2558 results about "Internal resistance" patented technology

The invention provides an electric vehicle battery health state monitoring and service life estimation method and system, and the method comprises the steps: S1, obtaining a real-time data flow of a battery during the operation of a vehicle, extracting the original records of charging and discharging depth, temperature change, internal resistance parameters and capacity data from the real-time data flow, a dynamic monitoring sample is calculated through voltage, current and temperature values collected by a sensor; s3, calculating a capacity attenuation rule through the time sequence feature set, analyzing the decrease amplitude of the capacity after each charge-discharge cycle by adopting an exponential attenuation model, obtaining a long-term operation trend from historical data, and obtaining a dynamic curve of capacity attenuation; and S8, if the confidence interval of the final estimation probability distribution is greater than a preset threshold value, recollecting data with higher frequency for the dynamic monitoring sample, and performing iterative optimization on the life estimation model through the updated sample to obtain an adjusted life detection result.

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 battery safety risk grade early warning and evaluation method, and relates to the technical field of lithium ion batteries, and the method comprises the steps: collecting battery operation data, carrying out the processing to obtain a capacity attenuation characteristic and an internal resistance growth characteristic, and obtaining a degradation characteristic parameter related to the temperature through an Arrhenius model; estimating capacity loss trends at different temperatures and time; inputting the multi-source time sequence data into a long short-term memory network, and predicting the capacity and internal resistance change of a plurality of cycles in the future; and in combination with a life end criterion, calculating the remaining service life, fusing the remaining service life with the degradation index of the aging model and the degradation index of the capacity model, generating a safety state index, and outputting a battery safety risk grade and corresponding early warning information. According to the invention, overall safety risk assessment can be provided for the whole battery system, accurate monitoring and early warning can be carried out on the single batteries or local modules, and the safety and reliability of battery operation can be improved.

The invention relates to the technical field of storage batteries, in particular to a storage battery monitoring, detection, treatment and evaluation integrated system and a storage battery management method, and the system comprises a plurality of accompanying modules, a collection power distribution control module, a storage battery management capacity checking host and a remote platform; the internal resistance analysis test of the system is based on multi-frequency-point alternating current excitation (0.5 Hz-7. 5kHz), a complex impedance spectrum of not less than 20 frequency points can be constructed, and the internal polarization characteristics of the battery are analyzed; the system adopts a feed network type discharge capacity checking, so that discharge electric energy is fed back to a power grid, and the capacity detection precision is improved to 98%; according to the system, EIS data and an LSTM neural network are fused, and a battery state of health (SOH) prediction model is established; the system is based on a DSP digital power supply technology, and can realize 50A active equalization topology.

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.

The invention discloses an energy storage system state evolution trend prediction method based on multi-source data fusion. The method comprises the steps of terminal voltage, current and temperature time sequence data acquisition, time sequence segmentation normalization, multi-physics field coupling feature construction, trend prediction model construction and training and energy storage system state evolution trend prediction. According to the method, the distinguishing capacity of the model for charging and discharging physical characteristics is improved, meanwhile, the voltage change rate, the multi-dimensional feature vector of the differential internal resistance and the thermal-electric coupling effect and the explicit encoding electric-thermal-resistance coupling relation are constructed, the transient response and the temperature hysteresis effect can be effectively captured, and then the model can be used for analyzing the charging and discharging physical characteristics. A degradation-aware cross-cycle feature extraction and gating mechanism is adopted, short-term fluctuation and long-term trend are adaptively balanced in multi-scale prediction, the prediction conflict problem is relieved, finally, physical constraints based on the electrochemical law and the internal resistance temperature characteristic are embedded in a loss function, it is ensured that the prediction result is accurate in numerical value and conforms to the physical law, and the prediction accuracy is improved. And generation of physically impossible solutions is avoided.

The invention relates to the technical field of electrical variable measurement, in particular to a control system and a control method of a battery measuring instrument. The method comprises the following steps: acquiring single voltage, total voltage, current and internal resistance data of a battery pack to obtain initial electrical parameters; collecting the surface temperature of the battery pack to obtain an initial thermodynamic parameter; performing synchronous time sequence integration on the initial electrical parameters and the initial thermodynamic parameters, and unifying formats to obtain a battery running state monitoring data set; and performing dynamic threshold determination and trend analysis on each parameter based on the battery operation monitoring data set to obtain an abnormal state identification result. By constructing a complete intelligent monitoring system covering data acquisition, processing, abnormity identification, state evaluation, communication packaging and visualization, high-precision real-time monitoring, fault pre-judgment and remote communication of the running state of the battery are realized, and the synchronism, the accuracy and the adaptability of the system and the intelligence and the practicability of field application are remarkably improved.

The invention discloses a lithium battery temperature state estimation method based on a physical information neural network, and relates to the technical field of lithium ion battery temperature state estimation, and the method comprises the following steps: obtaining multi-working-condition lithium battery charging and discharging data, and carrying out sliding window denoising and abnormal data elimination preprocessing; a battery thermal model containing total irreversible heat, reversible heat and heat dissipation is constructed by combining heat production and heat dissipation mechanisms, and a temperature change thermodynamic equation is obtained; establishing a physical information neural network, based on a residual network, embedding a time-varying internal resistance module and a time sequence feature extraction module based on the Arrhenius law, constructing a multi-component total loss function, and optimizing parameters through adaptive weight adjustment and an Adam algorithm; temperature state estimation is realized in training and prediction stages, the model is optimized and parameters are stored in the training stage, and a temperature result is output and verified in the prediction stage. The method gives consideration to both physical consistency and data fitting precision, and can support thermal management of the battery.

The invention relates to a lithium battery charging system, and relates to the technical field of battery charging, the lithium battery charging system comprises a charging module, a detection module and a data processing module, and also comprises a battery health assessment module used for collecting battery cycle index, internal resistance and capacity attenuation parameters, and constructing a battery health state assessment model; the power grid interaction module is used for acquiring real-time electricity price data and a power grid load state; the environment sensing module comprises an environment temperature and humidity sensor and is used for collecting environment temperature and humidity parameters; and the AI prediction module is constructed based on a deep learning network and is used for predicting a battery state change trend. According to the invention, the battery health evaluation module is arranged to collect parameters such as cycle index, internal resistance and capacity fading rate to construct the SOH evaluation model, the problem of single dimension of battery health state evaluation in the prior art is solved, the aging degree of the battery can be accurately identified, and the risk of overcharge and undercharge of the aged battery due to improper power adaptation is avoided.

The invention provides an energy storage power station optimization operation mode decision-making method and system, and relates to the technical field of energy storage power station optimizing.A hybrid prediction model is constructed to realize high-precision decomposition prediction of power load, and meanwhile, the internal resistance of a battery is estimated in real time by adopting a recursive least square method; and the battery capacity and internal resistance parameters are dynamically corrected in combination with a temperature compensation mechanism. Through health state multi-index fusion evaluation, self-adaptive distribution of charging and discharging power is achieved, and compared with the prior art, the problem that a traditional static model cannot adapt to complex environment changes is solved. The battery capacity fading risk can be predicted in advance by introducing a double-compensation mechanism of an environmental influence index and an electric power influence index. According to the scheme, the response speed and economical efficiency of energy storage in a high fluctuation load scene are remarkably improved, and a reliable dynamic optimization decision support system is provided.

The invention belongs to the technical field of intelligent traffic and new energy power supply, particularly relates to a traffic equipment solar power supply fault diagnosis system, and aims to solve the problems that a solar power supply system is not timely in fault diagnosis, low in precision and difficult to distinguish instantaneous interference and continuous faults. The system collects multi-source data through an environment sensing and electrical parameter monitoring module, generates a power deviation sequence and extracts time sequence characteristics by combining dynamic expected power modeling with actual output comparison; the fault identification module adopts a multi-level logic discrimination and 12-hour continuous verification mechanism, accurately identifies photovoltaic panel pollution, storage battery aging, poor line contact and controller faults, and distinguishes instantaneous interference; and the decision alarm module generates graded alarms according to fault types and grades, and realizes accurate positioning and operation and maintenance scheduling in linkage with geographic information. The system also has the functions of internal resistance pulse detection, dual-channel redundancy sampling, adaptive threshold adjustment and model self-learning, and the diagnosis accuracy and the operation and maintenance efficiency are significantly improved.

The invention discloses an energy storage liquid cooling unit control strategy generation method and device based on working condition prediction, equipment and a storage medium, and relates to the technical field of power grids, and the method comprises the steps: building a prediction model based on working condition data of an energy storage system, determining initial prediction power and predicted battery electric quantity based on a preset coefficient and the working condition data, the prediction model is used for correcting the initial prediction power to obtain target prediction power, the predicted heating power of the battery cell is determined based on the equivalent internal resistance of the battery cell and the voltage of the battery cell, the operation parameters of the liquid cooling unit are fitted based on a preset calibration coefficient to obtain the expected refrigerating capacity, and the predicted temperature of the battery cell is determined; adjusting the initial strategy by using the liquid cooling unit control rule and based on the temperature deviation degree and the temperature change rate to obtain a target energy storage liquid cooling unit control strategy so as to control the energy storage liquid cooling unit; the temperature deviation degree and the temperature change rate are data determined based on the preset battery working temperature range and the battery cell predicted temperature, so that the efficiency of generating the energy storage liquid cooling unit control strategy is improved.

The invention relates to a battery self-discharge characteristic analysis and rapid evaluation method, and aims to improve the precision and efficiency of battery self-discharge performance detection. According to the method, in a set environment stable interval, an instantaneous voltage change sequence and a current perturbation response sequence of a target battery are acquired through micro-load disturbance, and a battery internal resistance dynamic evolution tensor representing dynamic response characteristics in the battery is constructed; extracting local features by applying a multi-scale time window segmentation strategy, forming a feature vector group containing a voltage nonlinear rebound factor and a delay response index, inputting the feature vector group into a trained Bayesian weighted adaptive mapping network, and outputting a battery self-discharge rate estimated value combining a manufacturing batch residual error and temperature offset compensation; performing trend fitting and consistency checking on the estimated value to obtain a battery self-discharge grade evaluation result, and generating a structured detection report containing initial offset, rebound amplitude and estimation confidence; the method can be widely applied to battery production, quality control and performance screening scenes.

The invention discloses a ship power battery health state intelligent assessment method and system based on multi-feature fusion and a dynamic threshold value, and relates to the technical field of ship power battery management. The method comprises the following steps: synchronously acquiring single voltage, current, temperature, internal resistance and insulation resistance signals in real time through a signal acquisition layer; processing through a feature fusion layer to form a fusion feature vector; inputting the CBP-LSTM model to continuously output SOH and SOC estimation values in real time to form an SOH / SOC estimation sequence; calculating adaptive upper and lower threshold boundaries for the sequence based on an RLS algorithm; health levels are divided through comparison of the health deviation and a threshold value, a control instruction is generated and issued to a BMS / EMS system, and closed-loop protection and energy optimization are achieved. According to the method, the defects of insufficient feature dimension, high model rigidity, lack of adaptive ability and system closed loop deficiency in the prior art are overcome, the method has the advantages of high evaluation precision, strong robustness and good real-time performance, and the operation safety of the ship power battery can be effectively guaranteed.

The invention relates to the technical field of battery management, in particular to a battery replacement cabinet battery real-time monitoring and scheduling method, the health state of a battery is analyzed and evaluated according to a preset algorithm model, and the health state of the battery is evaluated by comparing a comprehensive health score, an internal resistance change rate and a capacity fading rate with corresponding threshold values; and determining a corresponding processing mode based on the evaluation result, including generating and sending abnormal alarm information to the operation and maintenance terminal, or executing an adjustable charging mode according to the state of residual electric quantity of the battery, or executing a protective charging mode according to the deterioration degree of the battery, the health state of the battery is actively evaluated to accurately identify an abnormal battery, for example, the electric quantity may be not low but potential safety hazards exist, in advance, so that targeted charging scheduling can be realized. According to the invention, real-time monitoring and active protection of the health and safety state of the battery are realized, so that the operation efficiency is improved on the premise of ensuring the safety.

The invention belongs to the technical field of energy storage system battery management and state monitoring, and discloses an energy storage system battery monomer consistency detection method and system considering charging and discharging time sequence characteristics. According to the method, historical operation data of each monomer is acquired and preprocessed, an equivalent circuit model and OCV-SOC mapping are established, and the SOC and ohmic internal resistance time sequence is estimated by adopting filtering recursion; screening key influence factors based on correlation analysis and constructing a consistency detection database; a charging and discharging process is extracted according to a single voltage rising edge / falling edge, and a complete charging and discharging cycle time sequence sample is generated; and clustering the samples, calculating the distance to a clustering center, judging an outlier according to an outlier threshold value, positioning inconsistent monomers, and performing adaptive optimization on the clustering category number and the threshold value to output a final result and an alarm level. The method is suitable for a large-scale energy storage operation scene, the detection accuracy, robustness and traceability can be improved, and the safety risk is reduced.

The invention provides a lithium ion battery health state estimation method and system based on a dual-drive interpretable integrated model, and belongs to the field of lithium ion battery health state estimation. The problems that an existing lithium battery health state monitoring method is single in feature source, insufficient in model generalization ability and poor in interpretability are solved. According to the method, based on an incremental capacity curve and a first-order RC equivalent circuit model, IC peak value features and ohmic internal resistance features are extracted, and a multi-source health feature space is constructed in combination with voltage statistical features; an integrated learning model based on Stacking is established, three basic models of a random forest, kernel ridge regression and an interpretable enhancement machine are integrated, and collaborative optimization of model hyper-parameters is realized by adopting a tree structure-based Bayesian optimization algorithm.

The invention discloses a battery pack full life cycle internal resistance evolution tracking method, which belongs to the technical field of battery state monitoring, and comprises the following steps: identifying an operation gap window in the operation process of a battery pack; applying a micro excitation signal to the battery pack in the operation gap window to generate a voltage response signal; orthogonal component extraction and amplitude calculation are carried out on the voltage response signal, and a current internal resistance measurement value is obtained through calculation; jointly storing the current internal resistance measurement value and the corresponding battery state parameter to obtain an internal resistance data sequence; performing trend extraction and deviation modeling on the internal resistance data sequence to obtain an updated internal resistance evolution prediction model; and performing health state mapping analysis on the updated internal resistance evolution prediction model to generate a battery health state evaluation parameter. According to the method, a dynamic excitation adjustment technology is adopted, and a multi-source data time alignment strategy is combined, so that accurate tracking of full life cycle evolution of the internal resistance of the battery pack and accurate evaluation of the battery health state can be realized.

The invention provides an intelligent charging pile control system based on a new energy automobile. The intelligent charging pile control system comprises a communication module, a data processing module, a charging power adjusting module, a safety protection module, a user interaction module, a cloud management platform, a power supply checking and recording module, an information backup and transmission module and a second-hand power supply monitoring module. The power supply checking and recording module is connected with a new energy automobile power supply system, detects and records parameters such as battery capacity and internal resistance, generates a power supply state report, realizes comprehensive detection and recording of the power supply parameters, provides data support for battery maintenance and fault diagnosis, assists an automobile owner to know the battery condition, and improves the safety of the automobile. A basis is provided for reasonable use and maintenance of a battery, basic data is also provided for subsequent battery management, the information backup transmission module locally backs up a power supply state report and transmits the report to a vehicle merchant service platform, the module realizes backup and transmission of power supply information, and a vehicle merchant can timely obtain detailed information of a vehicle power supply.

The embodiment of the invention relates to a battery equalization control method, device and equipment based on internal resistance evolution, after the internal resistance of a battery is compensated, equalization is started according to a voltage difference threshold value, and equalization dynamic adjustment is carried out through an equalization current value obtained through calculation, so that the false triggering rate of equalization starting and closing is reduced, and the battery equalization control efficiency is improved. The capacity attenuation rate of the battery pack is reduced through an internal resistance trend prediction dynamic adjustment strategy; therefore, the accuracy and risk controllability of dynamic balance control in the charging or discharging process of the battery are improved.

The invention discloses a power battery health degree online evaluation method and system based on a charging curve, and the method comprises the steps: dynamically dividing a characteristic interval of a constant-current charging stage and a constant-voltage charging stage through monitoring the voltage and current data in a charging process in real time; extracting time domain and frequency domain characteristic parameters in the characteristic interval; inputting the time domain and frequency domain characteristic parameters into a health degree evaluation model subjected to transfer learning optimization, and generating a fusion health degree index at least comprising a battery capacity attenuation coefficient and an internal resistance change vector; and outputting a final health degree evaluation result and residual service life prediction through an adaptive weighting algorithm based on the fused health degree index in combination with historical cycle data and operating environment parameters of the battery. According to the embodiment of the invention, high-precision and non-intrusive online evaluation and life prediction of the health degree of the battery can be realized, and the real-time performance, accuracy and engineering applicability of evaluation are improved.

The invention discloses a state monitoring method and system for an outdoor emergency power supply. Firstly, original data such as battery internal resistance, charging efficiency, temperature fluctuation and load abrupt change are collected to form an initial data set; and then noise suppression and fusion processing are carried out on the data set to obtain a smoothed battery parameter sequence. And constructing a dynamic trend vector by analyzing the differential change of the sequence, judging a potential anomaly level according to the slope of the dynamic trend vector, and outputting an anomaly probability score. And identifying an inter-parameter interaction cluster based on clustering analysis, and performing causal reasoning on a dominant cluster to determine a fault dominant factor. And finally, in combination with the real-time state, the environmental parameters and historical defect information, constructing a modeling matrix, and executing multi-dimensional clustering to identify specific fault types such as bearing wear, insulation aging and the like. If the fault is caused by internal resistance abnormity, an interference source is further positioned, and an accurate fault position is output. According to the invention, the battery fault monitoring accuracy and positioning capability in a complex environment can be effectively improved.

The invention relates to the technical field of battery management, and particularly discloses a dynamic control method for an intelligent balance management system of a lithium battery module, which comprises the following steps of: acquiring load current of the battery module and end voltage signals of each monomer in real time; calculating instantaneous internal resistance and polarization voltage parameters of each single battery in real time; the terminal voltage is compensated in real time, and a purified open-circuit voltage signal is generated; fusing the charge state reference value with the current integral result, dynamically calculating a confidence index according to a current standing condition and voltage stability so as to perform adaptive weighting, and outputting a smooth charge state estimation value; calculating a balance urgency degree index by combining the estimated value, the real-time temperature and the inconsistency change trend, and generating a differentiated balance control instruction; an energy transfer path is established and controlled according to the instruction, the rate is dynamically adjusted in the process, and operation is terminated when the duration or state consistency condition is met; according to the method, the stability of state estimation and the accuracy, safety and efficiency of balance control under the dynamic working condition are improved.

The invention relates to the technical field of battery management, and discloses a hybrid battery module equalization method and system based on battery cell health state identification, and the method comprises the steps: carrying out the multi-dimensional state fusion evaluation of a battery cell based on the voltage, temperature and total loop current of the battery cell in a battery module, and obtaining the charge state and health state of the battery cell; based on a preset voltage difference threshold value, performing threshold value judgment on the voltage range of the battery cell to obtain a diagnosis trigger signal of the battery cell; carrying out capacity cross validation on the charge state and the health state to obtain a determination result of capacity inconsistency of the battery cell; carrying out abnormal fluctuation analysis on the voltage change characteristics of the battery cells to obtain an internal resistance inconsistency judgment result of the battery cells; carrying out equalization strategy decision on the battery cell to obtain an equalization mode instruction of the battery cell; performing equalization operation on the battery cell according to the equalization mode instruction; according to the invention, the equalization rate of the hybrid battery module based on cell health state identification can be improved.

The invention relates to the technical field of mobile energy storage charging piles, and discloses a fault detection method and system for a mobile energy storage charging pile, and the method comprises the steps: collecting a voltage and current original sequence during an operation period, and executing the Kalman filtering to obtain a smooth electrical parameter; adaptively adjusting a process noise covariance matrix by using numerical dispersion to obtain a stable internal resistance value sequence; extracting a fluctuation trend and matching a working condition mode to filter periodic disturbance to obtain a pure internal resistance offset sequence; extracting a health degradation rate slope, establishing a dynamic health reference range by using kernel density estimation, and judging an abnormal migration event according to the dynamic health reference range; the offset severity is calculated through severity probability weighted fusion, the fault early warning level is obtained, and an event trigger counter reset mechanism is driven to judge the overall health state. The method can solve the problem of low fault detection accuracy in the prior art.

The invention discloses an echelon utilization method for waste power batteries of new energy vehicles, which comprises the following steps: firstly, collecting and preliminarily classifying and screening retired batteries according to health states, and quickly eliminating potential safety hazards by using infrared thermal imaging and voltage consistency detection; comprehensive performance detection including complementary energy detection, discharge capacity test at different temperatures, direct current internal resistance measurement and charge and discharge temperature rise test is carried out; key parameters are extracted from the detection data, comprehensive performance analysis is carried out in combination with application scene requirements, and a comprehensive evaluation score is generated; selecting similar batteries to form a pre-combination unit according to the score sequence; a grouping result is optimized through a clustering algorithm in the three-dimensional performance coordinate system, and it is ensured that the deviation of core parameters of the same group of batteries is smaller than a set threshold value; the stability and the operation efficiency of the echelon utilization system are improved, the adaptive capacity of the echelon utilization system in different application scenes is enhanced, and a guarantee is provided for stable operation of the echelon utilization battery system.

The invention belongs to the technical field of batteries, and particularly discloses a secondary battery, a battery pack and an electric device. A negative electrode film layer of the secondary battery contains an additive; the additive comprises a carboxymethyl cellulose compound and a modified compound thereof; the compound is beneficial to improving the ionic conductivity of alkali metal ions and reducing the diffusion polarization phenomenon, so that the internal resistance of the battery is reduced. And the content of the additive in the negative electrode film layer close to the current collector is controlled to be higher than the content of the additive in the negative electrode film layer far away from the current collector, so that the technical effects of reducing the internal resistance of the battery, controlling the cost and reducing the influence on the energy density of the battery are achieved.

The invention relates to the technical field of battery modeling and photovoltaic energy storage systems, and discloses a self-adaptive battery modeling method for direct current bus voltage support of an optical storage system. The method comprises the following steps: establishing a basic model comprising a direct-current voltage source, an ohmic internal resistor and an RC polarization network; the voltage drop depth is obtained by monitoring the voltage of the direct-current bus in real time and used for correcting the polarization resistance; and performing differential operation on the voltage to obtain a change rate so as to adjust the polarization capacitance. And dynamically optimizing the battery model by combining correction results of the two, and realizing adaptive updating of model parameters. And applying the optimization model to the optical storage integrated grid-connected system. The adaptability of the battery model to the dynamic working condition is improved.

The invention relates to the technical field of new energy automobile and battery safety, and discloses a power battery thermal runaway risk early warning method, which comprises the following steps: synchronously acquiring battery internal resistance dynamic change, characteristic gas concentration and temperature gradient distribution through a multi-physics field coupling sensor network, and constructing an electrochemical-thermal-gas ternary early criterion; the internal resistance mutation rate is calculated through self-adaptive sliding window differential, the gas precipitation rate and the temperature space heterogeneity index are combined and input into a multi-branch fusion neural network containing a gating attention mechanism, the continuous thermal runaway risk probability is output, and corresponding early warning is triggered according to grading threshold values. The system comprises an internal resistance, gas and temperature monitoring module and a multi-modal fusion evaluation and grading early warning execution module. According to the method, the traditional early warning logic singly depending on a temperature threshold value is abandoned, a ternary early criterion system with the internal resistance abrupt change rate, the characteristic gas precipitation rate and the temperature space heterogeneity index as the core is constructed for the first time, and electrochemical and physical field anomalies caused by thermal runaway precursor events such as lithium dendrite piercing of a diaphragm and SEI membrane rupture can be effectively captured.

The invention discloses a storage battery grouping online capacity checking method and system fusing a health degree evolution model, and relates to the technical field of storage battery management, and the method comprises the following steps: S1, building a sensitive baseline, injecting a controlled short pulse, and measuring a voltage hysteresis area and a platform repetition rate for a tiny circulation working condition at the end of each charging and discharging period; generating a memory early warning index; s2, after the memory early warning index is obtained, an induction test is executed, and a grading threshold value is formed by comparing the capacity hysteresis residual error, the internal resistance transition rate and the open-circuit voltage deviation in the short-time charging and discharging process. According to the method, high-precision online health accounting and grouping differentiation operation limitation are realized through multi-parameter dynamic detection and grading judgment, and synchronous iteration of a management system and working conditions is guaranteed by matching operation result real-time write-back and baseline self-adaptive updating. The system can identify high-risk monomers in advance and dynamically group and reconstruct the high-risk monomers, thereby preventing failure and improving the safety and energy efficiency utilization rate of the battery pack.