4557 results about "Charge control" patented technology

The invention provides an unmanned aerial vehicle intelligent charging control method and system based on multi-scene adaptation, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: collecting environment data through multiple types of sensors, carrying out the scene recognition and classification through a deep learning algorithm, and generating a scene feature vector and a parameter data set; a basic strategy is called from a preset charging strategy library according to the scene type, a reinforcement learning algorithm is adopted to optimize a charging strategy, a charging power adjustment curve is generated in combination with the environmental parameters, and a charging control instruction is output; and when the charging process is executed, monitoring battery state parameters in real time, performing safety threshold comparison and protection control, calculating a battery health index to evaluate a performance state, and feeding back an evaluation result to the charging optimization model to realize closed-loop control of the charging process.

The invention discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The invention relates to the technical field of battery protection control, and discloses a battery charging protection method and system based on intelligent control and a storage medium. The method comprises the following steps: acquiring and calculating battery parameters to obtain a state value; constructing a neural network model based on the state value to output a charging parameter; generating a charging strategy according to the charging parameters; the charging strategy is input into the control circuit for execution; executing multi-stage safety protection to obtain a result; and updating neural network model optimization control parameters according to a result. According to the invention, the charging control model based on the back-propagation neural network is constructed, and the real-time battery state monitoring, the self-adaptive charging strategy generation, the multi-stage safety protection mechanism and the self-learning optimization function are combined, so that the precise control and the comprehensive protection of the battery charging process are realized, the service life of the battery is effectively prolonged, and the charging safety is improved.

The invention discloses a charge and discharge controllable system and method for a decommissioned battery, and relates to the technical field of intelligent charge control. By collecting the capacity fading rate, the internal resistance value, the cycle index and the environment temperature data of the decommissioned battery in real time, a health degree parameter is calculated by adopting a nonlinear coupling algorithm; and the future health degree evolution trend is predicted in combination with the LSTM neural network. And dynamically generating a grading label according to a preset scene threshold matrix, and matching the charging demand thermodynamic diagram with the battery grading label through a dynamic scheduling algorithm to realize intelligent distribution of charging and discharging power. And introducing a photovoltaic-battery-power grid cooperative power supply model, predicting and dynamically adjusting the power supply proportion based on the environment temperature and the photovoltaic output, and deploying to a target scene. And through a dynamic health degree evaluation and scene adaptive matching mechanism, the utilization rate of the retired battery is improved, the deployment cost of charging facilities is reduced, and the power supply reliability under multiple scenes is remarkably improved.

The invention discloses a new energy automobile charging control method and system combined with battery health monitoring, and relates to the technical field of charging control, and the method comprises the steps: collecting the working state of a target battery, and carrying out the health assessment; extracting a battery sample, positioning a health stage, and generating a life cycle position; performing progressive charging control on the target battery to obtain charging parameters; health assessment, decline analysis and stage division are carried out, and a predicted life cycle position is generated; and carrying out preamble judgment on a life cycle position, and analyzing preamble influence factors to generate a maintenance suggestion. The technical problems that in the prior art, the battery state cannot be comprehensively monitored and managed, so that the charging potential safety hazard of the battery is high, and the service life is short are solved, the full-process management from state collection, health assessment and charging control to fault prediction and maintenance suggestion generation of the battery is achieved, and the management efficiency of the battery is improved. The charging safety of the battery is improved; and the overall service life of the battery is prolonged.

The invention relates to the technical field of lithium ion batteries, in particular to a lithium ion battery safe optimization rapid charging control method, which comprises the following steps: S1, constructing a lithium ion battery electric heating aging coupling model, determining a model control equation, and establishing an offline charging environment required by reinforcement learning according to the model; s2, initializing a strategy network and two value networks, defining a reinforcement learning state space and an action space, and setting a charging reward function and a cost function; s3, state space variables are updated according to the battery model in the step S1, and the current state, the charging action, the reward value, the cost value and the state at the next moment are added to an experience pool, rapid charging with electric heating perception and health awareness can be achieved, the expression form of a multi-target optimization problem is reconstructed to enhance the safety of a charging strategy, and the method has the advantages of being high in practicability and the like. And meanwhile, an offline training and online deployment mode is adopted, so that the calculation complexity of online charging is reduced, and the method is more efficient in actual charging application.

Disclosed is a battery health state assessment method, including performing discharge control on a cell until a cutoff voltage; performing charge control on the cell until a preset cell load rated capacity, and performing discharge control on the cell until the cutoff voltage, and generating cell discharge efficiency information; performing charge and discharge cycle control on the cell and acquiring corresponding charge and discharge cycle control efficiency information; using a cell load algorithm to calculate and generate a cell load actual capacity information; and generating a cell health state assessment information according to the ratio between the cell load actual capacity and the cell load rated capacity. The accuracy of the SOH assessment process can be improved by implementing the methods of the embodiments of the present application to optimize the battery performance assessment process and improve the battery charge and discharge efficiency assessment capability.

The invention discloses a new energy charging pile charging control method and system, and relates to the technical field of new energy, and the method comprises the steps: collecting user historical charging data, power grid load data, fixed electricity price information and weather forecast data in real time, and recognizing user charging habits and demands; generating a personalized charging scheduling strategy according to the charging habit of the user and the real-time power grid state; the availability of various energy sources is monitored in real time, the generating capacity and the cost of each energy source are evaluated, the priority of energy source selection is adjusted by using a personalized charging scheduling strategy, and an optimal charging mode is automatically selected; executing charging operation based on the charging mode selected by the user, and performing adjustment according to the power grid load and the energy consumption condition; the availability of various energy sources is monitored in real time, the generating capacity and the cost are evaluated, and the priority of the energy sources is dynamically adjusted, so that the response capability of the charging pile to environment change is improved, the charging cost of a user can be reduced, and renewable energy sources are fully utilized.

The invention provides a marine high-power container type charging station, which belongs to the technical field of marine charging stations, and realizes a nine-step charging control process by integrating an electric energy stability regulation module through a control chip: firstly, receiving power grid monitoring data, calculating an evaluation index, and constructing a prediction model to generate an early warning signal; adjusting the power output of the electric pile assembly according to the early warning signal; receiving data of a ship battery management system and calculating an optimal charging curve; adjusting charging parameters based on the temperature data and the thermal diffusion equation; the harmonic content is calculated in real time, and harmonic waves are suppressed through a fluctuation compensation model; monitoring a power factor and executing reactive compensation; calculating multi-path charging power distribution based on the battery optimization model; and the charging power change rate is adjusted when power grid frequency fluctuation is detected. According to the system, a multilayer convolutional neural network and a deep reinforcement learning technology are fused, and stable and efficient charging in a complex power grid environment is realized through adaptive weight adjustment.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an intelligent charging and endurance optimization method for an unmanned aerial vehicle nest, and the method comprises the steps: collecting flight, battery and environment data through an unmanned aerial vehicle, transmitting the data to a nest, and precisely evaluating the remaining endurance of the unmanned aerial vehicle through the nest in combination with historical tasks and geographic information; performing dynamic task planning based on reinforcement learning, and generating an optimal charging strategy by using a dynamic planning algorithm; by means of a multi-mode sensor and a self-adaptive charging control model, intelligent regulation and control of the charging process are achieved; and through solar auxiliary power supply and multi-machine cooperative charging, the endurance is enhanced. The problems of low charging efficiency, poor endurance management, insufficient system reliability and the like in a traditional method are effectively solved, the utilization rate of nest charging resources is remarkably improved, the service life of a battery is prolonged, the endurance of the unmanned aerial vehicle and the system stability are enhanced, and the method has important significance in promoting efficient application of the unmanned aerial vehicle in multiple fields.

The invention relates to the technical field of charging control, in particular to a shared charging cabinet charging control method and system based on artificial intelligence. The method comprises the following steps: acquiring real-time monitoring state data of the shared charging cabinet; performing real-time charging load calculation on the real-time monitoring state data of the shared charging cabinet, and performing short-term load trend evolution so as to generate short-term load trend characteristics; current charge pal storage monitoring is carried out on the shared charging cabinet, and charging shortage priority analysis is carried out, so that the charging shortage priority of each charge pal is obtained; dynamic charging load power distribution is carried out on the charging shortage priority of each power bank according to the short-term load trend characteristics, and a dynamic charging load distribution strategy is constructed; multi-period statistics of power bank taking amount is carried out on the historical operation log of the shared charging cabinet, and a power utilization demand fluctuation curve is constructed. The efficient and rapid charging control method for the shared charging cabinet is realized, so that the method adapts to various user demand scenes.

The invention provides an intelligent management system for automatic charging and endurance optimization of hotel robots. The intelligent management system comprises a positioning module, a posture adjustment module, a charging monitoring module, a charging control module, a battery management module, a charging strategy optimization module, an energy consumption balance task distribution module and a path planning module. Through the positioning and posture adjusting module, the robot can be accurately connected with a charging station in a complex hotel, and it is ensured that charging is started accurately and stably; multi-element monitoring is combined with precise charging control, regulation and control are performed according to battery characteristics, the service life is prolonged, and endurance is guaranteed; modules such as fine battery management, charging strategy optimization and the like are linked, charging and task assignment are reasonably arranged, energy consumption is reduced, efficiency is improved, and efficient operation of hotels is enabled.

The invention relates to the technical field of robot control, in particular to an energy consumption optimization and automatic charging control system for an intelligent pet robot. The system comprises a robot data acquisition module, an energy consumption state evaluation module, an energy efficiency optimization module, an automatic charging scheduling module and a battery health management module which are in communication connection in sequence. The system is driven by multi-dimensional sensing data, energy consumption characteristics are extracted in combination with potential energy field mapping and sparse modeling, and an energy consumption influence map is constructed; joint optimization of the path and the servo strategy is realized by using a quantum behavior particle swarm algorithm; scheduling a charging path through a graph attention network; and the battery health state is dynamically predicted based on an incremental learning algorithm, and the operation efficiency, the cruising ability and the system intelligence of the robot are improved. According to the method, a closed-loop control process is constructed from energy consumption evaluation to energy efficiency optimization, automatic charging and battery health management, good system integration and intelligent response capability are achieved, and the cruising ability and operation stability of the pet robot in a complex application scene are remarkably improved.

The invention relates to the technical field of lithium ion battery charging, and discloses a multi-device compatible intelligent fast-charging lithium ion battery system and a protocol matching method, the system comprises a charging device, the charging interface of which is used for being connected with a target device to charge a lithium ion battery pack of the target device; the battery management equipment acquires an initial communication signal of the connected target equipment in real time when the charging interface is connected with the target equipment, so as to extract a voltage fluctuation feature, a handshake time sequence feature and a protocol identification feature for generating an equipment feature vector; performing similarity matching on the equipment feature vector and the protocol feature parameter to obtain a candidate protocol set; according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load state of the target equipment, obtaining an optimal charging protocol and a corresponding charging curve template; and charging the lithium ion battery pack according to the optimal charging protocol and the charging curve template. And efficient, flexible and safe charging control can be realized.

The invention discloses a battery charging control method for a battery replacement cabinet based on the Internet of Things, relates to the technical field of charging control, and has higher adaptability in the aspects of battery replacement demand prediction, charging mode switching and charging safety management compared with a traditional fixed charging mode. The problems of sudden increase of battery replacement demands, changes of environmental factors and the like can be effectively solved, and the operation stability of the battery replacement cabinet is improved; the method comprises the following steps: acquiring a battery state, a battery replacement demand and meteorological data, predicting a future battery replacement demand curve in combination with a long-short term memory (LSTM) network and a multiple linear regression model, introducing a meteorological influence factor, adjusting a charging plan of a battery replacement cabinet 24 hours before severe weather early warning, and matching the battery stock with a peak demand; the charging modes comprise a fast charging mode, a general charging mode and a slow charging mode, dynamic switching is carried out according to the battery replacement demand peak value and the daily average battery replacement demand, the fast charging mode is preferentially adopted in the battery replacement peak period, the battery supply rate is increased, and meanwhile part of charging resources are reserved.

The invention provides a split type direct current charging multi-split group charging and group control control system, and belongs to the technical field of split type direct current charging piles. The split type direct current charging system comprising a main control cabinet, a power distribution unit, a charging pile terminal and other hardware architectures is constructed; a voltage monitoring unit, a current sensor module, a harmonic filter and other multi-dimensional sensor networks are integrated to acquire power grid state data in real time, and a sliding time window method is used to carry out segmented analysis on the data to extract power grid characteristic parameters; establishing a multi-objective optimization model comprising a power balance equation and a power grid stability equation to calculate an optimal power distribution matrix, adopting a neural network to realize intelligent distribution and dynamic balance adjustment of the power of each charging pile terminal, and starting an emergency power redistribution mechanism to ensure stable operation of the system when the power grid is detected to be abnormal. The technical problem of unbalanced power distribution of the multi-pile charging system caused by power grid voltage fluctuation and harmonic pollution is solved.

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 relates to the technical field of electronic equipment battery management, and discloses a battery charging management method and an electric quantity accurate display method based on electronic equipment. The dynamic adjusting module generates a multi-dimensional strategy through a staged charging control unit (including a fast, balanced and trickle charging sub-unit), a parameter transition processing layer and a global charging state pooling layer; the environment adaptation module adjusts the charging power and the temperature threshold according to the environment data, and has the functions of environment feature extraction, self-adaptive compensation and anti-interference switching; and the strategy fusion module outputs a control instruction in a weighted manner, and is additionally provided with an exception handling and wireless charging optimization mechanism. When the electric quantity is displayed, the collected data is dynamically calibrated to compensate polarization errors, and the capacity is fused with a matching historical curve to generate a high-precision electric quantity percentage which is dynamically rendered and displayed. According to the scheme, the charging safety, efficiency and electric quantity display precision are improved.

The invention provides a vehicle charging control method, a vehicle charging system and a vehicle. The vehicle charging control method comprises the steps that after a first charging port of a vehicle is connected with a first charging pile, first insulation detection voltage of the first charging port is obtained; and according to the magnitude relationship between the first insulation detection voltage and the highest allowable charging voltage of the vehicle, controlling a boost charging circuit to work, and performing boost conversion on the charging voltage provided by the first charging pile to charge the power battery, or the direct charging circuit is controlled to work to receive the charging voltage provided by the first charging pile to directly charge the power battery. According to the vehicle charging control method, any charging port in the vehicle charging system can be compatible with the high-voltage charging pile and the low-voltage charging pile.

A blender using different charging modes with wireless charging is disclosed. Exemplary implementations may include a base assembly, a container assembly, an electrical motor, a blending component, a control interface, blending control circuitry, charging control circuitry, and / or other components. The base component may include a rechargeable battery and a wireless charging interface. The charging control circuitry may be configured to make different types of detections related to the availability and / or usage of electrical power and related to the usage and alignment of the wireless charging interface with an external charging structure. The charging control circuitry may conduct electrical power to the rechargeable battery using at least two different charging modes, thus providing different amounts of electrical power to the rechargeable battery in different charging modes.

The invention relates to the technical field of charging control, in particular to a new energy automobile charging safety regulation and control method and system, and the method comprises the steps: collecting a multi-dimensional feature parameter set; acquiring battery health state and charge state information, and collecting multi-point temperature information on the surface of the battery; constructing the multi-dimensional feature parameter set into a multi-dimensional time sequence, performing statistical analysis on the multi-dimensional time sequence, and extracting dynamic feature indexes; performing structured fusion on the dynamic characteristic indexes, thermal field gradient parameters calculated according to multi-point temperature information and a multi-dimensional characteristic parameter set, and projecting the dynamic characteristic indexes, thermal field gradient parameters and the multi-dimensional characteristic parameter set to a key characteristic subspace through principal component analysis to obtain a charging response vector; generating a multi-dimensional security hyperplane through the dynamic security boundary model; continuously comparing the charging response vector with the multi-dimensional safety hyperplane, and calculating a safety margin scalar in real time; and inputting the safety margin scalar into the self-adaptive regulation and control model, and adjusting the charging current output. According to the invention, active predictive charging safety regulation and control are realized through multi-dimensional perception.

The invention relates to the technical field of data processing, in particular to a distributed automobile liquid cooling over-charging control method and system, and the method comprises the steps: merging the operation information of an over-charging network and the real-time charging parameter information in a current time period, and obtaining the merged information; performing internal weight focusing distribution on the merged information to obtain temporary vector representation, and performing multi-target decision prediction according to the temporary vector representation to obtain target charging scheduling strategy information of one or more layers; and according to the target charging parameter information determined by adopting the target charging scheduling strategy information, starting to perform charging scheduling decision on the overcharge charging demand in a time period after the overcharge management of the charging pile. The priority judgment is carried out on different types of charging parameter information and over-charging network operation information based on the internal weight focusing strategy, so that the effect of advanced features is expanded, and the scheduling effect can be improved when the output target charging scheduling strategy information is used for the charging scheduling decision in the next time period.

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 lithium battery charging and discharging control method and system, a product and a medium, and relates to the field of charging control, and the method comprises the steps: collecting the terminal voltage and charging and discharging current data of a current battery, and calculating the state of charge and the state of health of the current battery according to the terminal voltage and the charging and discharging current data; collecting surface temperature data of the current battery, and judging whether the surface temperature data is within a preset safety interval range or not; if yes, a preset charging curve corresponding to the charge state, the health state and the surface temperature data is called from a preset battery model database, and the preset charging curve comprises a plurality of charging stages; based on the charge state, the health state and the surface temperature data, carrying out segmented correction on the charging multiplying power and cut-off voltage of the preset charging curve to obtain an optimal charging curve; and charging the current voltage based on the optimal charging curve. By implementing the method, the charging efficiency of the lithium battery can be improved.

The invention discloses an electric wheel dumper energy-saving control method and system based on braking energy recovery, and relates to the field of electric vehicle energy-saving control, and the method comprises the steps: calculating an energy fluctuation value through collecting wheel rotation speed deceleration, building a cross-cycle energy accumulation curve, and generating an energy prediction feature map; establishing a double-loop energy mapping relation according to the energy prediction feature map, dividing the double-loop energy mapping relation into a quick response path and an optimization calculation path, and outputting a time-sharing braking strategy; the torque switching process is compensated, and a smooth braking instruction is output; generating a segmented charging power curve in combination with the maximum allowable charging power; carrying out segmented adjustment on the charging current to generate a charging control signal and calculating real-time charging conversion efficiency; and the charging conversion efficiency value is distributed to the dual paths according to the time weight, and the time-sharing braking strategy is adjusted. By establishing a double-loop control strategy of braking energy prediction and charging efficiency optimization, the energy recovery efficiency is improved, and collaborative optimization of the braking and charging processes is realized.

The invention, which belongs to the technical field of electrical variable adjustment, provides an integrated one-machine double-gun direct-current charging pile comprising a main control module, a double-path power conversion module, a communication interface module, a data acquisition module, a charging control module, a display interaction module, a safety monitoring module, an energy storage module, a metering module and a power management module. The main control module is internally provided with a control chip, a system control module in the control chip firstly obtains charging state data through a data acquisition module, performs eigenvalue decomposition on a charging state matrix to extract a main eigenvector, and constructs a power feature space to calculate a power distribution coefficient. Constructing a temperature characteristic matrix based on the temperature data, and calculating a power compensation coefficient in combination with the energy storage capacity; multi-parameter collaborative optimization is carried out through the charging optimization equation set, accurate power balance control is achieved, and the technical problem that in the prior art, a double-gun direct current charging pile is difficult to achieve accurate power balance distribution under the dynamic load condition is solved.

To reduce thrust required for carrying a charge carrier by weakening strong retreating electrostatic force acting on the charged charge carrier, and to increase a conveyable charge amount by weakening potential of the charge carrier being carried, in an electrostatic generator.SOLUTION: A charge control layer remote unit is provided that conducts to a main body of a potential control layer provided in the vicinity of a charge retention layer of a charge carrier and by applying a high voltage of a different polarity of charges charged on a potential control layer body to a charge attraction electrode close to the charge control layer remote unit, charges of the potential control layer main body are moved to a potential control layer remote unit. By increasing potential of the charge carrier and conducting the charge carrier and a recovery electrode, the charges can be safely recovered.SELECTED DRAWING: Figure 10

The invention discloses a photovoltaic centralized charging control method and device for a light commercial electric vehicle and a medium, and belongs to the technical field of new energy charging. The method comprises the following steps: acquiring weather data, a vehicle battery charge state and task scheduling data; photovoltaic module parameters and environment data are processed through an LSTM neural network model, and a photovoltaic power generation power prediction curve is generated; constructing a multi-dimensional evaluation index system including task emergency degree, charging efficiency and power grid interaction factors, and generating a charging priority sequence in combination with an entropy weight TOPSIS algorithm; solving a real-time power distribution scheme based on a mixed integer programming algorithm, and dynamically adjusting the output power of the charging pile; monitoring the operation state of the system, and triggering a multi-stage power compensation mechanism when the photovoltaic power is insufficient or the electric quantity is low; and the battery state and the photovoltaic power are updated in real time, and a charging strategy is corrected through a rolling optimization algorithm. By means of the method, centralized charging control over the light commercial electric vehicle is achieved, and the technical effect of meeting the requirements of a motorcade is achieved.

An energy storage system comprises a set of swappable battery modules positioned in battery slots with a battery module being controlled by a control switch. The control switches are linkable to form a charging control chain and / or a discharging control chain respectively to control sequential charging / discharging, parallel charging / discharging, and segmented charging / discharging among the set of battery modules in the energy storage system. Methods to detect defective battery module in a battery slot are also presented. The energy storage system can be configured in an AC power distribution environment, a DC power distribution environment, or in an EV. The energy storage system is scalable, automatically reconfigurable and accepts AC power and regenerated DC power.

The invention relates to the field of battery charging control, in particular to a lithium ion battery equalizing charge control method and system based on a BMS (Battery Management System). The method comprises the following steps: continuously obtaining battery cell monitoring parameters of a battery charging state based on a BMS, and carrying out high-frequency filtering and denoising processing and battery cell charging behavior analysis to obtain a battery cell behavior characteristic time sequence; identifying a charging voltage response signal of each battery cell based on a BMS, performing multi-frequency impedance change trend evolution, performing battery cell charging response characteristic perception based on a battery cell behavior characteristic time sequence, and constructing a plurality of battery cell charging state maps; and carrying out quantitative calculation on charging energy flow affinity according to the battery cell monitoring parameters, carrying out state deviation trend distinguishing by combining the plurality of battery cell charging state maps, constructing a topological connection diagram among the battery cells, and carrying out energy deviation complementary path mining and dynamic deviation balance planning on weak battery cells and strong battery cells. According to the invention, efficient and accurate energy balanced distribution and charging control are realized.