machine learning model to generate a charging priority coefficient; constructing a priority matrix, and dividing the charging piles into high priorities and low priorities according to the charging priority coefficients; and finally, dynamically adjusting a power distribution strategy according to the priority type, optimizing low-priority task power distribution while meeting high-priority task requirements, and improving the operation efficiency and the resource utilization rate of the charging station. According to the method, the real-time operation state and the charging task characteristics of the charging piles are combined, the power distribution strategy is dynamically adjusted, the priority division and power distribution proportion of the charging piles are adjusted in real time, and the self-adaptive capacity of operation of the charging station is improved.

The invention discloses a power distribution management system of an intelligent charging pile, and belongs to the technical field of electric vehicle charging facilities and intelligent power grids. The load prediction module carries out space-time alignment fusion on historical data and real-time monitoring data to generate a power distribution demand prediction value, and the edge calculation controller executes a model prediction control algorithm based on multi-source data to generate a real-time control strategy containing relay time sequence parameters and a capacitance compensation scheme. And the dynamic power distribution adjustment module executes strategy parameters through the solid-state relay array and the parallel compensation capacitor bank. Self-adaptive adjustment of the power distribution network is achieved through a real-time monitoring-prediction-optimization closed-loop control mechanism, the harmonic content of the power grid is effectively reduced, the energy distribution efficiency of the charging pile group is improved, and the method is suitable for intelligent electric energy management of public charging stations and other scenes.

The invention discloses a charging station operation maintenance method based on a multi-dimensional data system, and relates to the field of intelligent optimization, and the method comprises the steps: collecting the operation, environment, user and power grid data of a charging pile, preprocessing the data into JSON, carrying out the real-time analysis and classification through Kafka-Flume, storing the data into a distributed database, building a composite index, and carrying out the retrieval through Presto; after cleaning, combining GraphQL and a graph query language to construct a space-time atlas, and extracting a multi-dimensional feature vector; a digital twinborn model is generated, multi-source data are fused through virtual-real mapping, and residual correction and reinforcement learning optimization are combined; fusing multi-modal data by using a hypergraph neural network, and optimizing topology in combination with an entropy regularization multi-target strategy and meta-learning; power is dynamically distributed, LSTM-GAN predicts and positions faults, and PID regulates and controls output; and the block chain contract automatically triggers equipment freezing and power limiting, and on-chain data feedback optimizes scheduling and load balancing. According to the invention, through multi-dimensional data fusion and a dynamic optimization mechanism, the operation and maintenance accuracy and response efficiency of the charging station are significantly improved.

The invention relates to the technical field of new energy vehicle charging, and discloses a new energy vehicle charging station power supply optimization regulation and control system in a power supply peak period, and the system comprises a processor, a charging demand prediction module, a power supply load distribution module, a real-time regulation and control module, and a power supply performance monitoring module. The charging demand prediction module predicts a charging demand based on historical data and user behaviors; the power supply load distribution module distributes power supply priority and power upper limit according to the prediction result and the power grid state; the real-time regulation and control module adjusts power supply parameters according to the real-time data The power supply performance monitoring module records the regulation response time and the deviation value. In addition, the system is further provided with an energy storage coordination module, a user priority evaluation module and a power grid interaction module which are respectively used for optimizing energy storage management, evaluating user priority and coordinating power grid power supply. The system can accurately predict the charging demand, reasonably distribute power supply resources, regulate and control power supply parameters in real time, and improve the power supply efficiency and stability of the charging station.

The invention relates to a power distribution system optimization method considering a space-time game under vehicle-station-network interaction, and the method employs a double-layer optimization framework integrating the space-time game and dynamic electricity price, an upper decision maker is a power distribution system operator, and the power distribution system operation cost is minimized and the renewable energy consumption rate is maximized. Generating a dynamic electricity price signal in a time-sharing and partition manner; the lower-layer main body comprises an electric vehicle user and a charging station operator, and the electric vehicle user optimizes a charging time period and a charging station and forms charging demand distribution through a non-cooperative game based on a dynamic electricity price signal and a charging service charge by taking the maximum self-charging decision utility as a target; and a charging station operator optimizes and adjusts a charging service fee decision by taking charging demand distribution as input and taking revenue maximization as a target. Compared with the prior art, the method can collaboratively optimize the economic operation of the power grid and the consumption of renewable energy sources, promotes the efficient flow and balanced distribution of resources among regions, and provides decision support for the dispatching of the power distribution network.

The invention provides a new energy vehicle aggregation prevention and control system based on clustering identification and regional risk modeling, and aims to solve the potential safety hazards such as thermal runaway caused by high-density vehicle aggregation. The system comprises a state acquisition module, a clustering identification module, a regional risk modeling module, a risk grade determination module, a response control scheduling module and a thermal risk prediction module. The system collects running state data such as vehicle position, battery temperature and charge state, uses a density-based spatial clustering algorithm to identify clusters, calculates risk score values of the clusters, and implements current limiting and path guiding control according to risk levels. Meanwhile, the risk change trend of a future area is predicted through the trained long-short-term memory neural network model, and early warning and response are achieved in advance. The system is suitable for safety management scenes of new energy vehicles in high-density areas such as charging stations and underground garages.

An apparatus and method relate to analyzing metrics relating to EV charging points and facilitating selection of an EV charge point based on metrics associated with the EV charge points. Methods may include: determining locations of one or more electric vehicle charging stations; determining a total power available at the one or more electric vehicle charging stations; determining, for the one or more electric vehicle charging stations, if dynamic load balancing is used; determining occupancy of charge points at each of the one or more electric vehicle charging stations; calculating an available power at a charge point of the one or more electric vehicle charging stations based on the total power available and the occupancy of charge points for the one or more electric vehicle charging stations; and calculating an estimated time to charge a vehicle at the one or more electric vehicle charging stations based on the available power.

The invention discloses a dynamic power distribution method for charging equipment of an electric vehicle charging station. The method comprises the following steps: acquiring real-time state data of an electric vehicle through a vehicle sensor and a charging equipment communication interface; obtaining the charging priority of the current electric vehicle based on the real-time state data; based on the charging priority and the real-time state data, the power distribution proportion of each vehicle is calculated by adopting a weighted distribution algorithm, and an initial power distribution scheme is obtained; obtaining an available power interval of a charging station based on peak-valley state data of a current time period monitored by a power grid monitoring system; and based on the initial power distribution scheme and the available power interval of the charging station, an adjusted power distribution scheme is obtained, and dynamic power distribution of the charging equipment of the electric vehicle charging station is completed. According to the invention, efficient utilization of charging station resources and intelligent scheduling of vehicle charging demands can be realized, and charging efficiency and user satisfaction are improved.

The invention relates to the field of new energy charging, in particular to a new energy charging management method for a smart city. The method comprises the steps that a municipal vehicle operation feature data set is acquired, on the basis, municipal vehicles are subjected to type division through a vehicle type classification strategy, and a municipal vehicle driving type set is determined; acquiring a municipal vehicle task set, and on the basis, tracking a dynamic task variable set of a task execution vehicle in a municipal vehicle driving type set in a task execution process; according to the dynamic task variable set, analyzing an association relationship between the dynamic task variable set and the real-time power consumption data through a multi-modal fusion strategy, and determining dynamic power change information; and acquiring a charging station information set, dynamically optimizing a charging decision in combination with the municipal vehicle task set and the dynamic electric quantity change information, and determining and outputting an optimal charging strategy. In the municipal vehicle charging decision-making process, the utilization rate of charging resources is improved, and the continuity of municipal task execution is ensured.

The present disclosure describes a vehicle charging system comprising a vehicle charging gun comprising at least two pins, a protective earth (PE) pin configured to connect to ground and a proximity pilot (PP) pin configured to detect when the vehicle charging gun has been inserted into a vehicle; and an apparatus to couple with the vehicle charging gun, the apparatus comprising: a switch operable between a first position and a second position, the switch being controlled by a controller, wherein, when the switch is in the first position, the switch enables the vehicle or a vehicle charging station to detect that the vehicle charging gun is inserted into a vehicle inlet port of the vehicle, and when the switch is in the second position, the apparatus generates an electrical effect that simulates the removal of the vehicle charging gun from the vehicle inlet port.

The invention discloses a charging pile system management method based on dynamic load balancing, and particularly relates to the technical field of charging pile management, and the method comprises the steps: collecting time domain scheduling data of a charging pile side, a power grid side and a distributed renewable energy output device side, carrying out the fusion filtering, and obtaining a station-level state vector; generating a station-level prediction sequence of the charging pile in the next control period based on the station-level state vector and a rolling prediction model; an inner-layer controller reads the station-level state vector and the station-level prediction sequence, establishes a power prediction optimization model and solves the model, and outputs an expected station-level power trajectory and station-level power redundancy; a result output by the inner-layer controller is received, and a station-level power envelope curve and an energy storage charging and discharging set value are comprehensively solved and transmitted; the slope upper limit of the station-level power envelope curve is contracted based on the oscillation amplitude, the new slope upper limit is substituted into the next solution, the subsequent power change rate is limited, and the problem of unstable power exchange between the charging station and the superior power grid is effectively solved.

The invention relates to the technical field of electric vehicle charging management, and discloses an electric vehicle charging optimization management system. The system comprises a dynamic charging demand prediction module which constructs a model based on historical data, collects voltage fluctuation, current phase deviation and charging power pulse characteristics of a charging pile in real time, and outputs a theoretical charging load value; the multi-dimensional demand difference analysis module is used for performing three-dimensional comparison of time accumulation deviation, frequency response difference and pulse characteristic similarity on theoretical and actually measured load values to generate a charging pile group level difference characteristic tensor; the hot spot region association positioning module inputs the difference feature tensor into a spatial topology mapping network, generates a fault probability thermal distribution diagram in combination with power grid node capacity and charging station position information, and positions a charging bottleneck region; and the self-adaptive regulation and control strategy generation module is used for starting millisecond-level monitoring on a high fault probability region according to the thermal distribution diagram and applying a power disturbance test on adjacent nodes. The system can improve the accuracy and effectiveness of charging management.

The invention discloses an electric vehicle charging station ordered charging method based on intelligent queuing, which comprises the following steps: acquiring a remote charging queue adding request, and extracting a vehicle identifier and current position information; a sliding window mechanism is adopted, a fixed time interval is set, charging station traffic flow and charging pile state change data are collected, and the estimated waiting time is used for updating queuing information; according to the distance between the vehicle and the charging station, the priority queue algorithm is adopted to allocate the charging pile position in combination with the predicted waiting time, and it is ensured that the closer vehicle preferentially obtains the charging opportunity; acquiring position information of a plurality of vehicles entering the range of the charging station at the same time, processing the requests by using a timestamp sorting algorithm based on vehicle entering time, and determining a guiding sequence of each vehicle; and generating a navigation path from the current position to the specified charging pile position according to the guiding sequence and the distributed charging pile position, and sending path information to the vehicle-mounted navigation system. The charging efficiency can be remarkably improved.

The invention relates to the technical field of monitoring or controlling a charging station, in particular to a charging pile safety monitoring method and system, and the method comprises the following steps: step 1, monitoring: step 1.1, electric leakage monitoring: installing a high-sensitivity electric leakage sensor in a charging pile and a charging gun line, continuously monitoring the electric leakage current in real time, and monitoring the electric leakage current in real time; the monitoring frequency is that data are collected once every 10 milliseconds; step 1.2, overload and short circuit monitoring: real-time current change of a charging circuit is monitored by using a current transformer, and the acquisition frequency is once every 5 milliseconds; step 1.3, monitoring the temperature; according to the scheme, through monitoring of electric leakage, overload and short circuit, temperature, smog and flame, charging gun safety, vision and the like in combination with intelligent analysis, the operation state and the surrounding environment condition of the charging pile can be comprehensively, timely and accurately mastered, electrical faults, temperature anomalies and potential safety hazards can be rapidly detected and judged, and the safety and reliability of the charging pile are improved. And all-around real-time monitoring of the potential safety hazard of the charging pile is realized.

The invention relates to the technical field of new energy supply management, in particular to a charging pile background management system and method. The method comprises the steps of obtaining photovoltaic output, an energy storage state and charging load data, completing time alignment and normalization processing, and constructing a unified data input structure; configuring a photovoltaic priority strategy and energy storage scheduling parameters based on the structure, constructing and solving a multi-energy power supply optimization model, and generating a power supply scheduling output result; combining the electricity price sequence to identify peak and valley time periods, generating an arbitrage label, and further generating an energy storage charging and discharging control strategy and an energy storage regulation and control instruction structure; issuing a control instruction, collecting execution data, and generating an energy consumption deviation feedback structure; and finally, realizing adaptive closed-loop correction of the scheduling model based on a feedback structure. According to the invention, the photovoltaic utilization rate can be improved, peak-valley arbitrage and energy storage cooperative control is realized, the power supply cost of the charging station is significantly reduced, and the system scheduling precision and operation stability are enhanced.

The invention discloses an electric vehicle charging station layout optimization configuration method and system, and the method comprises the following steps: obtaining the traffic network information and power distribution network information of a planning target area, and determining the charging demand number of electric vehicles and the charging load demand distribution condition; according to the electric vehicle charging load demand distribution condition, determining the service range of each charging station, and constructing a proposed charging station capacity determination model; an upper-layer charging station site selection model is constructed, and the optimal site of each charging station is determined; establishing a lower-layer mixed integer programming model, and determining the configuration number of various charging piles of each charging station; solving the double-layer locating and sizing planning configuration model by using a hybrid genetic simulated annealing algorithm and an analytic hierarchy process to obtain an optimal locating and sizing scheme of the charging station; according to the method, the attraction of the charging station to the user and different types of charging pile configuration schemes are comprehensively considered, a bilevel planning configuration model is established, an optimal solution set is solved by using a hybrid genetic simulated annealing algorithm, and finally an optimal scheme is determined through an analytic hierarchy process.

Systems and methods for selecting a charging entity based on occupancy status are provided. In one embodiment, a method includes determining a current geo-location and state of charge of a requesting vehicle and a plurality of charging entities that are within a remaining distance of the requesting vehicle. Occupancy statuses for one or more charging entities of the plurality of charging entities are determined based on a number charging vehicles and a charging parameter. The method includes estimating charging speeds for the one or more charging entities based on the occupancy statuses. A charging station map user interface that pin points the current geo-location of the requesting vehicle and the one or more charging entities is presented. The one or more charging entities are presented with labels. The method further includes reserving a charging station of a selected charging entity of the plurality of charging entities by selecting a label.

A system for controlling a charging of an electric vehicle, wherein a charging at one electric vehicle charging station affect a charging at another electric vehicle charging station is disclosed. The system includes: an electric power grid, a first electric vehicle charging station connected to the electric power grid, and a second electric vehicle charging station connected to the electric power grid, wherein the first electric vehicle charging station facilitates a charge transfer for an electric vehicle at the second electric vehicle charging station using a mobile device. The mobile device relays communication from the electric vehicle charging stations to the cloud server. The charge transfer request received at the cloud server is authorized using identification information and credit account information received from the mobile device. The charge transfer at the first electric vehicle charging station is adjusted based on a charging level at the second electric vehicle charging station.

The present invention relates to a method for checking charging stations and their functional scope by vehicle-side or charging station-side detection of relevant parameters and faults occurring during a charging process of an electric vehicle at a charging station.

The invention discloses a charging pile output power control method, and particularly relates to the technical field of charging pile management, and the method comprises the following steps: collecting the real-time operation information of a charging station, analyzing the overall load fluctuation condition, and judging whether an early sign that the operation fluctuation intensity is too high exists or not; and if abnormity is identified, further collecting operation information of each charging pile, extracting feature data, and performing stability and adaptability evaluation. And an evaluation result is imported into a fuzzy reasoning model, and the charging pile is divided into a high-quality operation state and a low-quality operation state. In a single abnormal scene, the charging piles in a high-quality operation state maintain a constant-current and constant-voltage strategy, and the charging piles in a low-quality operation state are switched to an adaptive power adjustment strategy; in an overall abnormal scene, all the charging piles are switched to an adaptive power adjustment strategy, so that the operation efficiency and stability are improved; the strategy can be dynamically adjusted according to different operation scenes, and the requirements under different operation conditions are met.

An electronic device situated in an electric vehicle displays the precise state of charge (SOC) and charging status of the vehicle such that this information is visible from a substantial distance outside the vehicle while the vehicle is charging, connected to a charging station, or idle after disconnection from a charging station. The displayed information is easily comprehensible to a driver in an approaching electric vehicle who is interested in using the charging station, allowing the approaching driver to quickly determine if the charging station is available for use, or how soon it may be available. A method for accessing and displaying information regarding an electric vehicle charging session, allowing for a constant display of charging information outside of the electric vehicle.

The invention provides a marine high-power container type charging station, which belongs to the technical field of marine charging stations, and realizes a nine-step charging control process by integrating an electric energy stability regulation module through a control chip: firstly, receiving power grid monitoring data, calculating an evaluation index, and constructing a prediction model to generate an early warning signal; adjusting the power output of the electric pile assembly according to the early warning signal; receiving data of a ship battery management system and calculating an optimal charging curve; adjusting charging parameters based on the temperature data and the thermal diffusion equation; the harmonic content is calculated in real time, and harmonic waves are suppressed through a fluctuation compensation model; monitoring a power factor and executing reactive compensation; calculating multi-path charging power distribution based on the battery optimization model; and the charging power change rate is adjusted when power grid frequency fluctuation is detected. According to the system, a multilayer convolutional neural network and a deep reinforcement learning technology are fused, and stable and efficient charging in a complex power grid environment is realized through adaptive weight adjustment.

The invention relates to the technical field of charging stations, and discloses an intelligent charging station monitoring method based on digital twinning, which comprises the steps of 1, data acquisition and preprocessing, 2, digital twinning model construction and calibration, 3, real-time monitoring and intelligent decision making, and 4, full life cycle management. The invention also provides an intelligent charging station monitoring system based on digital twinning, which comprises a physical module, a virtual module, a data module, an application module, a connection module and a visualization module. The system has the beneficial effects that multi-source data are collected in real time through the Internet of Things, a digital twin model is constructed, a perfect 3D visual interface is provided, and information such as the equipment running state, the charging process and the energy flow of the charging station can be accurately displayed in real time. And meanwhile, remote monitoring and intelligent control of charging station equipment, such as functions of remotely adjusting the power of the charging pile, switching equipment and the like, are realized, and convenient and efficient operation experience is provided for a user.

The invention discloses an electric power-traffic collaborative balance scheduling method and system for electric vehicle charging, and belongs to the technical field of electric power systems, and the method comprises the steps: obtaining the travel charging demand information, charging station information and traffic network information of each electric vehicle in real time; constructing a power-traffic coupling operation network model which comprises a power network model, a traffic network model and a coupling network model; using a multi-agent multi-layer graph convolutional learning algorithm to perform power-traffic collaborative balance scheduling, using a preposed graph convolutional network to perform coupling feature representation and environmental state perception on the coupling network model to obtain the coupling feature of each node, and using a charging station as an agent to perform power-traffic collaborative balance scheduling; and inputting the coupling characteristics of the node where the charging station is located into the state space as a part of observed quantity of the intelligent agent, and outputting a charging scheduling decision of each electric vehicle through a target strategy network and a target value network in the intelligent agent. The electric vehicle charging routing behavior can be optimized, and real-time balanced scheduling of the electric power-traffic network is realized.

The invention provides an intelligent management system for automatic charging and endurance optimization of hotel robots. The intelligent management system comprises a positioning module, a posture adjustment module, a charging monitoring module, a charging control module, a battery management module, a charging strategy optimization module, an energy consumption balance task distribution module and a path planning module. Through the positioning and posture adjusting module, the robot can be accurately connected with a charging station in a complex hotel, and it is ensured that charging is started accurately and stably; multi-element monitoring is combined with precise charging control, regulation and control are performed according to battery characteristics, the service life is prolonged, and endurance is guaranteed; modules such as fine battery management, charging strategy optimization and the like are linked, charging and task assignment are reasonably arranged, energy consumption is reduced, efficiency is improved, and efficient operation of hotels is enabled.

A charging system comprises a local energy storage disposed at a charging site; an electric coupling configured to provide an electrical interconnect between the charging site and an electric vehicle (EV); and one or more controllers configured to: (i) detect a condition related to availability of electrical energy at the charging station, (ii) in response to the detecting of the condition: (a) estimate a number of EVs to which a charge remaining at the local energy storage is to be distributed within a certain time interval, (b) initiate a charging session for transferring electric energy from the local energy storage to the EV, via the electric coupling, and (c) limit an amount of electrical energy in the charging session in view of at least the estimated number of EVs.