27 results about "Charge model" patented technology

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 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 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.

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.

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.

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 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 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.

The invention discloses a two-stage electric vehicle battery replacement optimization scheduling method based on a cloud battery replacement scheduling system. According to the scheduling method, a cloud EV two-stage power conversion scheduling system model is constructed by analyzing the mechanism of an EV power conversion scheduling system; secondly, optimization scheduling is carried out on the EV through two-stage optimization, in the first stage, a day-ahead power conversion scheduling plan is output with the purpose of maximizing the power conversion request response rate, and bearing capacity evaluation is carried out; in the second stage, on the basis of the first stage, the time-of-use electricity price is considered, the charging cost is optimized, and an ordered charging model with the purpose of minimizing the charging cost is established, so that the purpose of reducing the operation cost is achieved. Compared with a traditional nearby guiding strategy, the method can more effectively improve the power conversion success rate and reduce the BCSS charging cost in a large-scale power conversion scene, and provides a solution for large-scale EV power conversion scheduling in a smart city background.

The application belongs to the technical field of new energy, and particularly relates to a charging station low-power capacity orderly charging management system. The system comprises: a charging station orderly charging model; the charging station orderly charging model is configured to: receive a user input charging limit state and a chargeable waiting time; obtain N chargeable time periods of an electric vehicle of the user based on the chargeable waiting time; determine a required charging time period number of the electric vehicle based on the charging limit state and the N chargeable time periods; obtain a charging load margin of each chargeable time period; wherein the charging load margin is used to represent the power consumption load of the chargeable time period; and determine a charging strategy of the electric vehicle based on the charging load margin of each chargeable time period, the rated charging power of a charging pile of the charging station and the required charging time period number of the electric vehicle. In this way, power grid resources can be reasonably utilized, both the waste of power grid resources can be avoided and the load peak-valley difference can be reduced.

The invention discloses an equipment parameter estimation method, and particularly relates to a capacitance estimation method and device based on improved variational Bayesian Kalman filtering. The method comprises the following steps: estimating a prior distribution parameter at a moment k based on a posterior distribution parameter at a moment k-1; n times of fixed point iteration operation are executed based on the measurement information of the moment k and the prior distribution parameters of the moment k, and posterior distribution parameters of the moment k are obtained; determining an estimated value of the state vector at the moment k based on the posterior distribution parameter at the moment k; calculating the capacitance of the capacitor based on the estimated value by using a pre-built pre-charging model to obtain an estimated capacitance; wherein the measurement information comprises capacitor voltage, the state vector comprises model parameters of the pre-charging model, and the measurement noise covariance matrix is a covariance matrix of random noise in the measurement process of the measurement information. The method can improve the accuracy of capacitance estimation of the capacitor.

The application discloses a calibration method for improving the precision of a small and medium load charging model, comprising the following steps: carrying out a charging model scanning test on an engine test bench to obtain original charging model scanning data; cleaning and arranging the original scanning data to obtain qualified data; classifying the cleaned and arranged data, and dividing the data into small load, medium load and large load according to a predetermined threshold; weighting the data points of different loads by different multiples; performing fitting calculation by using the weighted data, adjusting the charging model parameters through multiple iterations, continuously reducing the RMSE, and finally converging to obtain a group of charging model parameters; importing the finally obtained charging model parameters into an ECU, carrying out verification work of the charging model on the engine test bench, and judging whether the main charging model precision of the engine under the universal characteristic condition meets the requirements. The application realizes great improvement of the precision of the small and medium load charging model.

The application belongs to the technical field of semiconductor integrated circuits, and particularly relates to a calculation method of a bulk resistance of a partially-depleted silicon-on-insulator metal-oxide-semiconductor field effect transistor (SOI MOSFET). According to the structure characteristics of the H-type gate MOSFET, the depletion charge amount generated by the front gate voltage and the back gate voltage in the bulk silicon of the device is solved by using the principle of the capacitive induced charge. Then, according to the source pn junction and the drain pn junction model, the depletion charge of the pn junction in the bulk silicon is obtained. On this basis, a basic bulk multi-charge model is established, and all the residual multi-charge amounts in the bulk are obtained. Then, according to the charge movement law, the bulk silicon resistance model is established, so that the bulk resistance can be quickly and accurately obtained. The model results are highly consistent with the simulation and experimental results. The application has clear physical concept, is easy to calculate, and has high calculation precision, and provides an effective calculation method for extracting the key parameters of the SOI MOSFET.

A method and system for computing a mission plan for at least one aircraft powered by electric energy or by electric and fuel generated energy. The system is configured to receive input information including aircraft information, infrastructure information and flight routes information, and to implement modules for acquiring mission specifications, acquiring user preferences, and encoding the user preferences as a multi-criteria scoring scheme, selecting at least one electric energy consumption model and one electric energy charge model, based on the mission specifications, executing at least one multi-criteria constrained optimization algorithm, trained by machine learning, for mission planning, wherein each optimization algorithm is given as objective the multi-criteria scoring scheme, the optimization algorithm being further provided with the input information, mission specifications and the selected energy models, each optimization algorithm providing as output a mission plan including, for each aircraft, an electricity charging duration at each mission terminal.

The invention belongs to the technical field of magnetic parameter measurement, and discloses a testing device and a calculation method for magnetic force in all directions between permanent magnets. According to the device, a three-dimensional motion platform is constructed by using an electric sliding table and a vertical sliding table, and magnetic forces of a tile-shaped permanent magnet under different space displacements are acquired in real time through a sensor, so that the stress test requirements of an inner / outer rotor permanent magnet in axial, radial and tangential staggered states can be met; and the fixture can be adjusted to adapt to inner / outer rotor permanent magnets with different sizes. Meanwhile, according to the magnetic force calculation method provided by the invention, an equivalent magnetic charge model under a cylindrical coordinate system is taken as a theoretical basis, and the three-dimensional magnetic acting force of an inner / outer rotor permanent magnet at any space relative position is accurately solved through quadruple integration. By means of the testing device and the calculation model, accurate actual measurement and theoretical verification of the magnetic force in each direction of the permanent magnet can be achieved, calculation is easy and convenient, the goodness of fit is high, and the testing and calculation method has engineering universality and convenience.

The application provides a kind of multi-microgrid system optimization scheduling method considering coordinated charging of electric vehicle and carbon trading, belongs to the technical field of integrated energy system optimization scheduling, first proposes a dynamic urgency index reflecting user charging demand, establishes an EV coordinated charging model with the minimum peak-valley difference of total electrical load in microgrid as the target, then constructs a two-stage electric-gas, distributed power and cogeneration unit collaborative operation model, finally considers the existence of electricity-heat interaction in multi-microgrid integrated energy system, adds a reward and punishment type step carbon trading mechanism to constrain the carbon emissions of the system, further improves the low-carbon economy and economy of the system. The application can improve the low-carbon economic operation effect of the system; it can meet the charging demand of electric vehicle owners while reducing the peak-valley difference of total electrical load in microgrid; at the same time, it reduces the carbon emissions in microgrid while reducing the thermal-electric coupling relationship of cogeneration system.

A method for constructing an integrated energy system optimal scheduling model considering virtual heat storage, relating to the technical field of power systems. The method comprises: establishing operational models of working equipment of an integrated energy system, establishing an integrated demand response model for integrated energy loads and an orderly charging model for electric vehicles, and establishing a heating network energy flow calculation model considering virtual heat storage; on the basis of a mathematical theory, processing a direct current power flow model of a power distribution network, and by using a second-order cone relaxation method, converting general power flow constraints into mixed integer quadratically constrained programming constraints; and according to power flow constraints of the power distribution network, a heating network and a gas network of the integrated energy system, by taking minimization of the total operating cost of an integrated energy operator as an objective function, constructing an integrated energy system optimal scheduling model considering virtual heat storage. The method achieves accurate description of operating characteristics of integrated energy systems, so as to implement coordinated optimal scheduling of multiple energy sources and improve the economy, environmental protection property and reliability of the systems, thus providing support for efficient operation of the integrated energy systems.