3731 results about "Battery capacity" patented technology

The invention discloses a vehicle idling start and stop control system and method. The control system judges whether a vehicle has idling start and stop conditions by monitoring the water temperatureof an engine, the capacity of a storage battery, a safety belt and other information, judges whether the vehicle has conditions of stopping the engine by monitoring the rotating speed of the engine, the speed of the vehicle, the brake air pressure and other conditions, and judges whether the vehicle has conditions of starting the engine by monitoring the gear of a gearbox, the speed of the vehicle, a hand brake and other information. When the relevant conditions are met, start and stop work signals are sent to the engine though a CAN bus. In order to guarantee the reliability of frequent startand stop of the engine, the system strengthens flywheel gear rings of a starter and the engine. In order to ensure that the battery does not lose power, a storage battery capacity sensor is added tothe system on the basis of increasing the capacity of the storage battery, and the idling start and stop are allowed when the state of charge (SOC) of the storage battery satisfies a safe starting threshold. The idling start and stop control system has advantages of low cost and high reliability, and is especially suitable for commercial vehicles.

The invention relates to the technical field of battery monitoring, in particular to an electric vehicle battery state real-time monitoring system based on deep learning. Comprising a battery data acquisition module, a feature extraction module, a battery state prediction module, a fault monitoring module and a safety regulation and control module. The battery data acquisition module acquires battery parameters in real time, the feature extraction module extracts key features through deep learning, and the battery state prediction module predicts battery capacity, health state and performance degradation based on a neural network and a differential equation. The fault monitoring module adopts integrated learning to detect abnormity and output an alarm signal, and the safety regulation and control module optimizes a charging and discharging strategy through reinforcement learning and fuzzy control to guarantee safe and stable operation of the battery. According to the invention, accurate prediction and intelligent monitoring of the battery state are realized through the deep learning technology, comprehensive state evaluation and fault early warning are provided, and the accuracy and stability of battery state monitoring are effectively improved, so that the operation safety of the electric vehicle is improved, and the service life of the battery is prolonged.

A handheld fan includes an air supply portion and a handheld portion. The handheld portion includes a first outer shell and a battery module arranged in the first outer shell. The battery module includes a battery housing, and a first storage battery and a second storage battery which are arranged in the battery housing. The first storage battery and the second storage battery are connected in series through an electrical connector. The first storage battery and the second storage battery are arranged in a battery housing. The first storage battery and the second storage battery are connected in series or in parallel through an electric connector, which increases the battery capacity of the handheld fan, so that when a user uses the handheld fan, the battery life can be longer, bringing a better user experience.

The invention relates to the technical field of motor train unit battery pack evaluation, and provides a motor train unit battery health state evaluation and life prediction method and system, and the method comprises the steps: building a database through voltage fluctuation and temperature data in a floating charge state, and analyzing a correlation mechanism between target parameters, such as a battery capacity retention ratio and active lithium stock, and internal chemical substance loss; respectively extracting driving behavior characteristics, temperature parameters and electrochemical impedance spectroscopy by adopting second-level, minute-level and hour-level time granularity, and aligning multi-frequency data by utilizing a dynamic time warping algorithm; constructing a graph structure model taking the electrochemical parameters as nodes and cross-level association as edges, and extracting a battery aging characteristic topological relation through a graph neural network; and finally, establishing a prediction model fused with multi-dimensional features, and realizing accurate prediction of the residual life and the capacity recession inflection point of the battery. And the multi-scale characteristics are combined with an electrochemical mechanism, so that the accuracy of battery health state evaluation under a complex working condition is improved.

The invention discloses a power construction unmanned aerial vehicle path planning method and system, and relates to the technical field of space calculation, and the method comprises the following steps: determining a target region needing power construction and a preset construction point coordinate; obtaining a topographic map of the target area based on the geographic information database, constructing a three-dimensional map model, and marking the preset construction point coordinates; generating an initial construction path of the unmanned aerial vehicle; the method comprises the following steps: acquiring meteorological data in real time through meteorological data of a meteorological station in a target area, establishing a meteorological obstacle three-dimensional model, and dividing a preset radius threshold with a meteorological obstacle as a circle center into non-flying areas according to the severity level of the meteorological obstacle; adjusting the initial construction path based on the non-flying area; the cruise height is adjusted according to the meteorological data and topographic relief, a relief tracking path is generated, and the constant relative height is kept; and combining the battery capacity, the flight speed and the task priority, dynamically distributing inspection road sections, and generating a corresponding power construction unmanned aerial vehicle flight path.

The invention discloses a battery capacity attenuation prediction method and device, equipment and a medium. The method comprises the following steps: acquiring operation parameter information and current working condition information of a to-be-detected battery; performing feature extraction according to the operation parameter information, and determining a battery feature parameter set; according to the current working condition information, the pre-training state evaluation model set and the battery characteristic parameter set, determining a health state evaluation value of the to-be-detected battery; and determining a capacity attenuation prediction value according to the health state evaluation value, the battery characteristic parameter set and the pre-training time convolutional network model. A pre-training state evaluation model corresponding to the current working condition of a to-be-detected battery is firstly determined, a health state evaluation value is determined in combination with a battery characteristic parameter set, and then a capacity attenuation prediction value is determined in combination with a pre-training time convolutional network model. The influence of working condition fluctuation on prediction is reduced, the prediction precision, robustness and long-term adaptability are improved, and a foundation is laid for subsequently prolonging the service life of the battery and reducing the operation and maintenance cost.

The invention discloses a storage battery capacity checking method for a parallel intelligent direct-current power supply system, and relates to the technical field of storage battery capacity checking, which comprises the following steps of: respectively deploying electromagnetic sensing modules with nanosecond-level response capability at a master control end and a slave node end of a communication bus, acquiring voltage disturbance waveform signals of the communication bus in an operation process, and performing feature extraction operation on the transient high-frequency interference pulse detected each time, and constructing a multi-dimensional feature sequence of the high-energy transient electromagnetic interference. By deploying the high-response electromagnetic sensing module and combining an active suppression mechanism of interference feature extraction and risk index driving, time alignment modeling and self-adaptive regulation and control of electromagnetic interference and communication performance are realized, the communication stability, the battery state recognition accuracy and the capacity accounting continuity of the system in a strong interference environment are effectively improved, and the system reliability is improved. And the anti-interference capability and the operation safety of the parallel direct-current power supply system are obviously enhanced.

The invention discloses a lithium battery life prediction method and system based on a graph neural network, and the method comprises the steps: obtaining the charge-discharge cycle data of a plurality of historical periods of a lithium battery, and extracting the capacity data of the battery according to the charge-discharge cycle data; segmenting the battery capacity data to obtain a plurality of sequence samples corresponding to the time sequence, and dividing the sequence samples into a training set and a test set according to preset time nodes to train a preset neural network model; self-adaptively adding white noise into the battery capacity data, and amplifying the non-correlation degree among all modes so as to decompose the modes of the battery capacity data; and constructing a graph neural network, combining the adjacent matrix, graph convolution and an attention mechanism to obtain a lithium battery life prediction model, and predicting the residual life of the lithium battery through the lithium battery life prediction model. The problems that the lithium battery life prediction precision is insufficient and the prediction precision improvement is limited in the prior art are solved.

The invention provides a lithium ion battery health state estimation method and system based on an electrochemical model, and relates to the technical field of battery health state estimation, and the method specifically comprises the steps: collecting key parameters of a lithium ion battery in real time, and calculating an open-circuit voltage through an electrochemical reaction kinetic formula; calculating a health index, calculating a temperature influence index according to a set optimal working temperature and a current working environment temperature mean value, and calculating an internal resistance influence index according to the battery capacity, the cycle index and the internal resistance data of the lithium ion battery; calculating a battery health score for reflecting the health state of the lithium ion battery to be tested according to the health index, the temperature influence index and the internal resistance influence index; and comparing the battery health score with a preset health assessment threshold, and assessing the health state of the lithium ion battery according to the comparison result, thereby providing a scientific battery health assessment mechanism.

A system and method in at least one embodiment provides the means for conditioning batteries for increased battery capacity, battery runtime, reduced battery temperature while charging, and / or reduced battery temperature while in use, using current generated from a generator.

A method for detecting a capacity retention abnormality in an electric vehicle battery, comprising: acquiring a historical capacity retention sequence of a target vehicle; by means of an STL method, decomposing the historical capacity retention sequence and extracting a trend term; on the basis of number of samples of the historical capacity retention sequence, determining a mean deviation confidence interval; on the basis of the trend term and the mean deviation confidence interval, determining whether a current detected real-time capacity retention is abnormal. Extracting a trend term based on historical detection data by means of an STL algorithm, determining a reasonable deviation value based on a historical capacity retention sequence, and performing abnormality determination according to the reasonable deviation value and the trend term, it is possible to effectively determine whether a current detected real-time capacity retention is abnormal.

The invention provides a lithium battery health state prediction method and system based on fusion health indicators, and the method comprises the steps: obtaining lithium battery charging and discharging parameters and a data set, extracting indirect health indicators for reflecting the degradation characteristics of a battery, and verifying the reasonability of the indirect health indicators through a correlation analysis method; performing dimension reduction processing on the indirect health index by using a KPCA method to obtain first principal component data, constructing a lithium battery health state comprehensive estimation model, and further obtaining a battery health state; and decomposing an original battery capacity sequence into a local fluctuation sequence and a global degradation sequence, performing prediction by using a radial basis function neural network and Gaussian process regression, and performing superposition to obtain a battery residual life prediction value. Aiming at the problems of low accuracy, unstable model and the like in the prior art, the battery health state is measured and calculated by adopting a mode based on the fusion health indexes, the method has the advantages of high estimation precision, good application prospect and the like, and the prediction accuracy is effectively improved.

The invention relates to a lithium battery health degree detection method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the lithium battery health degree detection method comprises the steps: evaluating vehicle basic features according to vehicle source data, and obtaining a battery feature data table; taking the charging state of the battery as a research object, analyzing a charging segment, determining a sampling time threshold value, and judging data missing and interpolation filling according to the residual charge of the battery; calculating the battery capacity based on the charge charging time, and analyzing the battery capacity transversely and longitudinally to obtain an initial capacity value; extracting effective capacity data in all the initial capacity values according to a preset travel constraint condition; training a single-pack health degree evaluation matrix or a double-pack health degree evaluation matrix, predicting a charging fragment sequence, and constructing a battery health degree evaluation model; the single-pack health degree evaluation matrix or the double-pack health degree evaluation matrix is corrected, an optimal health degree evaluation weight matrix is obtained, and lithium battery health degree detection is completed in combination with a physical compensation mechanism; according to the method, upward fluctuation of the SOH is avoided, and the SOH estimation precision and the model adaptability are improved.

The invention discloses a battery state prediction method based on fusion of a time sequence neural network and a physical information neural network, which mainly comprises a fusion framework of the time sequence neural network and the physical information neural network, and comprises the following steps of: firstly, mining hidden time sequence characteristics of each time sequence state by using the time sequence neural network and predicting SOH and RUL of a battery; secondly, the mined hidden time sequence characteristics of all time sequence states serve as priori knowledge to be input into a deep hidden time sequence physical module DeepHTPM of the TNPINN to guide the learning process of the module, and the DeepHTPM also receives the battery capacity of all time sequences and a predicted value predicted by a time sequence neural network as input at the same time; the DeepHTPM module jointly establishes an implicit physical model for predicting the state of health of the battery according to the inputs, and effectively combines the prediction loss of RUL or SOH of the time sequence neural network with the loss obtained by the implicit physical model to serve as the final loss to constrain the model. The method can be used for predicting SOH and RUL of lithium battery data sets of various negative electrode materials.

The invention discloses a storage battery capacity attenuation trend prediction method, and belongs to the technical field of storage battery prediction. By collecting voltage, current and temperature data of each monomer in real time and combining historical capacity attenuation and internal resistance growth data, the method identifies a voltage and capacity difference value, evaluates cyclic stress non-uniform distribution, and determines a current sharing proportion and a load unbalance degree. Identifying an abnormal mode of new battery overload and aged battery deep discharge, constructing a mixing abnormal working condition identification mode, if the unbalance degree exceeds the standard, adaptively adjusting the charging and discharging time and the current switching frequency, establishing a load balance control framework, predicting the capacity attenuation rate and the residual cycle index of each monomer, and determining the capacity matching degree and the life matching degree; finally, a comprehensive residual life estimation value and a credibility interval are generated through fusion; the performance balance of the mixed battery pack is remarkably improved, the overall service life is prolonged, and the method is suitable for real-time monitoring of a battery management system.

According to the power control method and system based on energy storage unit adjustment priority evaluation, an energy storage unit calibrates the charge state, the battery capacity and the health state of the energy storage unit and feeds back the charge state, the battery capacity and the health state to a battery management system; the battery management system verifies a built-in battery state estimation model of the battery management system by using the calibrated charge state and health state; based on the calibrated charge state and the battery capacity, calibrating the energy state range; calibrating the voltage standard deviation coefficient, the voltage range and the temperature range based on the measurement precision of the sensor; taking the weighted sum of each evaluation index score after calibration of the energy storage unit as a comprehensive evaluation score; determining the adjustment priority of the energy storage unit allowed to be adjusted by using the comprehensive evaluation score and the charge-discharge cycle times of the energy storage unit allowed to be adjusted; and selecting the adjustment-allowed energy storage unit with the highest adjustment priority as an execution unit for responding to the scheduling target power instruction, and ensuring the safe and stable operation of the energy storage system at the same time.

The invention discloses a time sequence modeling method for lithium ion battery performance evaluation, and aims to solve the technical challenges of a traditional method in the aspects of multi-scale feature extraction and fusion. The method specifically comprises the steps of obtaining battery capacity data and performing preprocessing; constructing an MTH3 model by combining a time convolutional network and a multi-head attention mechanism on the basis of a Mama model, and performing multi-time scale feature extraction by taking the preprocessed data as input; constructing a NetTDF model based on a U-shaped network, fusing the features extracted by the MTH3 model, and modeling a global relationship; and dividing a training set and a test set, and inputting the training set and the test set into the overall model to obtain a prediction result of the lithium ion battery health state. According to the method provided by the invention, the advantages of each model are fully exerted, an efficient prediction model for the capacity of the lithium ion battery, which can accurately capture local features and effectively model a global relationship, is provided, and reliable support is provided for performance evaluation of the lithium ion battery.

The invention discloses a lithium battery residual life prediction method and system and terminal equipment, and relates to the technical field of lithium battery health management. The method comprises the following steps: receiving a battery capacity attenuation sequence as an original input sequence, detecting and filtering abnormal data by adopting a 3 sigma criterion, and carrying out noise suppression processing on the battery capacity attenuation sequence through a Dropout mask; and carrying out normalization processing on the preprocessed battery capacity attenuation sequence, dividing the battery capacity attenuation sequence into a training set, a verification set and a test set through a sliding window algorithm, and generating a time sequence characteristic matrix and a corresponding residual service life label. According to the method, a neural network structure fusing trend prior perception and dynamic attention regulation is constructed, a multi-scale capacity modeling strategy is introduced to separate a degradation trend, fluctuation disturbance and high-frequency noise, and compared with a traditional time sequence neural network or a single attention model, pseudo fluctuation characteristics caused by capacity regeneration can be more effectively recognized, and the method is more efficient and more reliable. And the judgment accuracy of the model in a complex degradation scene is improved.

The invention relates to the technical field of charging power management of charging piles, and particularly discloses a charging power distribution method and device for a multi-gun charging pile, and the method comprises the steps: calculating the theoretical power of each charging gun through collecting the battery information, including the current electric quantity, the battery capacity, the rated charging power and the charging demand, of each vehicle in real time; the safety and the stability are comprehensively evaluated according to the dynamic change of the battery temperature and the load rate of the charging gun; according to the method, the charging power can be dynamically adjusted, the safety and the high efficiency of the charging process are ensured, and the problems of battery damage and unstable charging caused by improper power distribution are avoided; the invention further provides a real-time monitoring interface and an alarm mechanism, abnormal conditions can be found and processed in time, and it is ensured that the charging process is carried out smoothly.

The invention discloses an electric vehicle charging path planning method based on a dynamic energy consumption model and road network information. According to the method, the influence of different temperatures on the battery capacity is analyzed according to the charging and discharging experimental data of the lithium battery, the power consumption generated in the driving process of the electric vehicle is analyzed, and a refined air conditioner energy consumption model and a mileage energy consumption model are established. Based on an improved A * algorithm, a method for searching a reachable path with the minimum energy consumption according to the residual electric quantity of a vehicle-mounted battery, the position of a charging station and the real-time road condition is established. The A * algorithm of the model solving algorithm serves as an optimal priority search algorithm in graph theory search methods, and the optimality requirement can be met by reasonably designing a function; and meanwhile, aiming at an urban road network, a heuristic A * algorithm is adopted to be matched with the path optimization solution of a large-scale traffic network in a road-electricity coupling scene. The effectiveness of the provided model in the charging path decision is verified by respectively comparing three targets of time optimization, path optimization and comprehensive energy consumption optimization provided by the invention.

The invention relates to the technical field of electric vehicles, and particularly discloses a method for online estimating the capacity and health state of a battery pack based on random charging information, which comprises the following steps of: preliminarily estimating the capacity of the battery pack: selecting a charging section and utilizing an ampere-hour integral to estimate a model; temperature-based battery capacity correction: temperature data is corrected based on fuzzy Kalman filtering optimization algorithm processing, and because relatively large noise is inevitably generated after preliminary estimation, a fuzzy control algorithm is adopted to control observation noise of a Kalman filtering algorithm in real time, so that an estimation result is converged to a real trend; estimating and predicting the state of health of the battery: establishing an Arrhenius model based on the capacity attenuation of the battery pack to predict the service life of the battery; according to the method, the defect of inaccurate estimation of real-time measurement noise of a traditional Kalman filtering algorithm is effectively overcome, and the measurement noise is corrected in real time; the method is suitable for AI adaptive parameter adjustment models of different new energy heavy truck models, and is applied to state-of-health evaluation and life prediction of batteries.

The invention discloses a battery capacity analysis and prediction method and system of a storage battery, and relates to the technical field of data processing. The method comprises the following steps: acquiring operation data of a target storage battery in a preset time period, wherein the operation data comprises a voltage sequence, an internal resistance sequence, a temperature sequence and a charge-discharge cycle number sequence; determining a time interval corresponding to the plurality of operation conditions according to the operation data; calculating a temperature influence factor, a power loss factor and a cyclic aging factor of each time interval based on the sub-operation data in each time interval; receiving the battery capacity attenuation amount of the target storage battery in each time interval; generating a capacity attenuation curve of the target storage battery by combining the battery capacity attenuation amount, the temperature influence factor, the power loss factor and the cyclic aging factor of each time interval; and predicting the battery capacity in the next time period based on the capacity change curve. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of battery capacity analysis and prediction of the storage battery is achieved.

A body for a vehicle configured for securing safety performance and vehicle rigidity according to a vehicle collision while increasing a battery capacity mounted in the vehicle, may include an internal side member mounted on a center floor in a longitudinal direction of the body; a battery cross member mounted in a width direction of the body inside a battery case provided at a lower end portion of the center floor while intersecting the internal side member; and a coupling structure coupled to the battery case, the battery cross member, and the center floor.

The invention relates to a lithium ion battery life prediction method, and belongs to the field of battery life prediction and intelligent maintenance. The method comprises the steps that S1, a battery capacity degradation sequence is collected, and integrity is checked and normalized; s2, decomposing the sequence by using an improved complete set empirical mode decomposition algorithm, and dividing the sequence into a high-frequency component and a low-frequency component according to a zero-crossing rate; s3, modeling the high-frequency component: fusing multi-scale channel interactive attention, a time sequence convolutional network and a hybrid expert model, and extracting short-term fluctuation and capacity recovery features; s4, modeling a low-frequency component: introducing a two-way gating circulation unit network constrained by a double-index degradation model, and simulating a long-term trend; and S5, constructing a high-frequency migration module through tensor decomposition, improving cross-battery generalization, and fusing high and low frequency results to output a residual life prediction value. According to the method, a dual-channel framework combining signal decomposition, deep learning and physical modeling is combined, the prediction precision and adaptability under complex degradation are improved, and the method is suitable for various battery systems.

The invention discloses a battery capacity performance testing method and device and a storage medium, and relates to the technical field of battery capacity performance testing. By constructing a dynamic load model and a multi-load test protocol and combining a high-precision data acquisition and filtering algorithm, adaptive data analysis and a machine learning regression model, the problems of difficulty in balancing precision and efficiency, large data acquisition error, fixed test protocol and the like in the traditional battery test are solved; by dynamically adjusting test parameters and environmental conditions, complex working conditions of the battery in actual use are accurately simulated, meanwhile, dynamic updating and optimization of a test protocol are achieved, the test precision, the data reliability and the test efficiency are remarkably improved, and a comprehensive, efficient and high-adaptability solution is provided for battery performance evaluation and health management.

The invention discloses a real vehicle power battery capacity attenuation trajectory prediction method based on a de-noising diffusion model. According to the method, a diffusion generation model is applied to the field of battery capacity prediction for the first time, multi-modal features are constructed for charging and discharging data under a real vehicle working condition, feature expressions are extracted through Transform and CNN, and the features are integrated into high-dimensional embedded vectors. And then, a DDPM model based on a ContextUnet architecture is utilized to learn a dynamic change rule of capacity attenuation in a noise environment through a noise diffusion and reverse denoising process under the guidance of multi-modal features, so that high-precision capacity prediction is realized. The training process is further combined with an implicit denoising mechanism of DDIM, and the reasoning speed is increased while the prediction quality is improved. Finally, a capacity attenuation track confidence interval is obtained by performing multiple times of prediction sampling calculation on a predicted capacity attenuation result, and the capacity attenuation track confidence interval can be used for deducing key indexes such as the state of health (SOH) and the remaining service life (RUL) of the battery. The method has the characteristics of high prediction precision and strong adaptability, obviously enhances the intelligent level of the battery management system, and has important practical value for health assessment and life management of the new energy automobile battery.

The invention relates to a charging station management method and system based on an intelligent box, and relates to the field of the vehicle charging technology, and the method comprises the steps: obtaining the overall power of a power grid and the charging demand power of each current charging pile; determining the overall required power according to each charging required power, and determining the available power of the power grid according to the overall power of the power grid and the anti-disturbance coefficient; when the overall demand power is greater than the available power of the power grid, acquiring the battery capacity percentage, the residual charging duration and the vehicle priority coefficient of each vehicle according to each charging pile; calculating according to the battery capacity percentage, the residual charging duration and the vehicle priority coefficient to determine a vehicle basic weight; and calculating according to the available power of the power grid, the basic weight of the vehicle and the charging demand power to determine effective distribution power, and controlling each charging pile to operate at the corresponding effective distribution power. The method and the device have the effect of improving the charging experience when the vehicle is charged by using the charging station.

The invention relates to the technical field of hybrid energy storage management, in particular to a hybrid energy storage fast charging station energy management method based on a decommissioned battery, which comprises the following steps of: comparing a load demand with a battery capacity based on the load demand, the residual capacity of the decommissioned battery and a health state parameter, dividing a load interval according to time, and determining the load interval of the decommissioned battery; and calculating the load change rate in each time period, performing battery health state evaluation and power response selection, and generating a time interval battery pack distribution result. According to the method, the fuzzy control algorithm is used for clustering and dividing the retired batteries, comprehensive calculation is carried out by combining the health states of the batteries and power distribution, the power output in the battery pack is optimized, the service life of the retired batteries is prolonged, overload or unreasonable use of the batteries is avoided, the energy storage cost is reduced, and the real-time power regulation scheme is optimized. The battery pack can be adjusted in time according to power grid load fluctuation, and the stability in a high-load period is ensured.

The invention relates to a joint estimation method for SOC and SOH of a power battery, in particular to a joint estimation method for SOC and SOH of a lithium ion power battery based on FOASEKF-EKF, comprising fractional order equivalent circuit models of two parallel fractional order CPE branches, and providing a hybrid genetic algorithm HGA fusing a differential evolution strategy and an adaptive variation mechanism. Accurate estimation of SOC and terminal voltage under a fast time scale is realized by introducing a sliding-mode observer and an FOASEKF, periodic online correction is performed on model parameters and battery capacity based on an EKF under a slow time scale, and high-precision and high-robustness battery SOC and SOH joint estimation is realized. The method is suitable for complex industrial environments such as electric automobiles and rail transit, does not need to set a large number of hyper-parameters, does not excessively depend on the quality and quantity of data, has good interpretability, adaptability and engineering practicability, can still achieve high-precision cooperative estimation of SOC and SOH especially under the working conditions of frequent start and stop and unsteady operation, and has good application prospects. And misjudgment and drift estimation risks are obviously reduced.

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.