46 results about "Charge model" patented technology

The invention relates to an electric vehicle V2G scheduling method considering consumption of photovoltaic power and off-peak power, and the method comprises the steps: building an EV disordered charging model based on a user driving behavior in a power distribution network system containing photovoltaic power generation; establishing peak clipping-oriented price excitation; combining the established EV disordered charging model and price excitation to construct a photovoltaic and EV cooperative power generation optimization scheduling model; solving the photovoltaic and EV cooperative power generation optimal scheduling model by adopting an improved multi-target particle swarm algorithm, and outputting to obtain an EV ordered charging and discharging strategy; and correspondingly controlling the charging and discharging behaviors of the EV according to the EV ordered charging and discharging strategy. Compared with the prior art, the method can reduce the load variance of the power grid, achieves the efficient utilization of photovoltaic and off-peak power, and improves the V2G income of the electric vehicle.

Disclosed is a method for constructing an optimal scheduling model of an integrated energy system considering virtual thermal storage, which relates to the technical field of electric power systems. The method includes: establishing an operation model of an apparatus, a demand response model of an energy load, a charging model of an electric vehicle, and an energy flow model of a heat supply network of the virtual thermal storage in the integrated energy system; processing, according to a second-order cone relaxation method, a direct current power flow model of a power distribution network, and constructing the optimal scheduling model of the integrated energy system by taking minimization of total cost of an energy operator as an objective, thereby coordinately and optimally scheduling a plurality of energy sources.

The invention discloses a hydrogen fuel cell optimization algorithm based on particle swarm optimization of an integrated system, and belongs to the technical field of micro-grid energy management. An existing hydrogen fuel cell has the problems of low fuel utilization rate, slow response speed and the like under a dynamic load condition. In order to solve the problem, a hybrid energy system topology consisting of a photovoltaic (PV), a proton exchange membrane fuel cell (PEMFC), a lithium ion battery (BAT) and a super capacitor (SC) is constructed, and mathematical models, including a photovoltaic output power model, a fuel cell voltage model and a cell state-of-charge model, of all energy units are established. A PI controller is designed for a fuel cell, and a proportional gain KP and an integral gain KI of the fuel cell are dynamically adjusted through a particle swarm optimization (PSO) algorithm. Specifically, PI parameters are coded into particle position vectors, a multi-target fitness function is constructed, and hydrogen consumption, system stability and oxygen utilization rate are comprehensively considered; and a dynamically adjusted inertia weight mechanism is adopted to guide particle search, and iterative optimization is realized. After optimization, parameters are written into a PI controller in real time, and the hydrogen and air flow is accurately adjusted. Meanwhile, a continuous monitoring mechanism is introduced, when the photovoltaic power fluctuation exceeds 10%, re-optimization is automatically triggered, and closed-loop control is formed. The method improves the fuel utilization rate and the reaction rate of the fuel cell, is suitable for the dynamic load micro-grid, and improves the overall efficiency and stability of the system.

A preconditioning system (200) and a method (100) for optimal preconditioning of EVs in a depot. The method (100) includes determining a preconditioning energy and a preconditioning duration for performing preconditioning of a cabin and a battery of EV and determining a preconditioning availability matrix for the EV based on the preconditioning energy and the preconditioning duration. The method also includes determining a plurality of preconditioning constraints associated with the preconditioning of the battery and the cabin of the EV and determining a charging model based on the plurality of preconditioning constraints and the preconditioning availability matrix for the EV. Further, the method also includes generating a charger profile comprising a plurality of power set points associated with the preconditioning of the cabin and the battery of the EV and causing to display the determined charging profile on a screen of a charger (1000).

The invention discloses a multi-antenna charger charging scheduling method under energy budget constraint. The method comprises the steps of defining a network model, a charging model and a charging utility model; formalizing a multi-antenna unified control charger scheduling problem under the energy budget constraint; obtaining candidate directions with a limited number of multi-antenna chargers by using a dominating coverage set extraction method; converting a multi-antenna unified control charger scheduling problem under the energy budget constraint; and calling a multi-antenna charger antenna switch unified control scheduling algorithm under the energy budget constraint to calculate the direction and switch scheduling scheme of the multi-antenna charger. According to the invention, the direction and the on-off state of the multi-antenna charger are adjusted in multiple time periods of the whole charging process, so that the overall charging effect of the sensor is maximized in a limited energy scene, and the energy utilization efficiency of the multi-antenna charger when the multi-antenna charger executes a charging task is improved.

This invention discloses an online measurement device and method for coal dust parameters based on multi-physics field fusion and adaptive decoupling. The device includes: a co-location fusion sensing probe, which integrates a charge sensing module and a microwave resonant sensing module, the microwave resonant sensing module operating at a frequency of 1.0-5.0 GHz; an adaptive decoupling calculation unit, equipped with an adaptive decoupling algorithm, configured to simultaneously receive electrostatic and dielectric characteristic signals, identify coal type characteristic parameters online through a pre-stored dielectric characteristic-coal type parameter mapping relationship, substitute these parameters into the charge signal physical model, and decouple and calculate the estimated coal dust concentration and flow rate values ​​to eliminate the influence of coal type through the online parameter identification method; and a data communication unit for outputting the estimated coal dust concentration and flow rate values. This invention, through online identification of coal type characteristics using microwave dielectric measurement and dynamic correction of the charge model, fundamentally eliminates measurement drift caused by changes in coal type, achieving long-term calibration-free, high-precision online measurement.

The application belongs to the technical field of magnetic parameter measurement, and discloses a device and a calculation method for testing magnetic force in each direction between permanent magnets. The device uses an electric sliding table and a vertical sliding table to construct a three-dimensional motion platform, and collects the magnetic force of a tile-shaped permanent magnet under different spatial displacements in real time through a sensor. The device can not only meet the force testing needs of the inner / outer rotor permanent magnet under the axial, radial and tangential misalignment states, but also can adapt to different sizes of the inner / outer rotor permanent magnet by adjusting the clamp. Meanwhile, the magnetic force calculation method proposed by the application takes the equivalent magnetic charge model under the cylindrical coordinate system as the theoretical basis, and accurately solves the three-dimensional magnetic force of the inner / outer rotor permanent magnet under any spatial relative position through quadruple integration. With the help of the testing device and the calculation model proposed by the application, the precise measurement and theoretical verification of the magnetic force in each direction of the permanent magnet can be realized, the calculation is simple, and the degree of coincidence is high. The testing and calculation method has engineering universality and convenience.

The invention discloses a community alternating current charging pile load optimization method, which comprises the steps of obtaining historical basic load data, alternating current charging pile information, electricity price information and user orders of a target community, constructing a neural network prediction model, and further predicting current load data; constructing an ordered charging model containing an ordered charging target function and constraints according to the predicted load data and the lithium battery charging characteristics; searching an optimal solution of the model through a preset optimization algorithm, and generating an optimized charging scheme; and outputting the optimized charging scheme to the target alternating current charging pile and executing the optimized charging scheme through the target alternating current charging pile. According to the community alternating current charging pile load optimization method, on the premise that a user does not need to provide battery parameters, scheduling can be implemented according to charging time, electric quantity requirements and other basic information, off-peak charging is guided through an electricity price mechanism, the overload risk of a transformer is reduced, and the service life of the transformer is prolonged. Unification of power grid load balance, user cost optimization and safe and stable charging process is realized.

The invention provides a battery module state estimation method considering functional state dynamic characteristics, and relates to the technical field of battery management. The method comprises the following steps: collecting characteristic parameters of a single battery, and calculating polarization voltage in the battery; according to the factors affecting the state estimation of the battery module, a state-of-charge model representing the short-time scale characteristic of the battery and a state-of-health model representing the long-time scale characteristic of the battery are constructed respectively; establishing a functional state dynamic model of the single battery and a state estimation model under a dual-time scale; and expanding the functional state dynamic model of the single battery and the state estimation model under the dual-time scale to a battery module level, and establishing the functional state dynamic model of the battery module and the state estimation model under the dual-time scale so as to calculate the state estimation value and the functional state of the battery module. According to the invention, the accuracy and dynamic response capability of battery state estimation are effectively improved, and a basis is provided for long-term health management of the battery.

The invention belongs to the technical field of energy regulation and control, and provides an energy storage regulation and control decision method and system based on energy dynamic balance, and the method comprises the steps: constructing an energy storage equipment feature set, carrying out the numerical conversion of state features in the energy storage equipment feature set, and obtaining key energy storage features; the method comprises the following steps: constructing an equivalent state-of-charge model, an intra-day time-phased capacity measurement and calculation model and a system power model of an energy storage system, setting balance conditions of the energy storage system, and carrying out quantitative solution on key energy storage characteristics to obtain quantitative data of energy storage nodes; according to the quantized data and an energy balance grading rule, an alternative regulation and control scheme is determined through a multi-energy storage system regulation and control model; and setting regulation and control constraint conditions, constructing a regulation and control cost model of the energy storage system participating in the power grid, and screening the alternative regulation and control schemes to obtain an energy storage regulation and control decision scheme. According to the scheme, the regulation and control precision is effectively improved, the energy dynamic balance of the energy storage node network is realized, and the regulation and control effect is better.

The invention provides a dynamic charging mode for a battery swap station, which comprises a dynamic charging architecture and a dynamic charging device, and a plurality of data acquisition systems, a dynamic pricing model, a user-battery matching optimization module and a block chain transparent charging system are arranged between the dynamic charging architecture and the dynamic charging device. According to the invention, by fusing the Internet of Things and the block chain technology, the charging mode of the battery swap station is redefined, the battery health degree, the user behavior and the environmental factors are incorporated into a dynamic pricing system, the spanning from extensive charging to accurate value exchange is realized, and the fairness, economy, intelligence and transparency of charging of the battery swap station are improved.

The invention discloses a ship underwater water pressure field depth conversion method, which comprises the steps of constructing a ship electrostatic field conversion model based on a point charge model; the influence of the interface effect on a ship pressure signal under the shallow sea environment condition is equivalent to the influence of the interface effect on a ship corrosion related electrostatic field, and a model considering the influence of the medium effect on the ship electrostatic field is established; according to the electrostatic field model, a parallel equivalent source is constructed for a ship water pressure field along the longitudinal directions of a port and a starboard of a ship body based on a source-sink model, and a three-dimensional flow field problem is converted into an equivalent source model distribution problem; establishing a pressure signal inversion method of a point source on the basis of considering the influence of an interface effect on a ship pressure field signal and a source-sink model; the pressure signal model is solved, and then the pressure value of any field point is inverted; a multi-source coupling inversion system with physical interpretability is established, and the signal reconstruction precision under different water depth conditions is remarkably improved by introducing a constraint optimization algorithm; and the actual engineering requirements are met.

The invention relates to the technical field of power system optimization scheduling, and discloses an energy collaborative optimization method for a virtual power plant to participate in multiple power services, which comprises the following steps: step A, modeling optical storage and charging resources in the virtual power plant based on fuzzy opportunity constraints to obtain an optical storage and charging model and a resource operation characteristic model; b, evaluating the aggregation adjustment capability of the virtual power plant based on the Minkowski diagram and the optical storage charging model to obtain an aggregation adjustable domain of the virtual power plant; and step C, based on the resource operation characteristic model and the virtual power plant aggregation adjustable domain, establishing an energy collaborative optimization model for the virtual power plant to participate in the multi-element power service, solving the energy collaborative optimization model, and based on a solving result, constructing a joint declaration strategy for the virtual power plant to participate in the multi-element power service.

The invention discloses a method, a system, equipment and a medium for optimizing light storage and charging cooperative scheduling of a power distribution network in a mountainous area city, and belongs to the technical field of light storage and charging scheduling of the power distribution network, and the method comprises the steps: predicting the photovoltaic power generation power and the load power; establishing an uncertainty set; establishing a photovoltaic power generation model, an energy storage system model and an electric vehicle charging model; constructing a multi-objective optimization model comprising an operation cost objective function, a network loss objective function and a power balance constraint condition; solving the multi-objective optimization model by adopting a robust optimization method to obtain a scheduling scheme; performing rolling optimization on the scheduling scheme by adopting a model prediction control strategy, and updating a control decision according to the real-time operation state of the power distribution network; and outputting the charging and discharging power control quantity of the energy storage system and the charging power control quantity of the electric vehicle charging pile. According to the method, the operation cost, the network loss and the power balance constraint are comprehensively considered, so that the overall performance optimization of the system is realized.

The application discloses a charging remaining time confirmation method, device and vehicle, relates to the battery charging technical field, and discloses a charging remaining time confirmation method, which comprises the following steps: obtaining current charging data of a battery pack, wherein the current charging data comprises a charging rate and an SOC value; obtaining a temperature rising charging time and a temperature rising charging end SOC value based on the current charging data and a first charging model; obtaining a constant temperature charging time based on the temperature rising charging end SOC value and a second charging model; and confirming a charging remaining time based on the temperature rising charging time and the constant temperature charging time.According to the basic characteristics of the temperature in the low-temperature fast charging process, the charging remaining time is calculated in sections, the fast and accurate calculation of the fast charging remaining time is realized in combination with a fitting method and given data, and the overcomplicated calculation and the model building and calculation of heat generation, heat conduction and heat dissipation are avoided.

This invention discloses a method for establishing and extracting a charge model for HEMT transistors based on GaN technology, comprising: obtaining the gate-channel voltage based on externally input gate voltage, drain potential, and source potential; obtaining the gate charge based on gate charge density, number of gate elements, gate length, and width of a single gate element; obtaining the drain-source potential difference based on the drain potential and source potential; establishing a charge model based on the gate-channel voltage, gate charge, and drain-source potential difference to characterize the correlation between charge value and drain-source voltage after the transistor device is turned on; and differentiating the charge value of the charge model to establish a capacitance model to characterize the correlation between gate-source capacitance, gate-source voltage, and drain-source voltage. This invention supports the charge / capacitance description of HEMT transistors based on GaN technology, especially the description of the relationship between gate-source capacitance, gate-source voltage, and drain-source voltage after the channel is turned on, with high model accuracy.

This invention discloses a reverse simulation method and platform for the dynamic operation of an electric vehicle's powertrain. The method includes building a physical simulation model of the electric vehicle's powertrain, including a charging model, a temperature model, a battery model, a vehicle dynamics model, and an energy transfer model. Injecting collected real-world driving data into the model allows for rapid reverse simulation of the energy transfer, consumption, and battery degradation of an electric vehicle over a long period of real-world driving. This method enables electric vehicle consumers and manufacturers to quickly simulate and evaluate the vehicle's energy transfer process and the impact of vehicle usage factors on energy consumption and battery life.

The present application relates to electric vehicle charging technical field, especially to electric vehicle charging method, device and related equipment based on time-of-use electricity price, method includes: according to the travel data of electric vehicle constructs electric vehicle load model;Based on the electric vehicle load model, with the minimum peak-valley difference of total power load as objective function, the ordered charging model of electric vehicle is constructed;Establish the constraint condition corresponding to the ordered charging model of electric vehicle;Based on the objective function and the constraint condition, the ordered charging model of electric vehicle is solved by artificial bee colony algorithm.The present application can reduce the peak-valley difference of power grid, slow down the power grid load fluctuation caused by electric vehicle disorderly charging, improve the stability of power grid, and guide electric vehicle users orderly charging.

A method for scheduling electric vehicle charging based on two-dimensional graphic filling, specifically a method for scheduling electric vehicle charging based on two-dimensional graphic filling; the present invention first obtains charging history data of each existing scheduling area and municipal electricity consumption history data of each scheduling area, and constructs a charging model and a power consumption model based on the acquired historical data, and then fills the constructed charging model and power consumption model in a two-dimensional graphic filling manner, and finally outputs a filling effect diagram, which is used to schedule electric vehicle charging; the present invention can combine municipal electricity consumption and electric vehicle charging, and reasonably schedule charging for each scheduling area on the basis of avoiding exceeding the municipal electricity consumption limit, thereby ensuring the safety of municipal electricity consumption and reasonably planning the scheduling effect of electric vehicle charging. In addition, it can also be continuously updated to ensure the rationality of charging scheduling.

The present invention discloses a method and device for planning a charging route for electric vehicles. By integrating vehicle status, user habits and preferences, and historical trajectory data, a constant current and constant voltage segmented charging model is constructed to accurately calculate the charging time cost, and a hierarchical analysis method is used to quantify the user's charging preference weight. A set of accessible charging routes is generated based on the remaining mileage, and the routes are scored and ranked using the TOPSIS multi-criteria decision model to achieve personalized recommendations. The device integrates BMS power monitoring, CAN bus communication, and cloud computing modules to support real-time road condition feedback and personalized route push. The system dynamically corrects charging curve parameters and preference models by recording user selection behavior, forming a closed-loop optimization mechanism. Compared with traditional solutions, the present invention takes into account both charging efficiency and user convenience, reduces decision-making complexity, and is suitable for intelligent charging decision support in dynamic traffic environments.

Receive, by a price calculation microservice, tenant user input from a particular tenant of a multi-tenant system. Generate, by the price calculation microservice, a particular charge model based on the tenant user input. Detect, by a usage microservice, a usage event associated with a customer of the particular tenant of the multi-tenant system. Receive, by a rating microservice, a rating request, the rating request being received in response to the detecting the usage event. Calculate, by the price calculation microservice in response to the receiving the rating request, a usage charge for the usage event based on the particular charge model. Store, by the rating microservice, the usage charge. Generate an invoice based on the usage charge stored by the rating microservice. Provide the invoice to the customer of the particular tenant of the multi-tenant system.

The application discloses a regional electric vehicle charging load prediction and management method and device and a storage medium, and relates to the technical field of electric vehicle load interactive operation management. The method comprises the following steps: constructing a regional traffic road model, a vehicle travel space-time model and an electric vehicle charging model; determining whether an electric vehicle is charging at a node based on the relative state of charge of the electric vehicle, and combining the regional traffic road model, the vehicle travel space-time model and the electric vehicle charging model to construct charging load space-time prediction models for nodes and regions respectively; partitioning a power network and constructing a distributed operation management model based on the partitioning; and solving the distributed operation management model based on charging load prediction results obtained by the charging load space-time prediction model to perform power distribution management. The application improves the accuracy of electric vehicle charging load prediction and reduces the complexity of prediction.

1. The name of this design product: pet nail clippers. 2. Purpose of this design product: used for trimming pet nails, and has functions such as file design and lighting design. 3. The key design point of this product lies in the overall shape. 4. The picture or photo that best illustrates the key points of the design: Design 1 Stereoscopic Figure 1. 5. Designate Design 1 as the base design. 6. Other situations that require explanation: Design 1 is a USB charging model, and Design 2 is a battery model.

The application provides a magnetic signal quantitative detection method, comprising the following steps: step 1, establishing a classical magnetic charge model, and defining a transmission compensation factor based on the classical magnetic charge model; step 2, constructing a radial transmission compensation factor; step 3, constructing an axial transmission compensation factor; step 4, describing the additional attenuation of the static magnetic shielding effect of the ferromagnetic material by the radial transmission compensation factor obtained in step 2 and the axial transmission compensation factor obtained in step 3, and introducing the classical magnetic charge model in step 1 for improvement to obtain an improved magnetic charge model, so as to solve the problem that the magnetic signal transmission process is affected when passing through the ferromagnetic wall thickness in the prior art, the amplitude of the magnetic flux leakage detection signal is reduced, the damage degree of the defect is underestimated, and the safety and service life of the material are incorrectly judged.

The present application relates to a kind of electric vehicle day-ahead scheduling strategy based on conversion economic target, comprising the following steps: S1: obtaining basic information;S2: establishing electric vehicle charging model;S3: whether the electric vehicle scheduling time obtained above exceeds constraint condition is analyzed;At the same time, whether the calculated number of electric vehicles is greater than N is judged, if less, return to S2 continue to calculate until the scheduling plan of N electric vehicles is completed;S4: the objective function of electric vehicle day-ahead scheduling is calculated, and the optimized day-ahead scheduling plan is obtained.The present application makes full use of the flexibility of electric vehicle load, considers the interests of grid side and user side, and limits the objective function with minimum variance, reduces the negative impact of electric vehicle connected to the grid, and the technical index of grid load peak-valley difference is converted into the economic index of minimum user charging cost.The strategy guarantees the optimal economy of user, reduces the grid load peak-valley difference at the same time, and verifies the effectiveness of the strategy.

A modeling method of a surface potential-based GaAs process pHEMT compact model comprises: establishing a surface potential-based GaAs pHEMT compact intrinsic model; establishing a surface potential-based GaAs pHEMT compact extrinsic model; combining the physical structure and behavior mechanism of the GaAs pHEMT device, and establishing a compact model and a model topological structure based on intrinsic and extrinsic model equation architectures; describing the surface potential-based GaAs pHEMT compact model by using a Verilog-A language; and giving a compact model parameter extraction method. The method solves the problems that the existing GaAs PHEMT model is difficult to describe the characteristics of a complex and non-uniform surface potential function, the approximate solution is inaccurate, and the channel charge model equation is discontinuous and cannot be used for nonlinear circuit simulation.

The invention is suitable for the technical field of electric vehicle scheduling, and provides an electric vehicle multi-stage charging optimization scheduling method and device, and the method comprises the steps: obtaining multi-stage constant-voltage charging data of an electric vehicle; based on the multi-stage constant-voltage charging data, constructing an electric vehicle charging model; based on the electric vehicle charging model and the personalized charging demand of the user, constructing a multi-stage modular package model, and based on the multi-stage modular package model, determining a charging package module of the user; constructing a charging scheduling model based on the charging package module of the user, and determining a preliminary charging strategy based on the charging scheduling model; optimizing the preliminary charging strategy based on a multivariate hybrid priority algorithm, and determining an optimized charging scheduling strategy of the electric vehicle; and the optimized charging scheduling strategy is used for scheduling multi-stage charging of the electric vehicle. According to the invention, the charging scheduling of the load aggregator can be realized, the optimization of the personalized charging demand of the user can be satisfied, the orderly charging of the electric vehicle is realized, and the power grid pressure is reduced.

The invention discloses a light storage and charging integrated charging pile and a shared charging model construction method, relates to the technical field of charging pile operation optimization, and aims to solve the problems of inaccurate data processing, one-sided priority evaluation, rigid resource constraint and the like of the existing shared charging technology. According to the method, on the basis of time-space associated data of an optical storage and charging system, a user and a power grid, a high-quality dynamic data set is output through processing of a double-exponential weighting algorithm; on the basis, user charging priorities are quantified through a three-dimensional coupling algorithm, and a priority sequence is formed; combining the data and the sequence to construct an energy storage capacity and power grid interaction power constraint model dynamically adjusted along with the priority; a multi-objective optimization function of a priority adaptive weight is constructed, a charging pile distribution and power scheduling scheme is solved by adopting an adaptive particle swarm algorithm, full-process cooperation of data processing, priority evaluation, resource constraint, optimization objective and dynamic response is realized, and high efficiency, fairness and stability of shared charging are improved.

The invention discloses an electric vehicle charging model construction method considering a vehicle-road-network coupling relationship, and belongs to the technical field of electric vehicle charging, and the method comprises the steps: building a single electric vehicle model, a road network model and a power distribution network model based on the vehicle-road-network coupling relationship; based on the single electric vehicle model, analyzing the charging behavior of the electric vehicle user, and combining with the constraint of the power distribution network model to establish an electric vehicle charging demand prediction model based on the user charging behavior to obtain an electric vehicle charging demand; based on the road network model, combining a map application interface to obtain the distance between the electric vehicle and the charging station and the time required for going to the charging station; and coupling the charging demand, the arrival time and the like, and establishing a global electric vehicle charging model. According to the method, the limitation of traditional single-dimension research is broken through by establishing the vehicle-road-network three-dimensional coupling model, accurate prediction of the charging demand of the electric vehicle is facilitated, and the minute-level cooperation problem of the charging behavior and power grid dispatching is solved.

The application discloses a kind of orderly charging methods of multiple types of charging stations considering the optimization operation demand of distribution network, considering that different types of charging stations are distributed in city, according to the charging characteristics of electric vehicle users, different charging stations undertake different responsibilities and functions to meet the charging needs of different types of electric vehicle users.According to the difference of construction site, charging stations are mainly divided into three categories: private charging stations, public charging stations and fast charging stations.Based on the charging characteristics of electric vehicles, combined with the operation characteristics of various charging stations, an effective charging station orderly charging planning strategy can be proposed, which is a multiple type charging station orderly charging model considering the optimization operation demand of distribution network, to achieve the charging of distribution network load ratio more or less, and to achieve the reasonable use of multiple type charging stations, to balance the network loss to the greatest extent under the condition of ensuring the normal operation of distribution network, to meet the effectiveness and sustainability of electric vehicle charging.