1323 results about "Charge management" patented technology

The invention relates to the technical field of charging pile power dispatching, in particular to a service area new energy charging management method and system based on multi-device data analysis. The method comprises the following steps: acquiring real-time operation monitoring parameters of all charging piles in a service area; multi-dimensional feature perception and charging behavior time popularity distribution analysis are carried out, and a dynamic charging behavior hotspot map is constructed; carrying out multi-time-point power sampling according to the dynamic charging behavior hotspot map, carrying out power cooperation path topology evolution, and constructing a power cooperation path network; performing charging pile power trend prediction and power peak congestion evolution according to the power cooperation path network, and generating a power peak congestion state feature of the charging pile; and obtaining new energy traffic flow data of the service area, carrying out unit time traffic flow average calculation, and carrying out traffic flow prediction to obtain a traffic flow prediction thermodynamic diagram. Charging power scheduling is carried out through demand prediction, and the operation efficiency and stability of charging equipment are improved.

The invention relates to the field of new energy charging, in particular to a new energy charging management method for a smart city. The method comprises the steps that a municipal vehicle operation feature data set is acquired, on the basis, municipal vehicles are subjected to type division through a vehicle type classification strategy, and a municipal vehicle driving type set is determined; acquiring a municipal vehicle task set, and on the basis, tracking a dynamic task variable set of a task execution vehicle in a municipal vehicle driving type set in a task execution process; according to the dynamic task variable set, analyzing an association relationship between the dynamic task variable set and the real-time power consumption data through a multi-modal fusion strategy, and determining dynamic power change information; and acquiring a charging station information set, dynamically optimizing a charging decision in combination with the municipal vehicle task set and the dynamic electric quantity change information, and determining and outputting an optimal charging strategy. In the municipal vehicle charging decision-making process, the utilization rate of charging resources is improved, and the continuity of municipal task execution is ensured.

The invention relates to the technical field of electric vehicle charging management, and discloses an electric vehicle charging optimization management system. The system comprises a dynamic charging demand prediction module which constructs a model based on historical data, collects voltage fluctuation, current phase deviation and charging power pulse characteristics of a charging pile in real time, and outputs a theoretical charging load value; the multi-dimensional demand difference analysis module is used for performing three-dimensional comparison of time accumulation deviation, frequency response difference and pulse characteristic similarity on theoretical and actually measured load values to generate a charging pile group level difference characteristic tensor; the hot spot region association positioning module inputs the difference feature tensor into a spatial topology mapping network, generates a fault probability thermal distribution diagram in combination with power grid node capacity and charging station position information, and positions a charging bottleneck region; and the self-adaptive regulation and control strategy generation module is used for starting millisecond-level monitoring on a high fault probability region according to the thermal distribution diagram and applying a power disturbance test on adjacent nodes. The system can improve the accuracy and effectiveness of charging management.

The invention provides an intelligent parking lot charge management and parking space resource intelligent allocation optimization method, and relates to the technical field of intelligent parking lots, and the method comprises the steps: obtaining the real-time occupation state data of parking spaces in a parking lot, carrying out the time window division and sequence similarity analysis through time sequence data mining, and calculating the real-time parking space pressure value of each region; when the pressure value of the first region exceeds a threshold value, selecting a second region for integration based on a prediction result and a region stability integration degree, and forming a dynamic management region; and calculating a real-time rate by adopting deep reinforcement learning, determining a core influence area according to the parking request, and distributing a target parking space based on neighborhood density characteristics. According to the invention, the utilization rate of parking lot resources is improved, and intelligent parking space allocation and dynamic pricing are realized.

The invention relates to the technical field of battery management, and discloses a forklift battery charging method and device, and the method comprises the steps: collecting the real-time multi-dimensional physical state data, including vibration data, of a forklift battery; dynamically reconstructing a multi-physical field coupling model matched with the current working condition in a preset model library according to the data; performing predictive multi-target optimization based on the model to determine an optimal charging equalization strategy considering charging time, energy loss, health attenuation and thermal risk; charging is executed according to the strategy, the deviation between physical response and model prediction is compared in real time, and online self-correction is conducted on model parameters. The system comprises a state acquisition module, a model reconstruction module, a strategy optimization module and a charging control and correction module. According to the method, the digital twin model capable of self-evolution is constructed and closed-loop feedback correction is carried out, so that the charging strategy can actively adapt to working condition changes caused by factors such as vibration and temperature, and safer and more efficient charging management of the forklift battery is realized.

The invention provides a special lithium ion battery system for a UPS (Uninterrupted Power Supply) based on intelligent management, and relates to the technical field of UPSs, the system comprises a battery state monitoring and predicting module, an intelligent charging management module and a fault diagnosis and fault-tolerant control module of a battery system, and the SOH and SOC of a battery are accurately predicted through a support vector machine, a random forest and a particle filter algorithm. A basis is provided for reasonable charging and discharging, the service life of the battery is prolonged, the intelligent charging management module dynamically adjusts charging parameters according to the SOC and the temperature of the battery through a fuzzy logic control algorithm, balance of rapid charging and battery protection is achieved, the intelligent charging management module adapts to different environments, and the fault diagnosis and fault-tolerant control module detects faults in time with the help of a support vector machine algorithm. A model prediction control algorithm optimizes control input when a fault occurs, it is ensured that the UPS system still operates normally under the battery fault, a data acquisition and transmission module and a system monitoring and display module monitor the battery state and provide historical data query analysis, and a user can conveniently master the battery condition and make a decision.

The invention relates to the technical field of charging management, and discloses an intelligent charging management method, device and equipment for an electric scooter and a storage medium, and the method comprises the steps: carrying out the parameter collection and initialization setting of a single-phase bidirectional converter and a permanent magnet synchronous motor in the electric scooter, obtaining system basic parameters and communication data, and obtaining a network authentication identifier and a charging station operation parameter. And dynamic planning of a charging strategy is executed according to the parameters to generate a segmented charging control curve. And the data are input into the self-adaptive prediction controller to realize real-time adjustment in the charging process, and charging state data and power compensation parameters are obtained. And adopting a double-neural network model structure to obtain a battery safety feature matrix and a fault probability vector. And constructing a three-dimensional charging state space model to carry out optimization calculation on the power compensation parameters, inputting a deep reinforcement learning network to carry out iterative optimization, and generating charging control parameters of the next period, thereby improving the charging efficiency and the electric energy quality of the electric scooter.

The invention discloses a charging pile communication method and system based on multi-protocol adaptation, and solves the problem of compatibility between a charging pile and vehicles with different communication protocols. The method comprises the following steps: acquiring charging request data of a charging pile and a vehicle, identifying a vehicle communication protocol, and performing compatibility comparison with a charging pile protocol; and if the protocol is not matched, carrying out transcoding strategy matching by using a protocol converter, constructing a protocol adaptation scheme, and realizing communication link connection between the charging pile and the vehicle. And the system monitors the charging state of the vehicle in real time, evaluates the conversion performance of the protocol adaptation scheme based on the charging state, and obtains performance data. And finally, performing optimal configuration on the parameters of the protocol converter according to the performance data. According to the method, dynamic adaptation and optimization among multiple protocols are realized, the compatibility and efficiency of charging communication are improved, the intelligent level of charging management is enhanced, the method is suitable for various charging environments, and the charging experience and the system stability are improved.

The invention discloses a charging pile management method and system based on data analysis, and belongs to the field of charging management.The charging pile management method based on data analysis comprises the following steps that a time sequence prediction model is constructed based on historical charging session data, environmental factors, special time period information and the real-time occupancy state of a charging pile, predicting peak periods and valley periods of charging demands in different areas from a few hours to one day in the future; the method has the beneficial effects that the charging service price is dynamically adjusted by predicting the charging demand peak period and the charging demand valley period and combining the overall utilization rate prediction result of the charging piles in the region in the period, so that the regional charging price in the charging demand peak period is relatively high, and the regional charging price in the charging demand valley period is relatively low; therefore, the user is guided to actively perform peak shifting charging or select other areas for charging, the load distribution of the charging piles is optimized, the queuing pressure in the peak period of the charging demand is relieved, and the infrastructure utilization rate in the valley period of the charging demand is improved.

The invention provides a battery cell echelon utilization battery BMS charging management method comprising the following steps: obtaining historical usage record data of each battery, the historical usage record data comprising charging and discharging times, charging and discharging current values, voltage values and temperature values, and constructing a battery state original data set; according to the standardized battery state data set, calculating and establishing a battery health assessment model, a battery capacity attenuation rate, an internal resistance growth rate and charge and discharge efficiency, and calculating according to a preset weight ratio to obtain a battery health score; based on the health scores of the batteries, grouping the batteries by adopting a clustering algorithm, and generating battery grouping data according to a preset threshold value; and after each charging is completed, updating the original data set of the battery state, recalculating the health score of the battery, and dynamically updating the grouping data of the battery.

The invention relates to the technical field of power load prediction, in particular to a charging pile power load prediction and evaluation system and evaluation method. Comprising a data acquisition module, a model prediction module, a dynamic adjustment module and a fault alarm module. Historical charging power, charging duration and charging frequency data of a charging pile are acquired through a data acquisition module, a model prediction module establishes a power load prediction model of the charging pile by combining a convolutional neural network with a long-short-term memory network, and a dynamic adjustment module uses a genetic optimization algorithm to adjust the power load of the charging pile. The optimal power distribution scheme of each charging pile in different time periods is calculated, the operation states of the charging piles are dynamically adjusted through the intelligent charging management system, and the fault alarm module establishes a graded early warning system, so that the stability of power supply is ensured, early warning information is sent in advance, workers are notified to perform maintenance, and potential safety hazards are eliminated.

The invention discloses a charging user behavior pattern analysis system and method, and relates to the technical field of electric vehicle charging management, and the method comprises the steps: obtaining and building a multi-source data pool, fusing the multi-source data pool, analyzing a charging behavior pattern of a user, calculating the abnormal behavior probability, building a dynamic scheduling rule according to the charging behavior pattern of the user, and carrying out the dynamic scheduling according to the abnormal behavior probability. According to the method, through multi-source data collection and analysis, the user charging behavior is accurately predicted, resource distribution is dynamically optimized, the charging station operation efficiency and the user satisfaction degree are improved, virtual power plant integration is promoted, and the power generation efficiency is improved. According to the method, a vehicle-to-power grid strategy optimization module is constructed, the user participation power grid peak regulation potential is quantified, regional energy scheduling is optimized, in addition, an anomaly detection and response mechanism is provided, safety and stability are ensured, and the charging service is promoted to develop towards the efficient, intelligent and sustainable direction.

The invention discloses an unmanned aerial vehicle energy-power conversion system, and relates to the technical field of unmanned aerial vehicle energy, and the system comprises a battery module which is formed by combining a plurality of single batteries in a series connection or parallel connection manner, and is responsible for providing electric energy required by the flight of an unmanned aerial vehicle. And the energy management module performs real-time monitoring, optimal distribution and intelligent regulation and control on energy use of the unmanned aerial vehicle through an integrated sensor, algorithm and control technology, and comprises a data acquisition unit, a charging management unit and a power conversion unit. The charging management unit is used for managing an unmanned aerial vehicle battery charging process, externally connecting a charging power supply and controlling an unmanned aerial vehicle battery charging strategy according to the battery power reserve level; and the power conversion unit adopts a dynamic power distribution strategy, implements closed-loop control based on the real-time power spectrum characteristics of the driving module, and realizes on-demand power output adjustment. And the configuration of longer endurance time of the same power supply weight or fewer batteries under the same endurance time is realized.

The invention provides an electric vehicle charging demand prediction and scheduling method based on artificial intelligence, relates to the technical field of electric vehicle charging management, and integrates charging station historical data, user behavior modes and external environment factors through multi-mode deep learning, quantum computing optimization and dynamic user portrait generation technologies. Compared with a traditional method, the method has the advantages that the prediction error is reduced by about 30%, the prediction is particularly prominent in peak hours and extreme weather conditions, and meanwhile, charging resources in a region are integrated into a unified management unit through a virtual energy pool and an ecological self-adaptive scheduling mechanism, so that the prediction efficiency is improved. Distribution of the charging piles, new energy power generation and energy storage equipment is dynamically optimized, the utilization rate of the charging piles is increased by about 35%, and the utilization rate of new energy is increased by about 25%. According to the effect, resource waste is reduced, the contradiction between supply and demand of the charging station is relieved, more efficient charging service is provided for electric vehicle users, and meanwhile green development of an energy system is promoted.

The invention relates to the technical field of charging management, in particular to a front-and-back stage cooperative control method and system for a direct-current fast charging module. Monitoring working parameters and environmental parameters of the front-stage converter and the rear-stage converter in real time, and constructing a multi-dimensional feature matrix in combination with a pre-acquired historical charging mode and user behavior data; performing multi-scale prediction on the charging energy demand by adopting the trained time sequence prediction model, and generating an energy demand curve comprising a short-term prediction window and a long-term prediction window; and dynamically adjusting modulation parameters of the front-stage converter according to the energy demand curve of the short-term prediction window, and adaptively switching a hybrid modulation mode of the rear-stage converter based on the energy demand curve of the long-term prediction window. According to the invention, working parameters and environmental parameters of the front-stage converter and the rear-stage converter can be monitored in real time, and comprehensive analysis is carried out in combination with a historical charging mode and user behavior data, so that more accurate and efficient charging control is realized.

The invention relates to the technical field of intelligent charging management, and discloses a charging pile management method based on artificial intelligence, and the method comprises the steps: S1, charging pile scheduling modeling: building a charging pile scheduling mathematical model based on a charging demand and a power grid load, and setting a state space, an action space and a reward function; s2, reinforcement learning optimization scheduling: a multi-agent reinforcement learning algorithm is utilized to train charging pile agents, and a charging power distribution and queuing strategy is optimized; and S3, optimizing power distribution through a game theory: constructing a game model among the charging piles, and solving an optimal power distribution scheme through Nash equilibrium. The long-short-term memory self-encoder is adopted for equipment health state analysis, potential faults can be recognized in advance, early warning can be given out in advance, sudden faults of equipment are avoided, and compared with a regular inspection and manual intervention mode in the prior art, through data-driven intelligent prediction, the maintenance cost is greatly reduced, and the equipment downtime is greatly shortened.

An electric vehicle (EV) charging management system configured for managing charging of an EV comprises an EV charging system for charging a Li-Ion battery and an EVSE which is based on a charging station altitude at which the EVSE is charging the Li-Ion battery, an EV route stored in the EV charging management system and a likelihood that the EV will go downhill when travel commences after charging ends, in which the EV will start travelling without regenerative charging in place, as the EV goes downhill. The EV charging system comprises a processor and a memory storing software instructions that determine a safe and efficient state of charge (SoC), which is not to be exceeded, by adjusting a SoC limit based on the charging station altitude of an EV charging site and an expected route of the EV to provide an altitude and route adjusted target SoC for the EV.

The invention relates to the technical field of lithium battery protection boards, in particular to an intelligent charging and discharging management system based on a lithium battery protection board, and solves the problems that in the prior art, although important guarantee is provided for safe use of a lithium battery, some defects still exist, the protection board fails, and the lithium battery protection board is prone to damage in the long-term use process. And potential safety hazards such as over-charging, over-discharging, short circuit and the like of the battery cannot be effectively prevented. An intelligent charging and discharging management system based on a lithium battery protection board comprises system initialization, real-time battery state monitoring, charging management, discharging management, battery cell balance management, fault diagnosis and early warning, data recording and analysis, and system dormancy and awakening. According to the intelligent charging and discharging management system based on the lithium battery protection board, the service efficiency of the battery is improved, the service life of the battery is prolonged, through accurate electric quantity monitoring and intelligent charging and discharging management, the system can avoid the phenomena of over-charging, over-discharging and over-current of the battery, and therefore the service life of the battery is effectively prolonged.

The invention provides a multi-energy coupling complementary light storage and charging management system. The multi-energy coupling complementation light storage and charging management system comprises a photovoltaic power generation module, an energy storage module, a lithium battery pack, a power grid input end, an energy conversion module, an energy storage management module, an energy scheduling module, an energy storage management module and a power supply management module, electric energy input by a photovoltaic power generation module, an energy storage module and a power grid is allocated through a multi-energy coupling algorithm, a charging management module is used for controlling the stability of the electric energy output outwards, and system control software is used. According to the multi-energy coupling complementary light storage and charge management system, through an innovative multi-energy complementary technology and an energy efficiency optimization algorithm, the problem of energy loss generated in the energy switching process in the prior art is effectively solved, a deep reinforcement learning algorithm and a dynamic energy distribution model are adopted, and the energy efficiency is improved. And electric energy from different sources is intelligently dispatched.

The invention relates to the technical field of new energy automobile charging management, in particular to a new energy automobile charging pile orderly management system and method based on a WeChat applet, and the system comprises a multi-dimensional data collection module which is used for collecting the state of a charging pile, parking space occupation and power grid load data; the Internet of Things sensor group is deployed at the charging pile and is used for acquiring electrical parameters such as the state (during charging, idle charging and fault), charging power and voltage of the charging pile in real time; provided is a parking space visual monitoring device. Through combination of an interval tree data structure and a multi-target optimization scheduling model, space-time dimension dynamic management of charging pile resources is realized, the problems of reservation conflicts, resource idleness and the like in a traditional system are effectively solved, the utilization rate of the charging piles is remarkably improved, and an intelligent time period recommendation algorithm is based on user habits and a power grid load state. Users are guided to carry out peak shifting charging, balanced allocation of charging resources and power grid loads is promoted, and optimization of energy utilization efficiency is achieved.

The invention relates to the technical field of unmanned vehicle scheduling and charging management, and discloses an unmanned vehicle remote non-inductive scheduling and multi-vehicle cluster charging cooperative management system, which comprises a cluster state sensing module, a multi-vehicle cluster charging cooperative management module, an unmanned vehicle remote non-inductive scheduling module and a multi-vehicle cluster charging cooperative management module, the scheduling feature analysis module performs modeling analysis on the two types of flows to generate feature sets; the collaborative decision-making module is used for matching scheduling strategy levels based on strategy matrix fusion features; the charging strategy generation module calls a charging strategy according to the grade; and the central processing unit verifies and generates a final instruction set. The system is further provided with a communication relay interface, a charging pile dispatching module and the like, and data transmission, charging optimization, instruction execution and data archiving are achieved. The system improves unmanned vehicle cluster scheduling and charging cooperation efficiency, is suitable for large-scale management, and has the advantages of being high in real-time performance, accurate in decision making and the like.

The invention relates to the technical field of electric vehicle intelligent energy management, and discloses a charging management method and system for an electric vehicle, and the method comprises the steps: obtaining vehicle data, user charging demand data and charging station data; according to the charging station data and historical data of the vehicle data, calculation is carried out to obtain load data; comparing the load data with the available upper limit data, and generating priority charging sequence data in combination with the charging demand data; the idle charging piles are grouped, and an integer programming algorithm is adopted to obtain an optimal power distribution scheme; generating a charging plan according to the optimal power distribution scheme; in the plan execution process, collecting real-time charging data and updating the charging plan; and according to the real-time charging data, in combination with historical operation data of the charging station, a deep learning algorithm is adopted, an intelligent scheduling strategy is optimized, and intelligent distribution of resources of the charging station is completed. According to the method, historical data and real-time charging data can be fused to realize dynamic optimization of the charging plan of the electric vehicle.

The invention relates to the technical field of intelligent control, and discloses a robot charging management method and device, computer equipment and a storage medium. According to the method, first real-time electric quantity is obtained regularly, and when the first real-time electric quantity reaches a preset charging electric quantity threshold value, a first target charging pile is determined according to an initial pile machine distance, and a robot is controlled to move towards the first target charging pile; after it is confirmed that the real-time pile machine distance reaches a preset butt joint distance threshold value, if an electrode pressing-down signal sent by the first target charging pile is received, the robot is controlled to stop moving and send a charging starting instruction to the first target charging pile; acquiring a second real-time electric quantity and a charge-discharge state regularly; and if the second real-time electric quantity does not reach the full electric quantity of the battery and is in a discharging state, determining a second target charging pile according to the interruption pile machine distance, and controlling the robot to move towards the second target charging pile to continue charging until the battery reaches the full electric quantity of the battery. The rationality of robot control is improved, and the charging effect is ensured.

To provide a charging system, a charging method, and a computer program in which power to be supplied to an electric automobile can be determined by at least one of a first charger installed in a first point and a second charger installed in a second point.SOLUTION: A charging system includes a charging management computation unit that derives a first power amount to be supplied from a first charger installed in a first point to an electric automobile or a second power amount to be supplied from a second charger installed in a second point to an electric automobile, and creates notification information to notify the first charger of the derived first power amount or notify the second charger of the derived second power amount, and a communication unit that transmits the notification information created by the charging management computation unit. The charging management computation unit derives the first power amount or the second power amount in accordance with the charging priority level of the first charger or the charging priority level of the second charger.SELECTED DRAWING: Figure 1

The invention relates to a log transmission method, continuous blood glucose monitoring equipment and a system, the log transmission method is applied to the continuous blood glucose monitoring equipment, the equipment comprises a transmitter and a charging device, the transmitter comprises a first control unit and a charging management unit, and a TX pin of the first control unit is connected with a TRX pin of the charging management unit; a VIN pin of a first charging interface of the charging management unit is connected with a VBUS pin of a second charging interface of the charging device. The method comprises the steps that the charging device sends a first instruction from terminal equipment to the charging management unit; the charging management unit opens a data transmission channel between the TRX pin and the VIN pin according to the first instruction; the first control unit sends the log information to a TRX pin of the charging management unit through a TX pin; the charging management unit sends the log information to the charging device through a data transmission channel between the TRX pin and the VIN pin, and the VIN pin and the VBUS pin which are connected with each other; and the charging device sends the log information to the terminal equipment.

A computer-implemented method of optimizing charging load on electric vehicle chargers and allocating charging connectors to an electric vehicle fleet at a depot, includes mapping occupancy of charging connectors at each charger, and prior to arrival of an electric vehicle at the depot, checking a priority assigned to unoccupied charging connectors, or sorting unoccupied charging connectors based on their priority. If there is a predetermined priority, mapping the electric vehicle to an unoccupied charging connector depending on priority and if the electric vehicle has a predetermined priority requirement, determining a normalized stress metric, selecting an unoccupied charger with lowest normalized stress metric, calculating an allocation of a connector at the charger based on balancing the normalized stress metric during use, allocating the charging connector to achieve balance, and mapping the electric vehicle to the allocated charging connector. A charging management system for an electric vehicle depot is also provided.

The invention discloses a new energy automobile charging management system, and aims to improve the charging efficiency, prolong the service life of a battery and improve the user experience. The system comprises a battery monitoring module, a charging mode management module, an intelligent scheduling module, a remote control module, a safety protection module, a big data analysis module and a billing management module. The battery monitoring module collects data such as battery voltage, current and temperature in real time; the charging mode management module dynamically selects a fast, standard or slow charging mode according to the battery state and the user demand; the intelligent scheduling module optimizes a multi-vehicle charging sequence to realize reasonable resource allocation; the safety protection module provides overvoltage, overcurrent and temperature protection to ensure charging safety; the remote control module enables a user to monitor and adjust the charging state in real time. According to the invention, through intelligent charging management, resource configuration optimization, charging efficiency improvement, safety risk reduction in the charging process and battery life prolonging, convenient charging experience is provided for users.

The invention relates to the technical field of battery management, in particular to an unmanned aerial vehicle carrying platform battery management method and system, and the method comprises the steps: adjusting the charging power of a battery pack through a dynamic power adjustment type charging management algorithm; determining the charging demand and strategy of the battery according to the demand of the flight task; by implementing a quick charging technology, the charging time is shortened, and the flight efficiency of the unmanned aerial vehicle is improved; excessive charging and discharging are avoided by setting proper charging and discharging cut-off voltage. The vehicle-mounted unmanned aerial vehicle cluster system has the beneficial effects that through a vehicle-mounted unmanned aerial vehicle cluster scheme, the transportation efficiency of electric power materials in remote mountain regions is effectively improved, and meanwhile, comprehensive life guarantee and work support are provided for working field personnel. In addition, according to the technology, the energy supply process is optimized, and through a dynamic power regulation type charging management algorithm and a rapid charging technology, the charging efficiency of the battery is remarkably improved, and the service life of the battery is remarkably prolonged. And meanwhile, through advanced data acquisition and remote control capabilities, the accuracy and safety of operation are enhanced.

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 provides a battery car dynamic charging optimization management system based on battery health state evaluation, and belongs to the technical field of battery car charging management. The system comprises a data acquisition module which is connected with an environment sensing device and a battery management system and acquires environment and battery parameters in real time; the data storage module is used for storing an initial training data set, a charging and discharging data set and a battery aging feature library; the model building module is used for building a battery health state evaluation model through a migration strategy and building a response model through a modeling strategy; the health state evaluation module is used for extracting associated features with a critical damage threshold value as a core through a mapping algorithm based on the battery parameters, the environmental parameters and an aging feature library; and the charging strategy generation module comprises a strategy generation unit, a reference parameter generation unit and a dynamic correction unit. The system effectively improves the adaptability of battery car charging, guarantees the efficient and stable operation of the battery, and remarkably improves the safety and prolongs the service life of the battery.