38025results about "Charging stations" patented technology

The invention provides a charging pile intelligent scheduling processing method and system. Wherein the dynamic charging demand network topology is constructed by fusing the remaining electric quantity of the vehicle, the occupation state of the charging pile and the vehicle type priority label. And updating vehicle position and charging pile state data in real time, predicting a vehicle motion trend and generating a preemption conflict early warning signal. And based on a multi-main-body coordination distribution mechanism, a matching degree score of the vehicle and the charging pile is calculated in combination with a residual electric quantity attenuation rate and a departure time constraint, and a distribution strategy is dynamically optimized. And finally, the power distribution gradient is adjusted according to the score sequence, so that the high-electric-quantity attenuation vehicle obtains higher power improvement, and efficient resource matching and conflict relief are realized. According to the technical scheme, through dynamic prediction and priority matching, charging pile distribution and power adjustment are intelligently optimized, the charging efficiency of the parking lot is remarkably improved, and vehicle waiting conflicts are reduced.

The invention relates to the technical field of charging pile collaborative management, in particular to a new energy automobile charging pile intelligent operation and maintenance management system, which comprises a data acquisition and transmission unit, an edge computing unit and a cloud server unit, and is characterized in that the data acquisition and transmission unit collects multi-aspect information of a charging pile and transmits the multi-aspect information to the edge computing unit; the edge computing unit preprocesses the data, starts an emergency protection mechanism when the power grid fluctuates, and uploads the data and decision results, the cloud server unit constructs a cloud computing platform, a data storage service stores the data, a data analysis service excavates a charging rule, and an algorithm model service deploys an intelligent adaptive power distribution algorithm. The system comprises a dynamic priority determination module, a real-time power adjustment module and a cross-station cooperation power scheduling module which are respectively used for determining the vehicle priority, adjusting the power according to the power grid load and guiding the vehicle to perform cross-station charging, thereby solving the problems of unreasonable power distribution and insufficient cross-station cooperation in the operation and maintenance management of the charging pile, and improving the use efficiency of the charging pile.

The invention discloses an optical storage charging and discharging integrated power station vehicle network interaction method considering demand side response, and aims to improve the bidirectional interaction capability of a power station and a power grid and realize dynamic matching between the demand side response of the power grid and user charging and discharging behaviors. A high-precision photovoltaic output prediction model, an energy storage system SOC dynamic model and an electric vehicle charging load probability distribution model are constructed, and a joint output feature library is formed. A dynamic charging and discharging priority division method is put forward, and the charging and discharging power distribution weight is dynamically adjusted in combination with the vehicle state, the user electricity price sensitivity and the power grid load curve. And designing a composite demand side response excitation mechanism combining time-of-use electricity price and capacity compensation, and guiding the user to participate in peak regulation and valley filling of the power grid. An edge computing and cloud cooperative control framework is adopted, and a dynamic security check module based on model predictive control is adopted, so that potential out-of-limit risks are evaluated in advance, and the security of a power station and a power grid is guaranteed. And efficient operation of the light storage charging and discharging integrated power station and efficient utilization of new energy are realized.

The embodiment of the invention provides a new energy automobile charging pile fault monitoring method and system, and relates to the technical field of charging pile fault detection. The new energy automobile charging pile fault monitoring method comprises the following steps: S1, collecting multi-source data in the operation process of a charging pile; s3, preprocessing the multi-source data to generate a multi-dimensional feature data set; s3, constructing a dynamic fault threshold model; s4, on the basis of the multi-dimensional feature data set and the dynamic fault threshold model, multi-level fault detection is executed; s5, establishing a fault prediction mechanism; and S6, generating a fault monitoring report according to the fault type and the fault severity evaluation index. Through multi-source data acquisition, dynamic threshold construction and a multi-level fault detection and prediction mechanism, real-time accurate monitoring and fault early warning of the operation state of the charging pile can be realized, and compared with a traditional method, the accuracy and the advancement of fault identification are remarkably improved.

The invention provides a charging pile line fire-fighting early warning method based on big data and a storage medium, and the method comprises the steps: collecting a line operation data set of a target charging pile cluster, the line operation data set comprises multi-source time sequence monitoring data, carrying out the cross-modal feature alignment of the multi-source time sequence monitoring data, and generating a time-space correlation feature matrix; inputting the space-time correlation feature matrix into a pre-trained fire risk prediction model, generating a line abnormal risk probability distribution diagram, generating a layered early warning signal set according to risk levels corresponding to space nodes in the line abnormal risk probability distribution diagram, and triggering a dynamic protection mechanism based on the layered early warning signal set. The dynamic protection mechanism includes performing a current cut-off operation on a high-risk line segment, and performing a power attenuation operation on an adjacent line segment. According to the invention, the reliability and the intelligent level of the charging pile line fire-fighting early warning system can be comprehensively improved.

The preferred embodiments disclose a hangar comprising a gantry robot which includes a lifting tool. The gantry robot is provided for lifting and lowering batteries to replace them, for example, in a drone disposed in the hangar.

The invention discloses a charging chip real-time power scheduling control method, and relates to the technical field of new energy automobile charging pile power scheduling, and the method comprises the following steps: S1, obtaining the operation parameters of charging equipment in real time, including the voltage fluctuation value of an input power grid, the output current of a charging interface, and the temperature of a charging chip; according to the method, the power grid voltage fluctuation, battery SOC / temperature and chip working condition multi-source data are collected in real time, a multi-target optimization model of dynamic weight distribution is combined, the service life loss of the battery is minimized, the charging efficiency and the power grid load are balanced at the same time, the user charging mode and the battery state difference are taken into consideration by adopting a priority scheduling strategy, and the charging efficiency is improved. Millisecond-level power fine control is achieved through cooperative adjustment of the PWM duty ratio and the switching frequency, personalized charging requirements are met, the overall energy efficiency is improved, a dual safety defense line is constructed through a graded load reduction protection mechanism and a fuzzy control algorithm, and a power distribution scheme is intelligently reconstructed when a power grid is abnormal or a battery is overheated.

The invention relates to the technical field of battery swapping for electric vehicles. A control method of a smart battery swapping station includes: acquiring vehicle information of a vehicle requiring battery swapping, and retrieving a model type of a corresponding battery pack locking and unlocking hole and specific parameters from a database; when the vehicle arrives at a battery swapping operation platform, capturing an image of a vehicle chassis to determine an initial pose of the vehicle chassis; processing the initial pose to extract center coordinates of the locking and unlocking hole and a normal vector direction of the locking and unlocking hole; comparing the center coordinates of the locking and unlocking hole and the normal vector direction of the locking and unlocking hole with the retrieved model type of battery pack locking and unlocking hole and specific parameters in the database to determine if the information is consistent.

The invention relates to the technical field of electric vehicle battery charging detection, and discloses an intelligent charging pile system and method integrating real-time battery state detection. A data fusion module; a battery health state prediction module; a charging optimization control module; the fault early warning module is connected with the cloud platform and edge computing cooperative processing module; the method comprises the following steps: acquiring battery data through multiple sensors, and constructing a data frame; extracting electrical characteristics, and generating a battery state vector; a digital twin model is constructed, and health state prediction is carried out; formulating a dynamic charging power regulation strategy; comparing the health trend of the battery with an expected behavior, and generating a fault early warning signal; and uploading the data and the strategy to the cloud platform, and updating the control strategy. According to the invention, a multi-sensor data fusion technology is adopted, twin modeling and a deep learning algorithm are combined, the health state of the battery is monitored in real time, the health state of the battery is comprehensively evaluated, and the fault risk of the battery is accurately predicted.

The invention relates to the field of new energy vehicles, and discloses a charging pile load test data analysis method and system based on intelligent sensing, and the method comprises the steps: carrying out the dynamic mapping of a charging pile group in a public parking lot through a multi-source heterogeneous sensing assembly; carrying out dislocation labeling on handshake waveforms, thermal image hot spots and harmonic pulses based on a scene semantic segmentation network; performing multi-scale sliding window cross-domain correlation tracking on the data priority sequence, and combining historical waveform fingerprints, a power factor drift trajectory and an environmental electromagnetic interference matrix; based on a multi-dimensional time sequence feature tensor, a load evolution trajectory is extracted by using a priority adaptive model, and clustering density abrupt change and entropy drift inflection points are identified; calibrating a charging entity and a coupling range corresponding to the potential safety hazard candidate event as a candidate area; and on the basis of a local load priority interference atlas, potential unstable nodes and a global risk entropy fluctuation trend in monitoring data are combined. The method has the advantage of improving the safety and accuracy of the load in a complex environment.

The invention relates to the field of new energy automobile power supply, and provides a charging pile ordered charging method, which comprises the following steps: collecting a multi-source data set in real time; based on the multi-source data set, generating a dynamic scheduling strategy through a multi-objective optimization algorithm; generating a charging power control instruction set according to the power distribution matrix of each charging pile, and generating a charging sequence scheduling instruction based on the time window mapping table of each charging pile; issuing the charging power control instruction set and the charging sequence scheduling instruction to a target charging pile group to start a charging process; in the execution process of the charging process, monitoring an actual operation parameter set of each charging pile in real time, and verifying the execution consistency of actual operation parameters in the actual operation parameter set and the charging power control instruction set; and dynamically adjusting a power distribution coefficient in the power distribution matrix based on a deviation analysis result of the operation parameter set and the dynamic scheduling strategy, and triggering rolling optimization calculation of a multi-objective optimization algorithm to update the scheduling strategy.

The invention provides an optical storage and charging cooperative control method and system based on multi-energy complementation. The method comprises the following sub-steps: respectively establishing a photovoltaic power generation model, an energy storage system model and a charging load model; acquiring historical illumination information, charging information and electricity price information, constructing a prediction model based on a neural network, and predicting and outputting illumination intensity, charging load demand power and electricity price in a future time period; constructing a target optimization function by taking the annual net cost and the power deviation rate as optimization targets; according to the method, the cooperative optimization control of the photovoltaic power generation, energy storage and charging system is realized, the annual net cost is reduced, the power deviation is reduced, the energy storage and charging cooperative control strategy is established, the target optimization function is solved by adopting the improved whale optimization algorithm, and the charging and discharging power sequence is generated according to the optimal solution obtained by solving and is input to the system for execution. And the operation efficiency and reliability of the whole system are improved.

A base station is disclosed for an unmanned aerial vehicle (UAV). The base station includes: an enclosure; a slide mechanism that is connected to the enclosure and which is repositionable between a retracted position and an extended position; and a cradle that is connected to the slide mechanism and which defines a chamber that is configured to receive the UAV such that the UAV is movable into and out of the enclosure during repositioning of the slide mechanism between the retracted position and the extended position. The cradle includes: an upper shell; a lower shell that is connected to the upper shell; and at least one thermal insulator that is located between the upper shell and the lower shell.

The invention relates to a charging pile fault diagnosis method based on dynamic state time sequence division, and the method comprises the steps: obtaining the current, voltage, temperature and charging mode signals of a charging pile in real time through a multi-source synchronous collection module, and constructing multi-mode data; based on a dynamic window optimization algorithm of reinforcement learning, time sequence window parameters are adaptively generated according to charging mode switching and a power change rate, and the problem of data segmentation failure of a fixed window in a fast charging / slow charging scene is solved; nonlinear transformation is carried out on the time sequence data segment, and weak fault features submerged by noise are extracted; event triggering synchronization is performed on asynchronous multi-modal data, so that time dislocation interference is eliminated; the fault probability is output by fusing the multi-dimensional features, a diagnosis report is generated by combining time continuity combination and confidence rating, the fault occurrence time period and type are accurately positioned, the weak fault detection capability and the multi-modal data fusion precision are improved, the omission ratio and the false alarm rate are reduced, and the method is suitable for intelligent operation and maintenance of the charging pile under the complex working condition.

The present disclosure describes a connector box, comprising a vehicle inlet port configured to receive a vehicle connector configured to connect to a direct current (DC) or alternating current (AC) vehicle charging station; a locking mechanism configured to secure the vehicle connector within the vehicle inlet port, wherein the locking mechanism is configured to be activated upon insertion of the vehicle connector into the vehicle inlet port and remain activated until deactivation by an authorized user; and an outlet port configured to supply electrical power received via the vehicle inlet port and to an apparatus, the apparatus configured to supply the electrical power from the outlet port to a vehicle connected to the apparatus in a vehicle charging session.

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 provides an electric vehicle ordered charging strategy optimization method and system. Wherein the dynamic change of the electromagnetic field intensity is monitored, and a constraint model is established based on association of electromagnetic interference and charging efficiency; sound wave sensors are arranged on the periphery of the charging pile, the occupation state of the charging pile and the vehicle parking position are analyzed, and charging time window prediction is generated in combination with vehicle battery parameters; according to the power distribution priority and the charging time window prediction, generating an initial charging scheduling sequence, and starting a reactive power compensation device to carry out closed-loop compensation on the power factor deviation of a charging pile power supply loop; in the charging scheduling process, performing coupling iteration on the electromagnetic interference suppression index and the power factor adjustment parameter, and dynamically optimizing the power distribution gradient; and updating charging time window prediction according to vehicle movement information, and generating a final charging strategy in combination with a dynamic optimization result. The technical scheme provided by the invention improves the charging efficiency of the electric vehicle and stabilizes the power quality of a power grid.

A system and method that manages charging of Electric Vehicles (EVs) at a depot equipped with EV chargers. For each EV arriving at the depot, data is collected from the EV including a current state of charge (SoC), an SoC goal, a route estimate, and a charging schedule. A depot controller determines from the collected data a charging priority for the EV relative to other EVs at the depot. When charging is required to meet the SoC goal of the EV, a charger is selected for charging the EV and a charging start time and charging duration is determined by an optimization algorithm that concurrently optimizes a minimal total cost of electricity for total power transferred to the EV during charging while maximizing a probability of the EV leaving the depot with its SoC goal satisfied.

The invention discloses a charging pile intelligent control management platform, relates to the technical field of charging pile intelligent control, and can identify the peak and valley periods of the power grid load in advance by collecting and analyzing the power grid load data and the operation state of a charging pile in real time and combining a regression model of a load prediction and demand analysis module. The power output of each charging pile is dynamically adjusted through the charging pile scheduling and power adjusting module, the power grid resource distribution is optimized, the overload phenomenon is avoided, and the stability of power grid operation is improved. By monitoring and feeding back the charging behavior data of the user, the platform can reasonably plan the working state of the charging pile. Therefore, too fast loss of some charging piles due to continuous overload is avoided, resource waste of idle charging piles is reduced, the utilization efficiency of equipment is improved on the whole, and the service life of the charging piles is prolonged. By using the load prediction and demand analysis module, the platform can pre-judge the charging demand peak of the user and optimize the scheduling strategy in advance, thereby shortening the waiting time of the user.

The invention relates to the technical field of electric vehicle charging pile regulation and control, in particular to a control method of charging pile equipment, which comprises the following steps of: constructing a multi-mode sensing data fusion model, integrating electrochemical impedance spectroscopy, ultrasonic wave and infrared thermal imaging sensor data, deeply excavating the aging condition of a battery, and obtaining the charging pile equipment. The aging trend is predicted by means of a deep belief network, a dynamic optimal power strategy is generated according to a deep Q network algorithm, the charging efficiency is improved, the service life of the battery is prolonged, and for single battery management, an electrochemical-thermal-mechanical coupling microenvironment model and a finite element and data driving fusion strategy are used for solving the problem. The microenvironment of the single batteries is strictly monitored and finely balanced and regulated, and the temperature and voltage differences among the single batteries are strictly controlled in an extremely small range, so that the performance consistency and stability of the battery pack are greatly improved, and the charging efficiency of the single batteries of the electric vehicle is improved.

Systems and techniques for location management are described herein. In an example, a system may include at least one processor and at least one memory with instructions stored thereon that when executed by the processor, cause the processor to obtain data originating from one or more sensors proximate to the location. A trained activity-based detection model may identify an activity at the location and perform a determination of a service to be offered at the location based on the detected activity. The system may then send a message to a user offering the service to the user, and in response to receiving an authorization accepting the service from the user, cause the service to be implemented at the location, which may include classifying the service as a service type, matching the service type to a service provider, and sending a notification to the service provider.

The present invention relates to the technical field of new energy vehicles, and specifically to a load balancing and scheduling method for charging piles of new energy vehicles based on intelligent algorithms, comprising the following steps: S1, data collection: real-time collection of operating data of charging piles; S2, charging demand prediction: prediction of the charging demand of each charging pile within a predetermined time in the future through a graph convolutional network model; S3, charging pile spatial scheduling optimization: spatial optimization of the scheduling of charging piles according to the geographical distribution of charging piles, the flow of surrounding vehicles and the results of charging demand prediction; S4, load scheduling and resource allocation: based on the results of charging demand prediction and charging pile spatial scheduling optimization, combined with the load of the power grid, resource allocation is performed. The present invention improves the utilization efficiency of charging pile resources, optimizes the layout of the overall charging network, and ensures the fairness of charging services and the balance of power grid load.

The invention discloses a safety monitoring system of a liquid cooling over-charging pile, and relates to the technical field of over-charging pile monitoring, the system comprises a data acquisition module, a data processing and analysis module, a dynamic model construction module, a temperature prediction module and a safety early warning module; according to the method, the dynamic model is constructed through the long short-term memory network LSTM, the complex nonlinear relation and the time sequence dependence of the multi-dimensional data are mined by using the gating mechanism of the dynamic model, the accurate characterization of the operation state of the liquid cooling over-charging pile is realized, the actual operation state of the equipment can be accurately described, the temperature data time sequence modeling is performed through the LSTM, and the accuracy of the temperature data time sequence modeling is improved. Parameters such as multi-source temperature and cooling liquid flow are fused, real-time prediction of the temperature change trend is achieved, the defect that a traditional algorithm is insufficient in temperature time sequence dependence capture is overcome, temperature abnormity can be recognized in advance, a safety threshold value is dynamically adjusted through a fuzzy logic algorithm, and self-adaptive threshold value adjustment is achieved in combination with parameters such as charging power. The problem that a traditional fixed threshold value is poor in adaptability is solved, and the early warning accuracy is improved.

The invention relates to the technical field of charging pile group load intelligent regulation and control, and particularly discloses a charging pile group load intelligent regulation and control method and system based on dynamic power equalization, and the method comprises the steps: collecting the oil temperature of a transformer in real time, calculating a total output power threshold value, and correcting the actual output power of a charging pile through combining the line loss and the equipment aging degree; a multi-dimensional load regulation and control decision matrix including user priority, equipment health degree and power regulation sensitivity is established, a dynamic power strategy is allocated through matrix weight, power allocation is inclined to high-priority users and health equipment, emergency power allocation and progressive readjustment during newly-added demands are supported, and the power regulation and control efficiency is improved. Impact of sudden power change on a power grid is avoided, system stability is ensured when newly added loads are accessed, refined intelligent regulation and control of loads of the charging pile group are finally realized, transformer safety, charging efficiency and user experience are improved, and power grid stability and resource optimization configuration are ensured.

The invention relates to the technical field of charging pile power distribution, and discloses a charging pile dynamic power distribution method and device based on load prediction, and the method comprises the steps: carrying out the system identification analysis of the real-time operation data of a charging pile, and constructing a charging pile system small signal model; training to obtain a low-sensitivity load prediction model and a high-sensitivity load prediction model, and generating charging load prediction data; performing control optimization calculation to obtain a dynamic power compensation control parameter; genetic optimization and multi-stage power distribution are carried out, and a multi-stage power distribution scheme including a conventional load distribution mode and a peak load distribution mode is obtained; power limitation is carried out by reducing charging power of non-priority users, load transfer is carried out through power coordination of adjacent charging piles, an emergency power adjustment control instruction is obtained, a closed-loop optimization mechanism is adopted, and stable operation of a charging pile system under various working conditions is guaranteed through parameter online optimization and dynamic adjustment of a control strategy.

The present disclosure describes an apparatus comprising a first set of inlet ports to connect with a vehicle charging station, the first set of inlet ports configured to receive first power from a vehicle charging station; a second set of inlet ports to connect with an external power source, the second set of inlet ports configured to receive second power from the external power source; a first set of outlet ports to supply the first power from the vehicle charging station to a first vehicle in a first configuration and the second power from the external power source to the first vehicle in a second configuration; and a second set of outlet ports to direct the first power from the vehicle charging station to a second apparatus in the second configuration, wherein the second apparatus is configured to supply the first power to a second vehicle.

An electrified fire fighting vehicle includes a chassis, a front axle coupled to the chassis, a rear axle coupled to the chassis, a water pump, an energy storage system, an engine, an electric motor coupled to the engine and the energy storage system, and a power divider positioned between the electric motor, the water pump, and the rear axle. The power divider facilitates selectively driving the water pump and the rear axle with the engine and the electric motor.

A robotic emulation device includes a processor, memory and multimedia interfaces to capture a video signal from a target device, analyze it, and transmit manipulation control signals to the target device input by emulating a peripheral input device, wherein the manipulation control signals determine manipulation or selection of a target interface control associated with a first user interface encoded and transmitted in the video signal. Furthermore, the robotic emulation device may be connected via an embedded transceiver to at least one computer or computer tenant for relaying the captured video data for analysis, and based on the analysis, applying inferred manipulation semantics.

A vehicle includes a chassis including a frame rail extending longitudinally, a tractive element coupled to the chassis, a cab coupled to the chassis and configured to support an operator, an electric motor configured to drive the tractive element to propel the vehicle, a battery configured to supply electrical energy to the electric motor, a heat exchanger, a cable electrically coupling the electric motor and the battery, and a conduit fluidly coupling the battery and the heat exchanger. The cable and the conduit extend laterally from the battery to the frame rail, and the cable and the conduit extend longitudinally along the frame rail.

Examples relate to vehicle battery systems comprising a base unit connectable to a vehicle's electrical system, a removable modular replaceable battery, and a secondary battery within the base unit. The base unit is configured to interchangeably use modular replaceable batteries of different chemistries. Control circuits optimize charging and discharging parameters based on battery chemistries. The secondary battery provides power when the modular battery is removed. The system includes thermal management, compatibility with lead-acid charging systems, and mobile base unit functionality. The modular battery can power external devices and can foreseeably store energy up to and beyond 1 kWh. The system operates at various voltages suitable for different vehicle types.