2868results about "Secondary cells testing" patented technology

The invention relates to the technical field of battery management, in particular to a battery module early abnormality early warning method and system fused with a time sequence knowledge graph, and the method comprises the steps: obtaining static parameters and dynamic sensor data of each battery node in a battery module, and constructing the time sequence knowledge graph; performing node-relation importance evaluation of topology embedding, and screening out key nodes and relations; constructing heterogeneous knowledge unified mapping of multi-manifold representation, and projecting battery voltage, temperature and state manifolds to a unified representation space; performing spectrogram-driven adaptive Transform structure optimization on the data of the uniform representation space, and performing spectrogram-driven adaptive Transform structure optimization on the data of the uniform representation space; bidirectional coupling graph-sequence information flow cooperative processing is realized, graph structure information is converted into sequence representation, and a graph structure is fed back and updated; carrying out multi-scale feature hierarchy fusion and integration, and integrating hierarchy feature information of a battery monomer, a module and a system; and evaluating the health state of the battery module based on the multi-scale fusion feature information, and generating early warning information in an abnormal state or an abnormal state.

The invention discloses a distributed electrochemical energy storage fire alarm system based on an intelligent algorithm, and relates to the technical field of energy storage battery safety monitoring. Technical processes of multi-source data acquisition and preprocessing, key health factor extraction, multi-modal feature fusion, depth model detection, cross-system intervention and adaptive strategy optimization are provided; a high-quality data stream is generated through real-time synchronous sampling and noise filtering, weak fault symptoms such as internal short circuit or material deterioration are highlighted by applying a multi-dimensional feature structure, a deep learning model is utilized to accurately recognize and output early warning, and then load reduction, isolation and temperature control measures are carried out in cooperation with a BMS, an EMS and a fire fighting system. An evolutionary algorithm or a reinforcement learning continuous iteration intervention strategy is introduced in a simulation environment, high-sensitivity capture and rapid suppression of latent risks under multi-scene complex working conditions are achieved, the rate of missing report and false report can be remarkably reduced, and the overall safety of the system is remarkably enhanced.

The invention relates to the technical field of fire-fighting management, and discloses an intelligent fire-fighting service management system, which comprises a data acquisition unit used for acquiring three basic parameters, namely environment data, working condition data and battery data, of a fire-fighting object in an office building, and the system acquires the environment, working condition and battery data of a fire extinguisher through the data acquisition unit; the fusion unit generates a dynamic composite abnormal threshold value, and the adjustment unit executes a three-stage sampling frequency adjustment mechanism according to the threshold value, the battery health degree score and the data deviation degree, which means that the sampling frequency can be reduced to reduce the battery energy consumption when the state of the fire extinguisher is normal and the risk is relatively low, and when the risk is increased or the battery health degree is reduced, the battery energy consumption is reduced. The whole system realizes accurate management and regulation of the battery according to the actual working condition of the fire extinguisher, energy waste is avoided, the service life of the battery is prolonged, the cost of frequently replacing the battery is reduced, and the sustainability of intelligent fire control management is enhanced.

An X-ray inspection device according to one embodiment of the present disclosure includes: an X-ray output portion irradiating X-rays to a battery including a cathode, a separator, and an anode in a stacking direction of the cathode and the anode; an X-ray detection portion acquiring a plurality of gray values based the X-rays that have transmitted through the battery; a signal processing portion acquiring an X-ray image including the plurality of gray values; and an inspection portion determining whether the battery is defective based on a distance between a first edge of an anode area including gray values representing the anode layer and a second edge of a cathode area including gray values representing the cathode layer in the X-ray image.

The invention discloses a full-immersion type liquid cooling energy storage system and a control method thereof, and relates to the technical field of energy storage control, and the method comprises the steps: installing multiple sensors in a liquid immersion tank, carrying out the calibration, collecting the state data of a battery module and cooling liquid in real time through the calibrated multiple sensors, and obtaining the state data of the battery module and the cooling liquid; the state data of the battery module and the cooling liquid are preprocessed and uploaded to a control center, a Transform model is constructed in the control center, historical state data of the battery module and the cooling liquid are used for training, the trained Transform model is obtained, the preprocessed state data of the battery module and the cooling liquid are input into the trained Transform model, and the state data of the battery module and the cooling liquid are obtained. And obtaining prediction results of the high-risk area position and the expected temperature change value. The invention provides an intelligent control strategy and a multi-sensor network based on a Transform model, and local cooling is realized by monitoring the temperature change of the liquid cooling energy storage equipment in real time and adjusting the flow and path of the cooling liquid in real time.

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

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

The invention relates to the technical field of energy storage technology, integrated circuit design and battery management systems, and discloses a passport-sensing integrated battery management chip and a battery management system architecture, battery information is collected based on the passport-sensing integrated battery management chip, and unique battery passport information is generated. And then real-time interaction with the BMS main control module is carried out through the daisy chain communication module, so that the health state of the battery is evaluated and managed. The battery cell state data acquisition and traceability functions are realized through the battery cell slave board, the BMS master control module has a fine management function on the battery state, battery safety early warning and daisy chain communication channels are provided to realize real-time interaction between the battery cell slave board information and the BMS master control module information, online and offline information disclosure modes are fused, a collaborative management architecture is established, and the battery cell state data acquisition and traceability functions are realized. Intelligent management and data tracking of the whole life cycle of the battery are realized, and the safety, reliability and traceability of the battery are improved.

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.

A test method, system, apparatus and device for an energy storage control system, and a storage medium. The test method comprises: receiving test indication information (S201); on the basis of the test indication information, simulating an associated device connected to a device to be tested in an energy storage control system, and determining simulation-associated parameters (S202); and on the basis of the simulation-associated parameters and the test indication information, testing the device to be tested, and receiving a test result of the device to be tested (S203). A test device (104) can implement the simulation of any device associated with a device to be tested in an energy storage control system. Specifically, interface implementations and function execution logic implementations of any device can be simulated, and the simulated interface implementations or function implementations are implemented by means of simulation-associated parameters. Therefore, the test process is not limited to testing a specific type of device to be tested, and multiple types of tests can be implemented, thereby improving the applicability of the test method to a certain extent.

The present application provides a battery thermal runaway detection method and apparatus. The method comprises: acquiring a time series signal generated when a gas sensor identifies a gas component released by a battery, and converting the time series signal into an image signal, so as to strength key features in the signal; processing the image signal by means of a target detection model, so as to predict a gas type and a gas concentration; and then determining a thermal runaway risk of the battery on the basis of a prediction result. Therefore, by introducing a machine learning algorithm, simple gas identification requirements are met, and complex data analysis and prediction can be carried out, thereby accurately predicting the thermal runaway risk of lithium batteries, and effectively improving the early warning precision of thermal runaway.

To provide an information collection device and a battery pack which improve the sealing performance of a signal collection device.SOLUTION: The present disclosure relates to the field of battery technologies, and provides an information collection device and a battery pack. The information collection device includes a casing, an air pressure sensor, and a sealing member. The casing has a first installation hole. The air pressure sensor is installed inside the casing, and at least a part of the air pressure sensor is snap-fitted into the first installation hole to acquire an air pressure parameter outside the casing. The sealing member seals the first installation hole, and the sealing member is used to allow gas to pass through and prevent liquid from passing through. Thereby, the information collection device can be arranged in a battery cell, and can collect information in an environment of electrolyte, thus capable of mitigating the technical problems of complex structure and insufficient reliability of a cover plate of the battery cell caused by external arrangement of the information collection device.SELECTED DRAWING: Figure 1

The invention provides a battery system health state equalization control method based on an LSTM-D3QN algorithm, and relates to the technical field of battery management, and the method comprises the steps: obtaining the historical health state value of each single battery based on the constant current complete discharge process of a plurality of single batteries; performing feature extraction based on the historical health state value of each single battery in the battery system to obtain aging features of the single batteries; inputting the aging features to an LSTM network to obtain a health state prediction value evolved along with time; and inputting the state space including the health state prediction value into the constructed Markov decision process, and generating a switch control strategy of the battery system in the current state by using a D3QN algorithm. According to the invention, efficient equalization control can be realized for the reconfigurable battery system with SOH inconsistency.

An apparatus for safety testing a battery pack of an electric vehicle includes a low voltage data connector configured to couple to a battery management system controller of the electric vehicle battery pack for data communication with the battery management system controller. A test connector is configured to couple a battery module of the electric vehicle battery pack for use in testing the battery module. Measurement circuitry measures a parameter of the battery module through the test connector. A diagnostic controller couples to the low voltage data connector and the measurement circuitry and is configured to identify an error in data received from the battery management system controller based upon a comparison of the parameter measured by the measurement circuitry and the data received from the battery management system controller.

The invention discloses an automatic detection method for a rechargeable battery of a battery changing cabinet, and relates to the technical field of automatic detection of the rechargeable battery of the battery changing cabinet, and the method breaks through the limitation of traditional single parameter monitoring through a health degree prediction model driven by task features, and achieves the early warning of damage in a high-frequency cycle scene. The dynamic charging strategy is combined with impedance spectroscopy and vibration analysis to accurately identify the change of the microstructure in the battery; a thermal coupling compensation mechanism effectively eliminates monitoring distortion of multi-bin concurrent charging, and detection reliability under complex working conditions is improved. The system adopts a lightweight algorithm architecture, the detection dimension is expanded on the premise that hardware is not transformed, and the ability of prejudging sudden faults is remarkably enhanced.

A defect inspection method includes controlling, in response to an inspection instruction for a target material, a camera device to acquire a two-dimensional image and a three-dimensional image of the target material, and determining a defect inspection result of the target material based on the two-dimensional image and the three-dimensional image of the target material.

The invention relates to the technical field of lithium battery charging under photovoltaic panel power supply, in particular to a Buck-Boost lithium battery intelligent charging method based on ML-MPPT and dynamic reference voltage regulation, and the method comprises the steps: dividing a charging stage into a trickle charging stage, a constant-current charging stage and a constant-voltage charging stage according to the voltage of a lithium battery; the method comprises the following steps: acquiring environment temperature, solar illuminance and maximum power point voltage data in a constant current stage, preprocessing the data, training a maximum power point voltage prediction machine learning model, distilling the model to obtain a compression model, and deploying the compression model on a microcontroller; real-time environment data is input into the model to predict and obtain the maximum power point voltage as the reference voltage, the output voltage of the photovoltaic panel is sampled, and the input voltage of the Buck-Boost circuit is controlled by using the innovative PWM control system, that is, the duty ratio of PWM pulses is controlled through the microcontroller, the input impedance of the Buck-Boost circuit is adjusted, and the output voltage of the Buck-Boost circuit is adjusted. The output voltage of the photovoltaic panel, namely the input voltage of the switching power supply, is adjusted to the maximum power point voltage according to environmental conditions; and dynamically switching the lithium battery in different charging stages according to the battery state and the input power. The method integrates machine learning into prediction of the maximum power point voltage, and is suitable for an intelligent charging scene of the lithium battery under photovoltaic power supply.

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.

The invention particularly relates to a communication base station standby power supply management system, which comprises the following parts: a monitoring module; a switching module; a state evaluation module; the charging and discharging management module is used for optimizing charging and discharging actions of the battery packs according to the evaluation values of the battery packs; and a feedback module. According to the invention, through comprehensive and accurate monitoring and an intelligent switching mechanism, the stability and reliability of the system are greatly improved; the monitoring module monitors and analyzes multiple parameters of the main power supply in real time to generate an accurate trigger value, so that the problem of wrong switching or untimely switching caused by single parameter judgment of a traditional system is avoided; when a potential fault occurs in the main power supply, a response can be made in time, switching of the standby power supply is triggered, and uninterrupted power supply of the communication base station is ensured.

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.

The invention discloses multifunctional battery cell comprehensive detection and treatment integrated equipment, and aims to realize full-process automatic treatment of a battery cell from electrical performance test to internal defect detection. The equipment comprises a lead plate shell, a multifunctional battery cell detection module and a battery cell Xray detection and sorting module. The multifunctional module completes IV / OCV testing, tab shaping, cutting and size measurement in sequence through the first rotating disc mechanism, and it is ensured that the physical and electrical performance of a battery cell reaches the standard; and the Xray module carries the double Xray detection mechanisms through the second rotary table mechanism, penetrating imaging is conducted on key areas at the head and the tail of the battery cell, and high-speed sorting of good products and defective products is achieved in combination with the double-head rotary discharging mechanical arm and the NG sorting four-axis mechanical arm. The multi-shielding design of the lead plate shell and the NG sorting frame ensures radiation safety, and the modular structure supports compatibility of multi-specification battery cells. Through cooperative control of the turntable, the robot and the sensor, the detection efficiency and precision are remarkably improved.

The invention relates to an intelligent diagnosis system for echelon utilization and recovery of a power battery, and aims to evaluate the health state of the battery with high precision and assist the echelon utilization and recovery of the battery. The system integrates five modules: the battery health state monitoring module acquires parameters such as voltage, current and temperature in real time through a high-precision sensor; the battery health state evaluation module accurately evaluates the battery state by applying algorithms such as ICA, DVA and LSTM neural networks in combination with a transfer learning technology; the echelon utilization recovery decision-making module outputs an optimal recovery scheme based on a three-level decision-making model; the intelligent disassembling and safety protection module adopts an advanced process and a monitoring system to guarantee safe and efficient disassembling; and the data storage and cloud prediction feedback module realizes system dynamic optimization by using big data analysis and machine learning. The evaluation accuracy of the system exceeds 95%, the single diagnosis time is shorter than 5 minutes, multiple types of batteries are supported, the self-learning iteration period is shorter than 24 hours, the detection cost can be remarkably reduced, the resource utilization rate is increased, carbon emission is reduced, and key technical support is provided for sustainable development of the new energy automobile industry.

A battery diagnosis apparatus includes a measuring unit to measure a battery voltage, which is a voltage between both terminals of a battery and a battery current which is a current flowing through the battery, a storage unit to store a plurality of actual-measurement internal resistance values, each of which has been calculated based on at least one of the battery voltage and the battery current at each diagnosis time when diagnosing defects of the battery, and at least one test internal resistance value determined according to a predetermined criterion, and a control unit to extract a plurality of previous diagnosis times, corresponding to a predetermined number of samples, from each diagnosis time, at the diagnosis time, calculate a moving average, and diagnose defects of the battery by comparing an actual-measurement internal resistance value calculated at each diagnosis time with reference values calculated based on the moving average.

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.