131 results about "Energy allocation" patented technology

The invention provides a hybrid vehicle energy management system and method based on a traffic state, a storage medium and a computer program product, and the system comprises the steps: constructing a congestion prediction model based on a deep learning model, the congestion prediction is used for predicting the traffic flow and the driving speed of a future time period according to the historical traffic flow data and the historical driving speed data; calculating a first congestion index according to the predicted traffic flow in the future period; calculating a second congestion index according to the predicted driving vehicle speed sequence of the future time period; performing weighted calculation on the first congestion index and the second congestion index to obtain a comprehensive traffic congestion index in a future time period; traffic jam types are divided according to the interval where the comprehensive traffic jam index is located; and determining an energy management mode and / or a target SOC of the hybrid vehicle based on the traffic jam type. According to the invention, multi-source data are collected and analyzed in real time, congestion is accurately quantified by using a deep learning algorithm, an energy distribution strategy of vehicles is dynamically adjusted, and the energy utilization efficiency is improved.

The invention relates to the technical field of energy management, in particular to a fuzzy logic-based multi-objective decision analysis method, which comprises the following steps of: firstly, dividing an evaluation criterion into quantitative and qualitative index sets; constructing an interval type-2 fuzzy set capable of describing data uncertainty according to quantitative indexes such as a full-life investment payback period; for qualitative indexes such as technology maturity, a spherical fuzzy set is introduced, and an intelligent operator for perceiving hesitance is used for aggregation; calculating a group utility value, an individual regret value and a comprehensive sorting index of each energy configuration scheme by applying a hybrid fuzzy VIKOR algorithm, outputting an optimal compromise sorting result, and identifying a reference optimal strategy and a risk avoidance strategy; according to the method, the explainable enhanced fuzzy rule is extracted, the robustness of the decision method is improved by analyzing different types of fuzzy logic, and the transparency, the credibility and the compliance of the energy configuration decision process are remarkably enhanced by the finally generated explainable decision basis, so that a feasible configuration decision reasoning model is provided for energy planning.

The invention discloses a micro-grid energy coordination control method considering communication topology dynamic change, which belongs to the technical field of electric energy control and management, and comprises the following steps: monitoring a communication connection state between control nodes, and generating a topology change event; evaluating energy supply capability and communication reliability and adjusting influence in energy coordination to generate adaptive weights; in combination with real-time sensor information and historical operation data, short-term energy prediction data is generated, in combination with adaptive weight, a global energy distribution consensus is achieved, and then an energy distribution decision is generated; in the execution process, the topology change event and the deviation between the short-term energy prediction data and the real-time energy data are monitored, and if a deviation threshold value is exceeded, an event signal is triggered to drive the control strategy to be updated. According to the method, the adaptive weight is generated by monitoring the communication topology and the energy state, and distributed negotiation and event triggering updating are performed in combination with short-term prediction, so that the energy coordination robustness and the operation stability of the micro-grid in a dynamic environment are improved.

Examples described herein provide a system for controlling power flow in an energy distribution network. The system includes a plurality of inverters, each having an inverter controller associated therewith. The system further includes a utility controller associated with a utility and configured to communicate with at least a subset of the plurality of inverters. The system further includes a customer controller associated with a customer of the utility and configured to communicate with at least the utility controller. The system further includes a direct current (DC) breaker associated with a battery of the customer. The system further includes a DC meter of the customer, the DC meter and the DC breaker each configured to communicate with the utility controller and the customer controller. The utility controller is configured to determine a current operational scenario of the energy distribution network and to control power routing within the energy distribution network.

The invention provides a one-dragging-N energy storage type elevator kinetic energy recovery management method and system, relates to the technical field of energy management, and is applied to an energy management framework comprising an energy storage device, a power grid, an energy dispatching module and N elevators, and the energy storage device, the power grid and the N elevators are all connected with the energy dispatching module. The energy scheduling module comprises a state monitoring unit and an energy distribution unit; comprising the following steps: acquiring operation data of each elevator through a state monitoring unit; the running state of each elevator is determined; determining an elevator kinetic energy quality coefficient in a power generation state; kinetic energy recovery is carried out according to the kinetic energy recovery priority in direct proportion to the kinetic energy quality coefficient; based on the dynamic impedance matching principle, an energy distribution unit distributes the SOC of the energy storage device after kinetic energy recovery to a load elevator in a power consumption state; and kinetic energy recovery management of each elevator is realized. The system life is prolonged, and the operation cost and the power grid instantaneous impact are reduced.

The present invention discloses a system and method for optimizing energy distribution in renewable energy plants. The system (100) comprises a solar power plant or a wind power plant to generate electrical energy. A renewable energy controller (108) monitors real-time energy output and manages energy flow to a grid (112). The renewable energy controller (108) optimizes charging cycles based on forecasts. The renewable energy controller (108) generates dispatch schedules through an intelligent bidding unit (114), considering market prices and grid conditions, while ensuring grid code compliance. A battery digital twin unit (122) is operatively connected to the renewable energy controller (108), to simulate future states of the plant based on historical data, real-time operational data, and weather forecasts, providing inputs for the intelligent bidding unit (114) to optimize energy dispatch and storage.

The invention provides a mobile kitchen resource scheduling and energy optimization method and system based on multi-modal perception, relates to the technical field of mobile kitchen management, and covers the working state of cooking equipment, the food material storage environment, the energy supply dynamic state, the cooking task queue and other data by obtaining a mobile kitchen multi-scene perception data set. A resource energy interference knowledge graph is constructed based on mobile kitchen multi-scene perception data to represent the mutual interference relationship of each link. And determining a dynamic adaptive priority of resource scheduling and energy allocation according to the resource and energy interference knowledge graph, and executing collaborative allocation processing according to the dynamic adaptive priority to generate a resource and energy collaborative allocation scheme. And in the cooking task execution process, the knowledge graph is updated and the collaborative allocation scheme is adjusted by using real-time sensing data, so that a dynamically optimized mobile kitchen resource scheduling and energy optimization scheme is formed, and the resource utilization efficiency and the energy utilization efficiency of the mobile kitchen are effectively improved.

The invention discloses a multi-energy management control method for a hybrid power system, and the method comprises the steps: S1, multi-source information processing, S2, working condition prediction modeling, S3, energy distribution optimization, S4, control execution and state monitoring, and S5, model and parameter adaptive adjustment. Future working conditions and required power are accurately predicted through multi-source information fusion and an LSTM model, and a prospective basis is provided for energy distribution. A dynamic weight multi-objective optimization algorithm is adopted, fuel consumption, battery SOC and emission indexes are planned as a whole, an optimal power distribution scheme is solved, and the energy efficiency and the environmental protection property are remarkably improved. Meanwhile, the temperature change rate of the component is calculated in real time through a heat flow evolution algorithm, a cooling system is dynamically adjusted, and precise heat management is achieved. The system further introduces a multi-dimensional deviation closed-loop correction mechanism, key parameters are adaptively adjusted through feedback data, and efficient, stable and reliable operation under different working conditions and environments is ensured.

The invention discloses a multi-scene-oriented electric hydrogen ammonia system operation optimization method and system, and belongs to the technical field of energy and control. The method comprises the following steps: performing wind and light scene generation on wind and light energy historical output data to obtain a multi-dimensional wind and light output scene library covering different output characteristics; by taking the scene library as an input, constructing and solving a multi-body operation optimization model of the electricity-hydrogen-ammonia integrated system comprising a source side, a network side, a hydrogen side, a nitrogen side, a storage side, an ammonia side and a market side, and obtaining an overall optimal energy interaction and scheduling strategy of the system; and finally, based on the strategy, performing multi-stage energy distribution on hydrogen production by water electrolysis, gas storage, nitrogen production and Harberg-Borschner synthesis ammonia sections. According to the invention, the problems of poor adaptability to wind and light output characteristics, insufficient multi-main-body cooperation and low energy distribution efficiency in the prior art are effectively solved, and safe, economical and efficient cooperative operation of the electro-hydrogen ammonia system in multiple scenes is realized.

The invention discloses a power grid seasonal load balance adjustment method based on energy storage optimization, which belongs to the technical field of power system dispatching, and comprises the following steps: acquiring operation parameters of energy storage equipment, constructing a multi-dimensional response feature mapping system, and determining a response boundary through combination of a response capability vector space and a combined threshold template; real-time calculation and trend judgment are carried out on a power grid load change rate through dual-time scale load change monitoring, a load surge early warning signal is generated, and cross-node cooperative scheduling is executed to realize distributed energy allocation of multiple energy storage stations; performing seasonal adaptive adjustment according to correlation analysis of the environmental parameters and the load characteristics, and dynamically adjusting a seasonal switching threshold value in the scheduling instruction; response deviation detection is carried out through scheduling execution monitoring, a scheduling deviation segment is identified and marked, the scheduling deviation segment serves as input, a scheduling parameter set and instruction generation logic are updated through adaptive learning, and scheduling logic closed-loop optimization is achieved.

The invention belongs to the technical field of electric vehicle energy management, and particularly relates to a city-oriented electric vehicle charging resource adjustable potential prediction method. The objective of the invention is to solve the challenges of low precision, poor robustness, difficulty in multi-dimensional data coupling, low processing efficiency, lack of fine space-time modeling and the like in urban electric vehicle charging load prediction in the prior art. According to the method, a multi-source heterogeneous data fusion model is established, deep feature extraction and space-time correlation analysis are performed, a charging load prediction model based on deep learning is constructed, and the adjustable potential of the electric vehicle is refined and quantified in combination with a multi-target optimization algorithm. According to the method, the accuracy and robustness of city-level charging load prediction are remarkably improved, the adjustable potential is effectively quantified, accurate support is provided for stable operation of a power grid, optimization of energy configuration and demand side response, and efficient intelligent management of a city power system is facilitated.

The invention relates to the technical field of intelligent control of new energy vehicles, in particular to a new energy vehicle following speed planning and energy distribution method, which realizes deep coordination of following speed planning and energy distribution by constructing a layered intelligent agent architecture, adapts to high requirements on dynamic decision in an intelligent driving scene, and improves the driving efficiency. The problem of performance loss caused by separation of the two in a traditional method is solved. And reinforcement learning training of a TD3 algorithm and a PER mechanism is combined, so that the adaptability to a complex dynamic traffic environment is enhanced, and variable car-following scenes in intelligent driving can be better coped with. The power distribution is optimized by incorporating an energy source durability model, the device loss is reduced, and the energy efficiency and the system life are both considered. The layered architecture also improves the decision-making efficiency, meets the real-time requirement of intelligent driving, achieves the comprehensive improvement of safety, comfort, energy efficiency and system durability, and provides a better solution for the application of the hydrogen fuel cell hybrid electric vehicle in the field of intelligent driving.

The invention provides a hybrid electric vehicle energy management and control method and device, and relates to the technical field of vehicle energy management and control, and the method comprises the steps: building a global optimization reference model based on vehicle parameters and vehicle power constraint conditions; inputting vehicle parameters into the global optimization reference model, discretizing the driving condition by a dynamic planning algorithm, calculating the minimum accumulated fuel consumption at each moment, and outputting a global power distribution track; an equivalent factor is dynamically adjusted according to a self-adaptive mechanism, and a real-time optimization control model is constructed in combination with a logic threshold and a self-adaptive equivalent fuel consumption minimum strategy; inputting the global power distribution track into a real-time optimization control model for power distribution verification, and outputting a gear control instruction; and performing vehicle execution on the global power distribution track and the gear control instruction based on the global optimization energy management strategy and the adaptive equivalent fuel consumption minimum strategy simulation analysis to obtain an automobile energy management and control scheme. The problems that fuel consumption is high and energy distribution is not accurate enough are solved.

This application relates to the field of energy dispatching, and in particular to a method and system for distributed energy storage dispatching based on multi-energy collaboration. The method includes: acquiring a set of factory order information; based on this, extracting and fusing multi-dimensional production value features to generate a dynamic production value assessment information set; based on this, combined with preset energy cost constraints, calculating dynamic electricity value thresholds and making global energy dispatching strategy decisions to generate an energy dispatching strategy information set; acquiring a set of energy supply influencing factors; based on the energy dispatching strategy information set and the energy supply influencing factors set, performing multi-timescale rolling optimization and global preset energy allocation to generate an energy allocation execution instruction set; based on this, issuing and executing dispatching instructions and dynamic production compensation for production lines and energy storage layers, generating and outputting a multi-energy collaborative dispatching report. This application can improve the economic efficiency of factories during energy dispatching.

The application discloses a kind of integrated energy system low carbon operation method, belong to power grid technical field.The method includes based on baseline method allocation system free carbon emission quota, calculate actual net carbon emissions;Set integrated energy system objective function;Adopt two-stage robust optimization method, handle renewable energy and load uncertainty, first based on predicted worst-case scenario information obtains pre-dispatch optimization result, subsequently based on pre-dispatch information seeks worst-case scenario, ensure stable operation of system by iterative solution;Build and include integrated energy system, integrated energy system and photovoltaic production and sales between and photovoltaic production and sales between multilayer transaction framework, adopt Nash negotiation model and multistage robust method, realize the energy distribution between multiple stakeholders;By solving integrated optimization model, obtain the optimal strategy of integrated energy system low carbon operation.Thereby realized the coordination optimization of carbon emission right and renewable energy consumption, further improve the low carbon operation effect of system.

The present application belongs to the technical field of adaptive energy-saving control, and particularly relates to a display cabinet adaptive energy-saving control method based on distributed control, steps of which comprise: building a communication module and a display cabinet to obtain the average energy consumption of the display cabinet in the region; building a data acquisition system inside and outside the display cabinet; designing a double closed loop energy-saving logic; building a display cabinet distributed control, based on the real-time energy consumption of the display cabinet, the heat temperature coefficient of the goods, and the current internal temperature of the display cabinet, to obtain the dynamic adjustment task of the local control node interaction data; obtaining the equipment operation signal characteristic data in the data storage library to establish a fault prediction model; based on the data on the cloud management node, using the fault prediction model to predict the peak value of use, and adjusting the operation strategy of the display cabinet. The present application can realize precise energy-saving regulation and control, optimize energy allocation, and reduce operating costs through the double closed loop energy-saving logic combined with regional average energy consumption calculation and energy-saving potential value sorting.

The invention provides an energy management method considering a single push failure characteristic, and belongs to the technical field of energy management, and the method comprises the steps: building an unmanned aerial vehicle hybrid battery system model with a hydrogen fuel battery as a main power supply and a lithium ion battery as an auxiliary power supply; and a power following management strategy is established, single-source fault tolerance of the fuel cell is combined with an equivalent hydrogen consumption minimum strategy, and the output power of the fuel cell and the lithium battery is controlled according to the SOC state to improve the fuel economy of the hybrid battery system and ensure the reliable power supply of the hybrid battery system. The lithium battery is prevented from being overcharged or overdischarged while the load power supply is ensured, and the instantaneous equivalent minimum hydrogen consumption of the hybrid battery system is solved, so that the optimal energy distribution of the system is realized, and the reliable power supply of the system is ensured. According to the method, the situation of single-source faults is considered, system energy management based on the ECMS is carried out, the economical efficiency is effectively improved, meanwhile, reliable power supply of the hybrid power system is guaranteed, and good performance is achieved.

This invention relates to a hybrid-powered unmanned aerial vehicle (UAV) energy management method, and more particularly to a UAV energy management method based on a pilot model, belonging to the field of UAV energy management technology. Based on the analysis of pilot operation characteristics, this invention constructs a power prediction model. According to the prediction time domain length, the weight matrix of the problem-solving framework in the model, and the constraints reflecting the pilot's operating style and the predicted impact on operation, a hybrid power system energy allocation strategy that balances range and power system lifespan is obtained. This achieves an energy management system that adapts to the pilot's needs, improving flight economy and personalization.

The invention relates to the technical field of agricultural robots, in particular to an energy recovery braking system and method for a new energy pesticide spraying robot, and the method comprises the steps: carrying out the semantic analysis and formalization processing of real-time farmland working condition data, and generating multi-source constraint data containing a braking position, an energy recovery weight and a scene type; based on the braking position and the weight, an exponential decay function is adopted to construct a dynamic energy potential field matrix representing the energy recovery potential; taking the potential field as a driving source, and establishing a multi-source energy recovery dynamic model containing an energy flow admittance item and a loss diffusion item; a multi-source cooperative influence coefficient is calculated in combination with the robot energy system evaluation value and the current potential field intensity, a local energy allocation component is generated, and a dynamic model is fused to form a self-adaptive braking control field; and an energy distribution strategy vector field is constructed in combination with a control field and a scene type, a multi-source cooperative energy recovery scheme is generated through a dynamic probability decision, the multi-source cooperative energy recovery scheme is converted into a real-time braking instruction, and closed-loop energy optimization scheduling is realized.

The invention belongs to the technical field of rail transit intelligent energy supply, particularly relates to an energy distribution method and system for a rail transit station photovoltaic and energy storage system, and aims to solve the problems of single feature learning dimension, poor multi-objective optimization balance and dynamic feedback response lag in the prior art. The method comprises the following steps: constructing a multi-subject space-time incidence matrix, carrying out feature learning by adopting a space-time double-scale attention fusion deep belief network, establishing a three-dimensional energy flow coupling tensor mining energy supply-demand relationship, constructing a multi-target optimization model based on a weighted ideal point method to generate a preliminary strategy, and carrying out multi-target optimization on the basis of the preliminary strategy; and parameters are dynamically adjusted by using an adaptive gradient feedback correction model, and a final energy distribution strategy is obtained through multiple rounds of iterative optimization. According to the method, the problems of single feature learning dimension, poor multi-objective optimization balance and dynamic feedback response lag in the prior art are solved, the energy supply and demand prediction precision is improved, and collaborative optimization of energy supply economy, environmental protection property and equipment reliability is realized.

The utility maximization energy allocation method with upper limit of distribution under random energy arrival condition studies the off-line one-dimensional resource allocation problem of realizing the utility maximization of a convex increasing function system within a certain time under the energy causality (hard) constraint, energy saturation (soft) constraint and task executability (hard) constraint. Based on the allocation strategy of maximizing the system utility under the energy causality (causality hard constraint) and energy saturation (saturation soft constraint), the energy allocation problem under the executable hard constraint is additionally considered, and an allocation scheme of reducing the iteration number is proposed. According to the executable constraint, energy is directly allocated to the time slot originally discussed, without iteration calculation and constant checking of additional wasted energy. The utility maximization energy allocation efficiency of the increasing convex function system under the causality hard constraint, saturation soft constraint and executability hard constraint is improved.

This invention belongs to the field of marine hybrid power system control and discloses an energy allocation optimization method and system for hierarchical control of a marine DC microgrid. The method includes: establishing a top-level optimized power allocation strategy model with the goal of minimizing diesel generator output and battery life loss; solving the top-level optimized power allocation strategy model using the second-generation non-dominated sorting genetic algorithm NSGA-II and the top-to-bottom solution distance method TOPSIS to obtain the power allocation scheme with optimal overall performance; introducing power regulation and voltage compensation in the intermediate layer, combined with droop control in the execution layer, to form an improved droop control, thereby achieving power allocation and current sharing control. This invention achieves optimal overall performance in marine power systems, including extended battery life, reduced fuel consumption, and efficient, energy-saving, and environmentally friendly operation.

The invention relates to the technical field of new energy buses, and discloses a fuel cell electric bus shutdown purging optimization control method, which comprises the following steps: S1, triggering and initializing; s2, evaluating a purging demand; s3, formulating an energy budget; s4, dynamic purging control and energy distribution; and S5, executing and terminating. Feedback control is carried out based on the real-time state (internal resistance) of the electric pile, excessive purging or insufficient purging in a fixed mode is avoided, and safety and health of the electric pile after shutdown can be ensured more reliably. Through intelligent energy budget management and purging mode switching, unnecessary energy consumption is reduced to the greatest extent, the loss of a secondary battery is reduced, and the energy utilization efficiency and reliability of the whole system are improved.

The invention relates to a renewable energy local consumption method based on an isolated micro-grid, and the method comprises the steps: constructing a layered distributed cooperative control architecture for the micro-grid, enabling a local control layer to collect the operation environment parameters and performance parameters of power generation equipment and energy storage equipment in real time, and enabling a central control layer to carry out the multi-time-scale energy optimization configuration according to the real-time equipment parameters; the real-time power of the micro-grid is balanced by responding to instantaneous changes of renewable energy sources and loads in an ultra-short-term optimization manner; an energy distribution plan of the next day is formulated through short-term optimization; power generation equipment and energy storage configuration are optimized in a medium-and-long term manner, and then global optimization decision making is performed to obtain an energy scheduling plan among the regions; and the regional coordination layer performs cooperative control on equipment in the region based on the energy scheduling instruction. Compared with the prior art, the method has the advantages that the special environment and technical problems of high-cold and high-altitude areas are solved, the local consumption efficiency of renewable energy in the isolated micro-grid is remarkably improved, the wind curtailment rate and the light curtailment rate are reduced, and the stability and the reliability of the micro-grid system are enhanced.

Predictive energy management across a plurality of microgrids situated at disparate facilities is disclosed. Each microgrid may include one or more distributed energy resources (DERs). Reception of profile data from these microgrids and the creation of aggregated profiles is enabled, which may incorporate the profile data, charts of accounts, energy transfer tariffs, and other energy-related attributes. An event detection engine may identify triggering events such as energy surpluses, deficits, or operational conditions, indicating a benefit to energy reallocation. A recommendation engine may generate energy allocation recommendations based on predictive models, optimizing factors like cost efficiency, carbon offset utilization, and energy availability. The recommendations may be executed through an aggregation server, which dynamically updates the aggregated profiles in real-time.

The application provides a kind of hybrid energy supply system energy management adaptive optimization method and system, belongs to power system energy distribution technical field, acquires load power, calculates the weight coefficient of optimization target;Based on the weight coefficient of optimization target, determine the constraint condition of energy distribution, randomly generate the initial position and speed of optimization factor, and carry out iterative update, finally calculate the cutoff frequency;Based on the cutoff frequency calculated, the energy distribution of low-frequency power source and high-frequency power source is carried out.The application adds optimization processing to the fluctuation of driving load in the process of hybrid energy supply system energy distribution, which is beneficial to the rationality of energy optimization of hybrid energy supply system under different load levels;At the same time, it can not only reduce the energy loss of hybrid energy supply system, but also can improve the service life of energy storage system, so as to improve the economy of transport equipment transportation.

The present application relates to the cross field of new energy power generation and agricultural facility energy saving, in particular to a strawberry intelligent greenhouse multi-energy complementary energy supply system and energy management method, wherein the system comprises a SOFC power generation module, a supercritical CO2 power generation module, a preheating recovery unit, a multi-form energy storage unit, an energy management system, a strawberry greenhouse load module and a DC bus; the preheating recovery unit processes the waste heat generated by the SOFC power generation module, and the waste heat is sequentially recovered by the supercritical CO2 power generation module, the strawberry greenhouse load module and the multi-form energy storage unit; the multi-form energy storage unit recovers the excess energy of the system; and the energy management system outputs an energy distribution scheme based on a large model driven energy management algorithm. Compared with the prior art, the present application significantly improves the comprehensive energy utilization rate through cascade waste heat recovery, the multi-form energy storage enhances the supply and demand flexibility of the system, the intelligent energy distribution adapts to the dynamic demand of the strawberry growth cycle, and effectively solves the problem of traditional strawberry greenhouse energy supply.

The invention discloses a flexible ammonia synthesis control method based on a wind-solar hybrid power generation scene. The method comprises the following steps: collecting real-time operation data and equipment parameters of a system, and obtaining renewable output predicted values of N hours and M hours in the future; according to the collected data and a set minimum load coefficient, boundary conditions required for maintaining the minimum load and full load operation of the system are calculated, and the minimum allowable reserve of the hydrogen tank is determined; judging that the system is in an energy shortage, energy balance or energy abundance state based on the energy difference between the prediction data and the operation boundary; executing a corresponding hierarchical energy distribution and multi-mode discharge strategy according to the judged state type; on the premise that power balance and equipment constraint are met, a hydrogen production power set value, a synthetic ammonia power set value and a battery charging and discharging power request are obtained through calculation and issued to the system. The method can effectively relieve the contradiction between the wind and light fluctuation and the stable operation of synthetic ammonia, and improves the safety and renewable energy consumption capability of the system.

This application discloses a vehicle power distribution method, apparatus, device, and storage medium, relating to the field of power distribution technology. The vehicle power distribution method includes: determining a corresponding target fitness based on an initial power distribution strategy with a preset initial quantity; determining a screening power distribution strategy with a preset screening quantity based on each target fitness and a preset screening strategy; determining a reorganization power distribution strategy with a preset initial quantity based on the screening power distribution strategy with the preset screening quantity; determining a target power distribution strategy based on the reorganization power distribution strategy with the preset initial quantity; and distributing power to the target vehicle according to the target power distribution strategy. By dynamically optimizing power distribution through intelligent algorithms, real-time energy allocation adjustment is achieved, improving the energy utilization efficiency of new energy vehicles and the performance of new energy vehicle power systems (including the power supply performance and cycle life of the motor and power battery).