1873results about "Secondary cells testing" patented technology

Disclosed is a BMS-based battery energy storage safety management system. The system comprises a BMS module, a control center module, an evaluation analysis module, a coupling analysis module, a prediction module, and a data storage module. The BMS module collects monitoring data of a battery under different operation states and then, according to the monitoring data, performs initial monitoring of a real-time health state of the battery. The evaluation analysis module, according to a change in data indicators in the monitoring data, evaluates and analyzes all fault analysis models in the BMS module to obtain an evaluation analysis result. Then the coupling analysis module combines the evaluation analysis result with the data indicators of the monitoring data to retrieve multidimensional spatio-temporal data, and constructs a coupling field model to perform coupling analysis on battery operation state. The prediction module then constructs a fault prediction model by using a coupling data set as training data, accurately locates coupling faults of different coupling surfaces, and implements comprehensive analysis and location of coupling faults.

An inspection method for a lithium secondary battery can improve the reliability, accuracy, and reproducibility of inspection results by advancing a learning of an artificial neural network according to one or more divided regions of one or more surfaces of the lithium secondary battery and one or more types of defects occurring in each region.

The invention provides a ship power battery state monitoring method and system based on multi-sensor data fusion, and relates to the technical field of batteries, and the method comprises the steps: interactively obtaining multi-mode monitoring data of a ship power battery in a ship matched intelligent power distribution system; generating a mapping confidence score by using the expected output prediction of the reliable twin of the sensor and the residual error of the multi-modal monitoring data; the method comprises the following steps of: after preprocessing multi-modal monitoring data, respectively extracting transient, trend and accumulation characteristics based on a multi-time scale sliding window; inputting the multi-modal feature set into a hierarchical fusion model, executing coupling fusion analysis in a module layer and a cabin ship layer, and outputting comprehensive operation state features; constructing an abnormal probability value according to a multi-dimensional abnormal index calculation result and the mapping confidence score; and monitoring and managing the state of the ship power battery according to the abnormal probability value. According to the invention, the technical problem of low monitoring efficiency of the state of the ship power battery in the prior art can be solved.

The invention discloses a lithium ion battery thermal runaway multistage early warning system and method and a storage medium, and belongs to the field of battery safety monitoring. The system comprises a data acquisition device, a feature extraction module, an early warning strategy module and an early warning model module. The method comprises the following steps: collecting multi-physical field parameters of heat, electricity, force and gas; screening key feature points by adopting a mutual information method; mining association rules of feature points and early warning levels based on an Apriori algorithm to generate a four-level early warning strategy; and constructing an early warning model integrating grade evaluation and remaining time prediction. According to the method, through multi-parameter coupling analysis and intelligent algorithm mining, the defects that the prior art depends on a single parameter and lacks grading capacity are overcome, early-stage, accurate and grading early warning and remaining time prediction of lithium ion battery thermal runaway are achieved, and the safety early warning precision and emergency disposal capacity of a battery system are remarkably improved.

The invention relates to the technical field of electromagnetic measurement, and discloses a lithium battery life monitoring and health state autonomous protection system and method, and the system comprises a data collection module, a trend consistency discrimination module, a health assessment report generation module, a health deviation degree verification module, a deterioration risk deduction module, and an autonomous protection strategy generation module. The method comprises the following steps: collecting lithium battery operation multi-dimensional electrical parameters, and eliminating abnormal fluctuations through a time sequence window to obtain a time sequence correlation parameter set; generating an initial health assessment report based on health state feature matching analysis, and verifying the initial health assessment report with historical data to obtain a health deviation degree; worsening risk deduction is carried out in combination with the parameter trend change rate, optimized health assessment and life early warning are output, and corresponding autonomous protection strategies are matched and started according to the optimization health assessment and life early warning; according to the invention, the life monitoring and health state autonomous protection efficiency of the lithium battery can be improved.

The invention is suitable for the technical field of battery safety monitoring, and provides a sodium ion battery energy storage system health state dynamic prediction method based on simulation digital twinning, and the method comprises the steps: constructing a digital twinning body comprising a physical layer, a virtual layer and a data interaction layer; performing health state estimation based on the physical layer and the virtual layer, and outputting a health state estimation value; comparing the health state estimated value with the measured value, calculating a residual error, and updating key aging parameters in the simulation model; inputting the updated key aging parameters into a pre-trained time sequence neural network model, carrying out sequence learning, and outputting a capacity attenuation trend and a residual life prediction result of the battery; generating a full life cycle optimization strategy based on the prediction result, and generating an optimal charge-discharge power table by simulating the influence of different charge-discharge strategies on the life; the problems of life attenuation and high operation and maintenance cost caused by insufficient prediction precision in the prior art are effectively solved.

A pre-lithiation diagnostic device according to one aspect of the present invention may include: a memory unit that stores a positive electrode reference start value and a negative electrode reference start value for a reference battery; and a control unit that is configured to acquire a positive electrode target profile and a negative electrode target profile for a target battery to be diagnosed, recognize the positive electrode target start value and the negative electrode target start value from the positive electrode target profile and the negative electrode target profile, and determine a lithium charge amount for a pre-lithiation process for the target battery based on at least one of the positive electrode reference start value, the negative electrode reference start value, the positive electrode target start value, and the negative electrode target start value.

Methods and systems may reliably predict the lifespan characteristics of a lithium secondary battery, for example, the mode of variation in cycle capacity in advance. A method may include a first step of subjecting a lithium secondary battery in a form of a blocking cell to an impedance spectroscopic analysis, a second step of deriving a relationship capacitance for each frequency and a calculating a charge amount, a third step of repeatedly performing the first and second steps, and a fourth step of measuring a capacity for each cycle of the lithium secondary battery and deriving a prediction expression of the capacity for each cycle of the lithium secondary battery.

A battery heating system includes: a temperature monitoring circuit configured to output a temperature monitoring signal; a voltage conversion circuit configured to receive a first voltage that is input by a power supply or a second voltage that is input by a to-be-heated battery; and a control circuit configured to receive the temperature monitoring signal and output a control signal. The voltage conversion circuit is configured to perform boosting processing or bucking processing on the first voltage based on the control signal or perform boosting processing or bucking processing on the second voltage based on the control signal to enable the to-be-heated battery to receive a charging current from the power supply in a first time segment using the voltage conversion circuit and the to-be-heated battery to output a discharging current to the power supply in a second time segment using the voltage conversion circuit.

Systems and methods are provided for testing and / or validating anti-propagation systems designed to mitigate a thermal runaway condition in lithium-ion batteries. A system comprises a first temperature sensor for detecting a temperature of a trigger battery cell within the battery module and a heater removably couplable to a substantially central portion of the trigger cell and configured to heat the trigger cell to a threshold temperature. The system further comprises a second temperature sensor for detecting a temperature of a test battery cell adjacent to the trigger cell within the battery module. The trigger cell undergoes a thermal runaway condition upon reaching the threshold temperature. The system may further comprise a controller for controlling the rate of heating of the trigger cell. The system ensures that the trigger cell will breach via the pressure release valve or cell vent after reaching the threshold temperature.

A battery monitoring system, for monitoring condition of a plurality of batteries, includes first and second pluralities of battery testers each coupled to a storage battery stored at a respective first and second storage location The plurality of battery testers including test circuitry configured to test a storage battery and to generate test data, a memory configured to store battery tester identification information, and communication circuitry configured to transmit the test data and the identification information over a wireless communication medium. A gateway includes wireless communication circuitry configured to receive test data and battery tester identification information from the plurality of battery testers and further includes cloud communication circuitry configured to communicate the test data and battery tester identification information to a cloud facility The cloud facility includes communication circuitry configured to receive the test data from the gateway, a report output configured to report the test data, and a database which links battery tester identification information with battery tester location information.

The embodiment of the invention provides a battery health state detection method, device, equipment, system, medium and product, and the method comprises the steps: determining a local density difference feature of a battery state parameter at a current moment relative to a battery state parameter at an adjacent moment in battery state data; the local density difference feature can represent the difference between the battery state parameter at the current moment and the battery state parameter at the adjacent moment, so that the battery health state can be accurately determined based on the local density difference feature, and the detection accuracy of the battery health state is improved.

The invention provides a lithium ion battery SOC multi-model fusion estimation method based on a neural network, and belongs to the technical field of lithium ion battery management, and the method comprises the steps: carrying out the segmentation preprocessing of the current, voltage and temperature data of a battery according to the charging and discharging direction; fitting an SOC-OCV relation curve based on a second-order RC equivalent circuit model, and optimizing dynamic response parameters; synchronously predicting OCV and SOC by adopting a multi-task neural network; a high-precision SOC initial value is obtained by looking up a table through an OCV predicted value, and dynamic updating is carried out in combination with an ampere-hour integral method; comparing the ampere-hour integral result with the SOC directly predicted by the neural network in real time, and dynamically fusing according to an error; for a long-term standing scene, a life model is constructed based on historical data to predict SOH, the nominal capacity is updated, and the integral reference is reset. According to the method, a physical model and a data driving method are fused, and the SOC estimation precision and reliability under complex working conditions and long-term standing are improved through dual-path collaborative estimation and SOH closed-loop calibration.

The invention relates to the technical field of lithium batteries, in particular to a lithium battery formation and capacity grading test health monitoring and performance diagnosis method and system. Comprising the following steps: acquiring voltage data, and identifying a target single cell according to the voltage data; a bypass current channel is gradually established for the target single battery cell, so that the bypass current is gradually increased from zero or an initial value according to the change speed of the bypass current; acquiring current change data of the bypass current channel in real time during the establishment period of the bypass current channel, and adjusting the establishment slope and / or the segmented switching time sequence of the bypass current according to the current change data; and when the bypass equalization operation is finished, gradually reducing the bypass current according to the corrected bypass current change speed until the bypass current channel is removed. The problems that in the lithium battery formation and capacity grading test process in the prior art, potential accumulative damage is caused by bypass equalization operation, and transient influences are difficult to accurately capture and evaluate by existing test equipment are solved.

The disclosure relates to an apparatus for charging or discharging including a case, a charging or discharging module unit and an air treatment unit provided inside the case, an equipment temperature sensor located in the case and sensing an equipment temperature, a module hole formed by communicating with the outside, and a controller charging or discharging, where the controller is configured to discharge at least a portion of the air discharged from the air treatment unit to the outside of the case through the module hole based on usage current of the charging or discharging module unit and the equipment temperature, a battery charging or discharging system including the same, and a control method thereof.

The invention discloses a fast charging MAP making method and device, and the method comprises the steps: placing a target cell with three built-in electrodes in an environment at a target temperature, and carrying out the charging at a current multiplying power; the current surface temperature and the current voltage of the target battery cell are detected, and the current heat production amount of the target battery cell is calculated according to the current surface temperature and the current voltage; calculating the current roll core temperature of the target battery core according to the current heat production amount and the roll core thermal resistance of the target battery core; calculating the current theoretical water temperature of a cooling system of the target battery cell by using the current heat production of the target battery cell and the current roll core temperature based on the fact that the current heat production of the target battery cell is equal to the external heat dissipation amount; adjusting the water temperature of the cooling system based on the current theoretical water temperature; and when the three electrodes reach the lithium precipitation limit potential, reducing the current multiplying power, recording the current SOC, and returning to charge the target battery cell according to the reduced multiplying power until the target battery cell is charged to the target SOC.

The invention discloses a battery internal short circuit detection method and device which are applied to a battery pack comprising a plurality of single batteries, and the method comprises the steps: obtaining the current information of the battery pack and the voltage information of each single battery; constructing observed quantity representing monomer difference, and estimating a first difference parameter representing monomer voltage source characteristic difference and a second difference parameter representing monomer impedance characteristic difference on line by adopting nonlinear filtering to obtain a corresponding time sequence; segmenting the time sequence according to a preset operation period, and performing weighted convergence according to weights related to electric quantity changes to obtain corresponding characteristic parameters; and constructing a first statistical discrimination quantity and a second statistical discrimination quantity which reflect extreme value deviation and fluctuation deviation based on the periodic characteristic parameters, performing joint discrimination with a threshold value, and determining an internal short circuit when abnormal monomers are consistent and meet a continuous periodic criterion. According to the method, through difference observation, filtering estimation, periodic weighting and double-discriminant-quantity combined judgment, the discriminant stability and reliability under working condition fluctuation and noise interference are improved.

A battery cell inspection system includes a photographing unit configured to obtain an electrode image of a battery cell, a memory storing a learning model trained to track an electrode endpoint in an electrode image of a battery cell, and a controller. The controller is configured to track the electrode endpoint in the electrode image of the battery cell by using the learning model, and to determine whether an electrode of the battery cell is defective, based on coordinate information of the electrode endpoint. A battery cell inspection method is also provided.

A method of generating a State of Health (SOH) estimate for a battery is disclosed. The method may include charging a battery to a reference point and obtaining a battery cell temperature at the reference point, obtaining an instantaneous impedance of the battery cell based on the battery cell's response to an applied probing waveform, and obtaining a SOH estimate based on the battery cell temperature and the instantaneous impedance. Methods of generating a State of Charge (SOC) estimate for a battery and of determining an amount of remaining device usage are also disclosed.

Provided are a method and apparatus for calibrating contact parameters of electrode materials, and a computer storage medium. The method includes the following operations. A discrete element simulation test is performed on an electrode material based on a calibration model and calibration ranges of contact parameters of the electrode material when an axial force-displacement curve of a target pellet of the electrode material meets an adaption condition of the contact model; then a simulated axial pressure-axial strain relationship of the electrode material is acquired based on a target discrete element simulation parameter of the electrode material; whether the contact parameters are calibrated successfully is judged based on this relationship; and when the contact parameters are calibrated successfully, calibration results of the contact parameters are determined.

A battery diagnostic device according to one embodiment disclosed in this document may include an acquisition unit that acquires a balancing execution time of a battery unit, a calculation unit that calculates at least one balancing execution ratio based on the balancing execution time, and a diagnosis unit that compares a specified balancing execution ratio corresponding to a specified period with a reference ratio and diagnoses an abnormality in the battery unit.

The present disclosure provides a battery welding defect detection system, method, device, and storage medium, wherein the system includes: the acquisition processing module, configured to acquire the welding image of the battery welding object and pre-process the welding image to obtain the processed welding image; the defect statistic module, configured to classify and count the processed welding image based on the target detection model to obtain the plurality of battery candidate frames; and the detection analysis module, configured to obtain the corresponding battery detection result according to the preset threshold and the battery candidate frames by the target detection model. The present disclosure performs the defect defection on the processed battery welding image based on the target detection model, thereby significantly improving the detection efficiency and the accuracy of the results, and at the same time effectively reducing the production cost.

A diagnostic device, a battery manufacturing system, a battery pack, an electric vehicle, and a diagnostic method are provided. The diagnostic device according to the present invention is a diagnostic device for diagnosing the states of first to n-th active materials (n is a natural number of 2 or more) included in a battery electrode (the electrode is a positive electrode or a negative electrode), and includes: a profile acquisition unit configured to acquire a target electrode profile indicating a correspondence relationship between the capacity and voltage of the electrode; and a diagnostic unit configured to generate first to m-th simulation electrode profiles (m is a natural number of 2 or more) from predetermined first to n-th reference active material profiles, individually compare the first to m-th simulation electrode profiles with the target electrode profile, and diagnose the states of the first to n-th active materials based on the comparison results.

The invention discloses a battery management method based on a physical information neural network. The battery management method comprises the following steps: step 1, constructing a lithium battery electrochemical-thermal coupling physical model; 2, a physical information neural network is constructed, the lithium precipitation risk coefficient of the lithium battery is predicted through the physical information neural network, and in the basic training process of the physical information neural network, the physical model is embedded into a loss function of the physical information neural network; 3, constructing model predictive control, and embedding the lithium precipitation risk coefficient into a target function of the model predictive control; and 4, calling the physical information neural network at a preset interval time to predict the lithium precipitation risk coefficient of the lithium battery, and optimizing the charging current curve through model prediction control. The charging strategy is dynamically optimized, the risk of lithium precipitation is reduced, collaborative optimization of battery safety, service life and charging efficiency is achieved, and the method is suitable for high-dynamic scenes such as electric vehicles and energy storage systems.

The invention relates to the technical field of battery simulation, in particular to a temperature adjustment method and system for simulating a battery based on a battery simulator. The method comprises the following steps: collecting the material thermal conductivity, thickness and layer number information of the battery cell lamination structure, and recording the information as battery cell lamination structure data; performing thermal resistance calculation on each layer of the battery cell according to the battery cell lamination structure data to obtain thermal resistance distribution data of each layer of the battery cell; performing space-time inversion simulation on the heat accumulation condition in the battery cell in the rapid charging process to obtain battery cell heat accumulation simulation data; and determining current density distribution data in the battery cell in the rapid charging process according to the thermal resistance distribution data of each layer of the battery cell and the battery cell heat accumulation simulation data. Through data monitoring technology data and data simulation technology, thermal resistance distribution calculation of heat accumulation in the battery cell is realized, accurate simulation of a heat accumulation path is realized, cooling efficiency is improved, and battery safety is enhanced.

A battery case analyzing apparatus includes a sealing portion which covers an injection hole formed on an upper side of a battery case. The sealing portion includes a lower side contacting the upper side of the battery case, a gas injection passage, and a vacuum forming passage formed in the sealing portion. The apparatus includes a gas injection line which is connected to an inlet of the gas injection passage formed on an upper side of the sealing portion, a vacuum forming line which is connected to an outlet of the vacuum forming passage formed on the upper side of the sealing portion, a gas supply portion configured to inject gas into the battery case through the gas injection line, and a vacuum pump which is connected to the vacuum forming line to form a vacuum in a vacuum forming groove formed on the lower side of the sealing portion.

The invention relates to the technical field of artificial intelligence, in particular to an electrochemical energy storage battery overcharge safety early warning method based on a large model, which comprises the following steps: collecting a literature data set, an experiment data set, an expert experience set and phenomenon description information; then, energy storage mechanism entities are extracted through a BERT-NER algorithm, and a mechanism map is constructed in a pairing mode; collecting energy storage bin detector data in real time, calculating a time sequence characteristic change rate, and generating a detection cue word in combination with phenomenon description; and inputting the mechanism graph and the cue word into a large language model for training, and generating a prediction result. According to the method, multi-source data are fused, the overcharge risk is effectively early warned through large model analysis, and safe operation of the electrochemical energy storage battery is ensured.

A method for inspecting a pouch battery cell for liquid leakage, the method including bringing a first electrolyte reaction sheet into contact with a top surface of the pouch battery cell, visually inspecting a change in a color of the first electrolyte reaction sheet, bringing a second electrolyte reaction sheet into contact with a bottom surface of the pouch battery cell, and visually inspecting a change in a color of the second electrolyte reaction sheet.

An electrode manufacturing system according to an exemplary embodiment of the present disclosure includes: a coating part comprising a first coating part for applying an electrode slurry to a first surface of a current collector and a second coating part for applying an electrode slurry to a second surface of the current collector; an air spraying part; and a drying part, wherein the air spraying part is disposed on a transfer path between the second coating part and the drying part. By moving an electrode slurry in the direction of an uncoated part by spraying air at a fat-edge of a second electrode active material layer caused by a sliding region of a first electrode active material layer formed on the first surface of the current collector, the formation of the fat-edge can be prevented.