24results about How to "Reduce charging costs" patented technology

The invention discloses an intelligent charging management system and method based on edge computing, and relates to the field of edge computation.The method comprises the steps that 1, an edge computing node module is arranged, the edge computing node module comprises a data acquisition unit and a data processing unit, data of a charging pile is acquired based on the data acquisition unit and the data processing unit, and the data of the charging pile is acquired; by introducing an edge computing node, computing and control part data of the charging pile are moved to the local; step 2, the edge computing node module uploads the collected data to a central control platform; 3, the central control platform receives and processes the data uploaded by the edge computing node, and based on the reservation time of the user, the current power grid load and the real-time electricity price, cyclic charging optimization scheduling is carried out in a cyclic iteration mode; and 4, after the edge computing node detects that charging is completed or the target electric quantity set by the user is reached, charging is automatically cut off, a charging record is uploaded to the central control platform, and long-term data analysis and strategy optimization are carried out.

The invention relates to the technical field of electric vehicle charging and discharging regulation and control, in particular to a large-scale electric vehicle charging and discharging rate multi-level optimization regulation and control method. Establishing a daily driving mileage prediction model by analyzing historical driving and charging data of the electric vehicle, and calculating a charging demand of the daily driving mileage according to the daily driving mileage prediction model; designing an optimization model based on linear programming based on the determined charging demand, and obtaining reasonable charging power of each electric vehicle in different time periods; on the basis of the determined charging demand, constructing a multi-level optimization model; under the guidance of the constructed multi-objective optimization model, a specific charging and discharging strategy of each electric vehicle is refined by adopting a heuristic algorithm; the charging power is dynamically adjusted by combining real-time data of the electric vehicle and the power grid; and based on the dynamic adjustment result, the charging power of different levels is coordinated, and multi-level cooperative control is realized. According to the large-scale electric vehicle charging and discharging rate multi-level optimization regulation and control method, efficient and stable operation of a charging system is achieved.

The present invention discloses a health status prediction and analysis method and system based on a charging pile, which relates to the technical field of charging pile management, including extracting data from a database system based on a central processing unit to obtain historical operation data of the charging pile; and obtaining complete historical operation data of the charging pile. The present invention determines whether the state of the charging pile is a healthy state by judging the temperature deviation of the charging pile, the charging deviation of the charging pile, and the factory parameter information of the charging pile. In addition, the state of the healthy charging pile is predicted to determine within what time the state of the healthy charging pile will become sub-healthy, and the state of the charging pile is monitored to avoid damage to the internal electronic components of the charging pile due to excessive temperature increase, thereby increasing the service life of the charging pile. Moreover, the charging rate of the charging pile is monitored to avoid the charging time of the charging pile being too long, resulting in abnormal energy loss, and reducing the charging cost.

The invention provides a mobile and fixed hybrid charging scheduling method and system, and a storage medium. The method comprises the steps of obtaining a charging request of at least one target vehicle; a space-time distribution model of a hybrid charging station is constructed according to the charging request, the hybrid charging station comprises a fixed charging pile FCP and a mobile charging pile MCV, and the total cost of the same target vehicle in the mobile charging mode and the total cost of the same target vehicle in the fixed charging mode are obtained according to the space-time distribution model; generating a target vehicle charging selection scheme based on a principle of lowest target vehicle charging cost; and if the target vehicle charging selection scheme is a mobile charging mode, constructing a target function by taking the minimum total operation cost of the hybrid charging station as an optimization target, and solving the target function to obtain an optimal scheduling scheme for selecting the mobile charging mode in the next day. According to the invention, the most economical charging selection scheme can be pushed to the customer, so that the user cost can be reduced, and the optimal scheduling of the MCV can be completed under the condition of selecting the mobile charging mode.

The embodiment of the invention provides a charging control method, device and equipment for an electric forklift. The method comprises the following steps: acquiring historical charging data of the electric forklift in a historical time period; processing the historical charging data and a preset peak-valley electricity price database based on a time sequence prediction model to obtain suggested charging time of a future time period; wherein the time sequence prediction model is obtained by training an initial neural network model through historical charging training data and a preset peak-valley electricity price database; the preset peak-valley electricity price database represents electricity price differences in different time periods; the historical charging data, the suggested charging time and a preset peak and valley electricity price database are processed based on a semantic model, and a charging control mode of the electric forklift is generated; based on the charging control mode, the electric forklift is controlled to be charged; wherein the charging control mode indicates the charging time period of the electric forklift. The method is used for achieving the effect of charging the electric forklift economically and efficiently.

The application discloses a kind of considering user false information's electric vehicle space-time scheduling optimization method and system, belong to electric vehicle optimization scheduling field.Scheduling center needs to minimize overall charging cost under the charging demand constraint of satisfying all electric vehicles, user once lies report demand, possibly will reduce own charging cost, but the charging cost of other electric vehicle users will rise, leading to the rise of total cost, to solve this problem, based on the space-time two-dimensional electric vehicle optimization scheduling framework, a new charging cost billing mechanism is designed, considering the influence of the charging strategy reported by user on optimization scheduling result, so that individual charging cost minimization and overall charging cost minimization tend to consistency.Not only reduce the charging cost of each user after participating in scheduling, but also avoid the phenomenon that user chooses to report false information to gain profit when participating in scheduling, better encourage user to participate in scheduling, improve optimization effect.

The application provides a kind of based on double-layer optimization model electric vehicle participates in power grid frequency modulation determination method and system, it is related to smart grid and electric vehicle technical field.The method comprises: based on the historical charging cost data of electric vehicle and the historical subsidy income data of participating in power grid frequency modulation, establishes first layer optimization model, determines the comprehensive income value of to be evaluated object, based on the time-of-use electricity price introduced random noise, the charging probability of to be evaluated object in different period is calculated, further determines the frequency modulation willingness evaluation value of to be evaluated object;Power distribution network frequency fluctuation model is constructed to be used to establish second layer optimization model, to quantify the evaluation based on the total load of power grid caused by to be evaluated object power grid fluctuation amount;According to optimization strategy, first layer optimization model and second layer optimization model are double-layer iteration optimization, and the frequency modulation participation evaluation value after optimization is obtained, to determine the frequency modulation participation evaluation value of to be evaluated object.The application realizes the charging cost of user side is lower while improving the stability of power grid.

The invention discloses an intelligent power dispatching unit and an intelligent power dispatching system thereof, and belongs to the crossing field of intelligent power grids, new energy storage and traffic infrastructures. The intelligent power dispatching unit comprises m charging piles, an energy storage system and a relay array; the relay array is arranged between the charging pile and the energy storage system and is used for power dispatching and coupling of the charging pile and the energy storage system; the energy storage system comprises n DC / DC chargers; the number of relays in the relay array is m (m + 3) / 2 + n, m is an integer greater than or equal to 2, n is an integer greater than or equal to 2, and m is an integral multiple of n. A plurality of charging piles are combined with an energy storage system and a related detection control system to form an intelligent power dispatching unit, so that the purposes of flexibly dispatching charging power, relieving peak electricity consumption pressure, charging an energy storage battery by utilizing photovoltaic or electricity price valley sections and reducing charging cost are achieved.

The utility model relates to a take-up track transfer type pile line split air mobile charging robot system, which comprises an air track, a walking module, a movable charger, a winding gun line module, a charger side butt joint module, a winding side butt joint module, a power taking module, a charging call response system and a control processing unit, the movable charger can move along the aerial track and change the track and steer under the driving of the walking module, a winding gun line module is arranged beside each parking space, the movable charger can achieve the purpose that any parking space can be in electrical butt joint with the winding gun line module, and therefore the charging service can be provided for an electric automobile below the movable charger. The utility model relates to the field of new energy vehicle charging, eliminates the one-to-one binding relationship between a movable charger and a charging gun line, greatly improves the utilization rate of charging resources, supports reservation charging and queuing charging, can take back the charging gun line after a user pulls out a gun, and is more convenient, more efficient, cleaner and safer.

This invention belongs to the field of power technology, specifically disclosing a method, device, equipment, and medium for autonomous charging and navigation of unmanned aerial vehicles (UAVs). It includes the following steps: scanning environmental information to obtain LiDAR data; identifying charging station landmarks from the LiDAR data and establishing a coordinate system; processing the coordinate system using the ICP method to obtain pose gain; acquiring the odometer information corresponding to the pose gain; fusing the odometer information and pose gain using the extended Kalman filter method to obtain an estimated pose; and using the estimated pose as input for a PID control method to complete navigation. This invention, by applying landmark recognition and positioning navigation technology, replaces the traditional manual charging method, greatly reducing the need for human resources, lowering UAV charging costs, and improving charging efficiency.

The invention discloses a wireless charging, positioning and locking system for an intelligent electric vehicle. The system comprises a charging receiving module, a communication pairing module, an intelligent monitoring center module, a wireless charging sending module, a positioning auxiliary module, a vehicle locking control module, a user interaction interface module and a payment module. The system can obtain the accurate position information of the electric vehicle in real time through the positioning auxiliary module, so that the problem that the traditional wireless charging system cannot position the electric vehicle is solved. A user can easily check the position of the electric vehicle through the user interaction interface module, the problem of lane occupation caused by disordered parking of the electric vehicle is avoided, the vehicle locking control module is tightly connected with the intelligent monitoring center, a vehicle locking mechanism of the electric vehicle can be automatically triggered according to an instruction of the intelligent monitoring center or a preset condition, and the user experience is improved. The electric vehicle is prevented from being stolen in the charging process, it is ensured that the electric vehicle is parked in order after being charged, and lane occupation and potential safety hazards are further reduced.

This application relates to the field of battery thermal management technology, and particularly to a method, device, vehicle, storage medium, and product for heating control of a power battery. The method includes: acquiring the current temperature, target state of charge (SOC), and initial SOC of the power battery during charging; determining the target temperature of the power battery based on the initial SOC and target SOC; calculating the temperature difference between the current temperature and the target temperature; and controlling the heating action of the power battery based on the temperature difference. This solves the problems of excessive energy consumption and increased costs in thermal management due to super-fast charging in new energy vehicle charging technology.

The application relates to the technical field of charging control, and discloses a charging cluster elastic charging method and system based on time-of-use electricity price driving. First, future time-of-use electricity prices are predicted, and charging time period constraints of vehicles are obtained, based on which elastic charging times of the vehicles are determined, and initial charging tasks are generated. During task execution, charging pile loads are monitored in real time, when the load of any charging pile is lower than a threshold value, the charging pile is marked as a schedulable charging pile, and charging tasks that have not been completed and whose original execution time period electricity prices are higher than current time electricity prices are screened in the elastic charging time. If there is a schedulable charging pile, the target task is adjusted from the original execution time period to the current time to start execution. The application triggers task adjustment by dynamically monitoring load states and electricity price differences, only migrates charging tasks when the electricity price is reduced, can effectively reduce charging costs, improves charging equipment utilization, and fully guarantees user vehicle use time constraints.

The present invention relates to a fast-charging load charging guidance method based on an adjustable hierarchical charging service fee, comprising: step S1, according to the regional road network and distribution network constraints, a fast-charging load prediction model based on the travel chain and the Monte Carlo method is established to predict the changes in the user's travel time and space trajectory and the fast-charging demand; step S2, a user charging position weighted decision model is established to make charging position decisions, and the fast-charging load of each charging station is counted; step S3, a regional hierarchical charging service fee adjustment model is constructed with the optimization goal of minimizing the absolute value of the sum of the voltage deviations of the distribution network nodes in the region, and the charging service fee is optimized and adjusted; step S4, based on the adjusted charging service fee, the user charging position weighted decision model is used to decide the optimal user fast-charging load charging position. Compared with the prior art, the present invention has the advantages of effectively guiding users to charge reasonably, reducing user charging costs, and improving the voltage of the distribution network.

The invention provides a charging control method and charging equipment. The charging control method provided by the invention comprises the following steps: when request charging information is received, sending a charging strategy request to cloud equipment according to the request charging information; determining reference charging power of the charging equipment based on a charging strategy fed back by the cloud equipment; acquiring actual grid-connected power between the alternating-current bus and the power grid and actual charging power of the charging equipment through the intelligent electric meter; determining redundant power according to the actual grid-connected power and the actual charging power; determining target charging power of the charging equipment according to the reference charging power and the redundant power; and generating a control signal according to the target charging power and sending the control signal to the charging equipment, wherein the control signal is used for adjusting the actual charging power of the charging equipment. According to the charging control method provided by the invention, the charging cost can be better reduced while the charging requirement of a user is met.

The utility model discloses a kind of multifunctional wireless charging equipment, comprising: top seat, with first charging contact surface;Base, inside is equipped with cavity, second charging contact surface is equipped in cavity, and cavity has opening on base, opening is equipped with the cavity door of movable connection, third charging contact surface is equipped on cavity door;The present application can realize three electronic devices charging simultaneously on the same equipment, for example, place mobile phone on top seat, receive wireless earphone in cavity, and unfold cavity door to support smart watch.Three charging areas do not interfere with each other and have high space utilization, effectively solve the defect that traditional equipment charging surface is single, significantly improve use efficiency and reduce the charging cost of multiple equipment users.

The invention relates to the technical field of electric vehicle charging control, and provides an electric vehicle time-sharing stepped charging control method and system based on a historical load, and the method comprises the steps: obtaining the historical load, time-sharing electricity price and electric vehicle charging load demands of a target power distribution area; calculating a time-sharing reference load based on the historical load; constructing a time-sharing stepped charging control model based on the time-sharing reference load, the time-sharing electricity price and the charging load demand of the electric vehicle; and solving the time-sharing charging control model by taking the minimum total charging cost and the minimum load fluctuation degree as targets to obtain an optimal control scheme, and controlling an electric vehicle charging strategy of a target power distribution area through the optimal control scheme. Compared with the prior art, historical load data are considered, the time-sharing charging control precision degree is improved, and the pressure of the peak period of the power grid is effectively relieved.

This invention proposes a two-stage optimization method for distribution networks integrating electric vehicles (EVs) and distributed generation. In the first stage, an orderly scheduling of EVs is achieved by establishing a user participation dispatch probability model and a dynamic electricity price model considering load. The optimized EV load is then superimposed onto the distribution network reconfiguration optimization model in the second stage. Finally, network reconfiguration and distributed generation output are used to reduce network losses and node voltage deviations across the entire distribution network. The proposed method reduces peak-to-valley load differences. Considering user participation rates, it not only reduces user charging costs but also improves node voltage levels and reduces network losses.

The application discloses an inland port electric transfer vehicle charging scheduling method, comprising the following steps: constructing a transfer vehicle task matching model with the purpose of maximizing the utilization rate of the transfer vehicle and completing all transfer tasks; constructing a transfer vehicle charging scheduling model with the purpose of minimizing the delay, maximizing the transfer vehicle efficiency and minimizing the charging cost; constructing an energy-traffic collaborative scheduling model through the transfer vehicle task matching model and the transfer vehicle charging scheduling model; taking the transfer task and the initial state of all transfer vehicles as the input of the energy-traffic collaborative scheduling model, solving the energy-traffic collaborative scheduling model with the input of the transfer task and the initial state of all transfer vehicles through a deep Q network algorithm, and generating an optimal energy-traffic collaborative scheduling scheme; and scheduling the transfer vehicle through the optimal energy-traffic collaborative scheduling scheme to complete the transfer task of the port. The application can improve the overall transportation efficiency of the port and reduce the transportation cost.

This application relates to the field of automatic control technology, and provides a charging method, system, device, and storage medium based on self-propelled charging devices. The method includes: a server responding to a user order sent by a user; determining the target service area where the vehicle to be charged is located based on the charging location information in the user order; determining whether there is an idle self-propelled charging device within the target service area; and, if an idle self-propelled charging device exists within the target service area, generating a movement command based on the charging location information and sending the movement command to the target idle self-propelled charging device. Because a movement command can be generated based on the charging location information and sent to an idle self-propelled charging device within the target service area upon receiving a user order, and the self-propelled charging device can move autonomously within the target service area, the power delivery process can be automated, thereby saving on the costs of delivery personnel and distribution vehicles, and ultimately reducing charging costs.

The invention discloses an ordered charging control system and method based on master-slave RS485 communication, and belongs to the technical field of ordered charging control. The system comprises a master device and a plurality of slave devices. The main device is connected with a gateway electricity meter in a station and collects an active power value in the station in real time. The master device is connected with the plurality of slave devices through an RS485 communication network; the master device is configured with a master device address, a slave device address range and a station output power threshold; and the slave equipment is configured with an equipment address. The method comprises the steps that a master device collects the total active power of a station and operation data of each slave device in real time; the master device determines the output power of each slave device based on a station output power threshold preset in advance in combination with the total active power and the operation data of each slave device; and the master device transmits the determined output power of each slave device to the corresponding slave device through an RS485 communication network. According to the invention, additional ordered charging equipment is prevented from being added to each charging station.

The invention relates to a parameter design method for an ultra-high-speed hybrid electromagnetic tripping device. An additional winding circuit of the electromagnetic tripping device comprises an additional winding, a capacitor and a plurality of groups of thyristor modules. According to the parameter design method, a simulation model is built according to the topology of the ultra-high-speed hybrid electromagnetic tripping device, expected tripping time is taken as a parameter design target, a characteristic parameter K1 related to an electromagnetic force action period and a characteristic parameter K2 related to capacitance energy are obtained through calculation in combination with a simulation result, and finally, according to the rated voltage Ur of a direct-current system, the parameter of the ultra-high-speed hybrid electromagnetic tripping device is calculated. And determining the optimal value of the pre-charging voltage Uc0 of the capacitor, and calculating to obtain the specific values of the capacitance value C and the number n2 of turns of the additional winding which need to be configured. The method is scientific and reasonable in design, parameter configuration optimization of the ultra-high-speed hybrid electromagnetic tripping device can be achieved, ultra-high-speed tripping is guaranteed, meanwhile, the capacitor charging cost is reduced, online capacitor charging is achieved, and the method has the advantages that multiple indexes such as the tripping speed, the tripping cost and the use convenience are considered.

The invention discloses a high-reliability data elastic storage management method compatible with multiple power markets, which comprises the following steps: carrying out generality extraction and difference analysis on an interaction scene, an interaction mechanism, resource characteristics and a data source, and establishing a unified data interaction model; constructing a network-operation two-stage collaborative interaction architecture based on a power grid system and an operator system; in combination with the interaction requirements of a power grid system and an operator system, mass multi-source heterogeneous data are accessed and collected and then processed, a mass data storage management system is formed, the functions of operator management and control, overall effect monitoring and the like are achieved, and development of vehicle network interaction aggregation management and control is completed. The invention belongs to the technical field of data management, and particularly provides a high-reliability elastic data storage management method compatible with multiple power markets, which is used for solving the problems of real-time performance, reliability and stability of mass multi-source heterogeneous data transmission and how to ensure the safety, stability and high efficiency of mass data storage.

The present invention discloses an intelligent charging control method and system, which determines the pre-charging demand by collecting vehicle information, battery information and historical charging information of electric vehicles, and determines the valley price period according to the short-term predicted load and peak-valley electricity price of the power grid in the region, and then determines the charging guidance information to guide the electric vehicles to be charged to charge in an orderly manner. The proposed orderly charging control method and strategy are expected to solve the problem that there is a lack of information interaction between existing electric vehicles and power grid charging control centers, which is prone to grid load fluctuations due to random charging, and increases grid energy loss. It is helpful to achieve peak-shaving and valley-filling of electric vehicle charging stations connected to the distribution network, thereby reducing the cost of charging and improving the overall economic benefits. At the same time, orderly control of reducing the power of the charging pile or operation of increasing the power of the charging pile is carried out according to the situation to prevent excessive charging during peak power consumption periods, support delayed charging, and ensure stable charging.