199768 results about "Battery cell" patented technology

The invention discloses an electric bicycle lithium battery data real-time monitoring and early warning system and method, and relates to the technical field of battery management, and the system comprises a multi-dimensional data collection module which is used for obtaining a multi-source heterogeneous data set of a lithium battery system; the collaborative feature extraction module is used for generating a comprehensive evaluation parameter set; the dynamic threshold generation module is used for constructing a self-adaptive early warning boundary model according to the comprehensive evaluation parameter set; the intelligent decision module is used for generating a hierarchical control instruction set based on a multi-objective optimization algorithm; and the cloud collaboration module is used for synchronizing the hierarchical control instruction set to the edge computing node and the cloud management platform, and triggering a multi-level linkage protection mechanism based on the game theory when the thermal runaway risk index is detected to exceed a first dynamic threshold value. According to the electric bicycle lithium battery data real-time monitoring and early warning system and method provided by the invention, the safety and reliability of a battery system are improved.

The invention relates to the field of battery fault prediction, in particular to a lithium ion battery fault prediction method and system based on a BMS. The method comprises the following steps: extracting multi-dimensional operation monitoring parameters of a battery through a BMS (Battery Management System), carrying out multi-state evolution perception and label mapping processing, and constructing a global multi-state perception map of the battery; short-term abnormal sudden change detection is carried out according to the multi-dimensional operation monitoring parameters of the battery, and normal characteristic deviation trend analysis is carried out, so that an abnormal fluctuation deviation evolution trajectory is constructed; and performing deep topological correlation learning on the global multi-state sensing map of the battery based on the abnormal fluctuation deviation evolution trajectory, performing heterogeneous node global sensing, performing abnormal behavior causal relationship mining on heterogeneous deviation nodes in the battery, and performing multi-causal fission simulation to generate a battery behavior deterioration chain under an abnormal trend. According to the method, accurate and efficient fault prediction is realized, transfer learning is carried out, and the perspectiveness of subsequent BMS fault prediction is improved.

An apparatus is dedicated to controlling the voltage of a battery comprising at least two modules connected in series and each comprising at least one electrochemical cell, and each coupled to voltage balancing means. The apparatus comprises measurement means for determining first voltages across the terminals of each of the modules, calculation means for measuring a second voltage across the terminals of the battery and for determining a mean voltage per module representative of the second voltage divided by the number of modules, and processor means for comparing each measured first voltage with the mean voltage per module and for delivering to the balancing means signals representative of the result of the comparison whenever the first voltage of a module is greater than the mean voltage, such that the balancing means reduce the voltage across the terminals of the module.

A pulse oximetry system includes a wrist portion configured for placement on a wrist of a subject, the wrist portion having a first component and a second component configured to removably secure to one another. The wrist portion can include emitter(s) and detector(s) operably positioned by the wrist portion. In some implementations, the pulse oximetry system further includes a ring member configured to secure around the subject's finger and operably position emitter(s) and detector(s) and a cable connected to the wrist portion in electrical communication with the emitter(s) and the detector(s) of the ring member and configured to transmit the signal(s) from the detector(s) to the wrist portion. The system includes a battery and hardware processor(s) configured to receive and process signal(s) outputted by the detector(s) to determine physiological parameter(s) of the subject.

The invention relates to the technical field of intelligent power grids, in particular to a power distribution network battery digital dynamic management system based on digital twinning. Comprising a data acquisition unit; the digital twinborn modeling unit is used for constructing a battery-power grid-environment multi-dimensional dynamic twinborn body and realizing virtual-real bidirectional mapping and adaptive updating by combining a multi-physics field coupling model and a long-short-term memory network time sequence prediction algorithm; a dynamic optimization unit; and executing the feedback unit. Through a distributed heterogeneous sensing network of a data acquisition unit, multi-dimensional operation data of a battery pack and a key node of a power distribution network are acquired, and a high-fidelity data set containing four-dimensional labels of a battery state, a power grid parameter, time and a position is generated in combination with a spatial-temporal feature extraction technology; the deep fusion of the full life cycle state of the battery and the global operation data of the power distribution network is realized, and the comprehensive data support covering the global is provided for the optimization decision.

The invention belongs to the technical field of batteries, and discloses a battery cover plate and a single battery, the battery cover plate comprises a plate body assembly and a pole assembly, the plate body assembly is used for covering an opening of a battery shell, the plate body assembly comprises a first plate body, a second plate body and a connecting part, the first plate body comprises a first surface and a second surface which are oppositely arranged, the first surface is configured to be arranged towards the battery shell, the direction from the first surface to the second surface is a first direction, the second plate body comprises a first side and a second side which are oppositely arranged, the second plate body inclines in the direction deviating from the first direction from the direction from the first side to the second side, and the first side is connected with the first plate body through a connecting part; the battery cover plate comprises a first plate body and a second plate body, a connecting part is arranged between the first plate body and the second plate body, the connecting part protrudes towards the first direction, a pole assembly penetrates through at least one of the first plate body and the second plate body, the connecting part protrudes out of the pole assembly along the first direction, the structural strength of the plate body assembly can be improved, and the probability that the pole assembly is bumped in the manufacturing process can be reduced.

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 relates to the technical field of battery detection, and particularly provides a battery fault early warning and diagnosis method, which comprises the following steps: acquiring operation parameters of a battery pack in real time, processing the operation parameters based on a preset judgment condition, generating a corresponding preprocessing signal according to a noise environment state, and extracting spatial-temporal characteristics to perform weak signal enhancement processing, so as to obtain a battery fault early warning and diagnosis result. Generating an enhanced feature set; based on the enhanced feature set, performing space-time fusion calculation according to a preset weight relation to obtain a fault energy accumulation value; dynamically correcting a fault judgment threshold according to the health state of the battery and the real-time environment parameters; and when the fault energy accumulation value exceeds the fault judgment threshold after dynamic correction, outputting a graded early warning signal. By capturing weak fault features of the battery pack and combining spatial domain feature extraction of temperature difference and voltage distribution and a signal enhancement technology, the detection sensitivity of early hidden faults is improved, and in a lithium battery safety early warning scene, the fault detection time is shortened, the false alarm rate is reduced and the like.

The invention belongs to the technical field of batteries, and discloses a battery top cover which comprises a plate body assembly and a pole assembly, the plate body assembly is used for covering an opening of a battery cell shell, the direction from the battery cell shell to the plate body assembly is configured to be a first direction, and the plate body assembly comprises a first plate body, a second plate body and a first reinforcing part; the first plate body is perpendicular to the first direction, a reinforcing part is arranged on the edge of the side, facing the first direction, of the first plate body, one side of the second plate body is connected with the reinforcing part, the second plate body inclines in the direction away from the first direction from the side facing the reinforcing part to the side away from the reinforcing part, and the first reinforcing part is connected with the reinforcing part. According to the battery top cover, at least one of the first reinforcing piece and the reinforcing part protrudes in the first direction, a pole assembly penetrates through at least one of the first plate body and the second plate body, the first reinforcing piece protrudes out of the pole assembly in the first direction, and the plate body assembly of the battery top cover is high in structural strength and can also play a role in protecting the pole assembly.

A portable fan with predictable battery life and a method for predicting battery life thereof. The portable fan includes a fan body, where a circuit board assembly and a storage battery are installed in a shell of the fan body, a microprocessor and a current collector are encapsulated in the circuit board assembly, a load and a display device are provided on the fan body, and the display device includes a function lighting display area and a battery life display area. According to the portable fan, the display device can be used to indicate a working mode that the fan body is currently turned on and the working battery life that the storage battery can provide under the corresponding working mode. An intuitive and standard time unit is used for indication, so that a clear visual perception effect is achieved.

A battery pack according to one or more embodiments of the present disclosure includes a plurality of battery cells; and a housing including a bottom member, a plurality of side walls, and a top member, the bottom member, the plurality of side walls, and the top member form an interior accommodation space configured to accommodate the plurality of battery cells, wherein at least one among the plurality side walls, the top member, and the bottom member are formed by a plurality of metal sheets that are roll bonded to each other, wherein at least one cooling channel is formed between bonding areas of the plurality of metal sheets, and wherein at least one crash channel is formed between bonding areas of the plurality of the metal sheets.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention discloses a V2G-based dynamic discharge power optimization control method, and belongs to the field of smart power grid and electric vehicle cooperative control, and the method comprises the following steps: collecting the battery state information of an electric vehicle and the load demand data of a power grid in real time; determining a demand response priority through a multi-objective optimization algorithm based on the battery state information of the electric vehicle and the load demand data of the power grid; calculating a power distribution strategy scheme based on the response priority; when the power distribution strategy scheme does not meet the battery health constraint, a safe discharge power range is adjusted based on a battery state evaluation result; generating a correction parameter based on the adjusted discharge power range and the environment variable; and monitoring the operation state of bidirectional energy flow in real time, and when the operation state of bidirectional energy flow does not achieve the response effect, adaptively adjusting the power distribution strategy based on the correction parameter and updating the execution parameter.

The invention relates to the technical field of lithium battery management, in particular to a lithium battery pack dynamic balancing method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: predicting battery cell state parameters of each battery cell based on a preset long-short-term memory neural network model to obtain a capacity attenuation trend of each battery cell; building a health state evaluation index based on the capacity attenuation trend; based on the health state evaluation indexes, performing health grade classification on each battery cell by adopting a preset clustering algorithm; obtaining the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell, and determining the balance priority of each battery cell based on the health grade category division result, the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell; and on the basis of the equalization priority, a target dynamic equalization adjustment strategy is generated, and each battery cell is adjusted according to the target dynamic equalization adjustment strategy, so that the energy scheduling accuracy of the lithium battery pack is improved.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

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

The present invention relates to an intelligent, context-aware battery management system embedded within a mobile device that dynamically allocates power resources based on real-time user behavior, system state, and environmental context. It incorporates a smart recognition engine that analyzes sensor-derived telemetry data to compute behavioral deviation scores, enabling the system to anticipate abnormal or emergency-prone conditions. A continuous activity classification module contextualizes user motion and geolocation to inform power policy decisions. Upon detecting significant behavioral anomalies or critically low battery conditions, an emergency mode subsystem is triggered, restricting device operations to essential functionalities while preserving energy for critical communication and navigation tasks. The system also establishes a secure, lightweight emergency communication tunnel for relaying essential metadata, including GPS and behavioral indicators, to predefined response servers.

The invention discloses an optical storage and charging integrated micro-grid energy management system, relates to the technical field of micro-grid energy management, and is used for solving the problems of low energy scheduling efficiency and insufficient stability in an optical storage and charging system. The system comprises a power generation monitoring module, an energy storage management module, a charging load control module and an energy coordination module. The photovoltaic power generation state and environmental parameters are monitored in real time through a multi-type sensor network, and the running state of the photovoltaic module is judged; based on battery monitoring data and photovoltaic output characteristics, a differential energy storage management strategy is formulated; the charging power is dynamically distributed in combination with the energy state to realize charging and discharging intelligent regulation and control; by converging multi-source information, source storage and load interaction is coordinated to cope with different power states; accurate monitoring, intelligent scheduling and efficient cooperation of the photovoltaic, storage and charging integrated micro-grid are realized, and the energy utilization efficiency and the operation stability are remarkably improved.

An ID token wirelessly communicates a battery voltage to a vehicle gateway via short-range communication. A battery health may be determined based on the current battery voltage and one or more previous battery voltages associated with ID token. Replacement of the ID token may be initiated based on the battery health to avoid communication failures by the ID token due to insufficient battery charge.

The invention relates to the technical field of energy storage battery diagnosis, and discloses an energy storage battery fault diagnosis method and system based on a data fusion algorithm. The method comprises the following steps: acquiring historical operation data and real-time operation data of an energy storage battery in a preset operation period, and generating a historical fault data set according to the historical operation data; performing multi-source feature fusion processing on the historical fault data set to generate a fusion feature parameter set integrating voltage, current, temperature and impedance parameter joint change features; a multi-dimensional fault space is constructed based on the parameter set, coordinate axes of the multi-dimensional fault space correspond to different parameter dimensions, and spatial position coordinates represent parameter change characteristic values; calculating the fault correlation degree of the historical fault event in the multi-dimensional fault space, and determining a fault early warning index set; and extracting real-time characteristic parameters based on the real-time operation data to form a state vector, carrying out space mapping correlation calculation on the state vector and the fault early warning index set in a multi-dimensional fault space, and outputting a real-time fault correlation factor.

The invention relates to the technical field of fire early warning, and discloses a tunnel fire early warning method and system based on deep learning, and the method comprises the steps: deploying a sensor, constructing a wireless transmission network, and obtaining multi-modal data; carrying out preprocessing, feature extraction and feature fusion on the multi-modal data; constructing a tunnel fire early warning model adapted to the construction environment and designing a dynamic threshold mechanism; and designing a multi-level early warning response mechanism and carrying out early warning. According to the invention, the dust-proof smoke sensor is arranged to overcome the problem of susceptibility to dust interference; sampling frequency and communication frequency are automatically adjusted according to monitoring requirements during data transmission, so that the monitoring effect is ensured and the service life of a battery is prolonged; a tunnel fire early warning model based on TCNN is constructed, and a large-scale fire data set is constructed through FDS digital simulation to train the model; a dynamic threshold value capable of being automatically adjusted according to the environment is designed, and the problem that a fixed threshold value system does not adapt to the changing environment is solved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent flight path planning and energy management system and method for a long-endurance fixed-wing unmanned aerial vehicle. Comprising an environment sensing unit; the flight path planning unit is used for planning a flight path meeting task requirements, safety requirements and energy constraints based on the flight environment information of the unmanned aerial vehicle acquired by the environment sensing unit and pre-stored performance parameters of the unmanned aerial vehicle; and the energy management unit realizes energy dynamic management and optimization according to the real-time energy state of the unmanned aerial vehicle, the flight task and the planning result of the flight path planning unit. The flight path planning unit can call working condition energy consumption data such as navigational speed and height output by the energy management unit in real time, and dynamically adjust the weight of the path to avoid high-energy-consumption flight segments; the energy management unit synchronously and intelligently adjusts a main / standby battery charging and discharging strategy to optimize energy distribution according to a task time sequence (such as waypoint priority and track curvature) of a track.

The invention relates to the technical field of battery intelligent monitoring, and discloses an intelligent monitoring system and method for a new energy automobile battery. The method comprises the following steps: acquiring a target battery operation data set of a new energy automobile battery, and extracting time domain differential characteristics and frequency domain energy loss characteristics in a battery charging and discharging process to obtain a multi-dimensional state characteristic set; performing electrochemical characteristic analysis on the new energy automobile battery based on the multi-dimensional state feature set to obtain a micro degradation state judgment result; and dynamically adjusting the parameter configuration of a hybrid Kalman filter according to the micro degradation state judgment result, and generating a battery state-of-charge estimation value. According to the method, the problems of insufficient estimation precision and accumulative errors in a traditional method are solved, and the use safety and reliability of the battery are improved.

The invention discloses a lithium ion battery health state evaluation method and device based on deconstruction physical information, a medium and a product, and relates to the technical field of lithium ion battery health state evaluation, and the method comprises the steps: extracting a multi-dimensional health factor, inputting the multi-dimensional health factor into a deep physical information neural network fusing a self-attention mechanism module and a Koopman neural operator module, and obtaining a deep physical information neural network; and outputting the SOH estimation value. By extracting the universal health factors suitable for multiple working conditions, the problem that the universality of the health factors is insufficient is solved; a self-attention mechanism is utilized to enhance features and reduce redundancy, and the perception ability of the model to key information is enhanced; physical information is deconstructed by means of a Koopman neural operator, a physical mechanism of battery degradation is fused into the model, and the physical interpretability of the model is enhanced; the deep physical information neural network is fused with multi-dimensional information for estimation, individual differences and complex working conditions of different batteries are effectively dealt with, and therefore high-precision and high-robustness lithium ion battery SOH estimation is achieved.

This invention discloses a fluorene-based organic small molecule hole transport material and its applications. The general structural formula of the fluorene-based organic small molecule hole transport material is as follows: where n = 1-4, and R represents the hole transport capacity. This fluorene-based organic small molecule hole transport material is used as an undoped hole transport material in perovskite solar cell devices. By introducing dicarboxylic acid groups onto the fluorene-based organic small molecule hole transport material, when used in an inverted perovskite solar cell device, it can form chemical bonds with the hydroxyl groups of the glass conductive substrate, forming a directionally aligned self-assembled layer on the surface of the glass conductive substrate. This effectively improves the charge transport capability at the interface and increases the photoelectric conversion efficiency of the perovskite solar cell device. Furthermore, since this perovskite solar cell device does not contain lithium salts, it exhibits high water and oxygen stability. In addition, the synthesis process of this fluorene-based organic small molecule hole transport material is simple, and the raw materials are inexpensive and readily available, making it highly valuable for practical applications.

The invention discloses a battery abnormity identification monitoring method for battery operation and maintenance, and particularly relates to the technical field of battery detection. After a battery enters a station, a temperature difference array, shell micro-deformation, cavity gas indication and end plate near-end temperature are synchronously acquired to form a skin observation vector; applying slight current disturbance and recording voltage response and temperature rise differential to generate a disturbance differential vector; performing event alignment, same-window shadow trajectory depolarization and quantile projection normalization under a unified time reference to obtain a stable and reliable equivalent short window representation vector; performing weighted matching on the vector and historical reference fingerprint quality to obtain a consistency score, calculating a multi-physical disturbance coupling index and a historical trajectory deviation index, outputting a short window credibility coefficient through a discrimination model, generating a risk tag by combining a consistency threshold coefficient after zooming the consistency score, and performing risk identification on the risk tag. And linkage isolation recheck, power limitation and temperature rise limitation or release and archiving are carried out, so that the problem that abnormity in a short time window is easily covered by an environment common mode and artifacts is solved.

A battery pack including a housing and a cell frame supported within the housing. The cell frame includes a plurality of openings to secure a plurality of battery cells, and a weld strap disposed within the cell frame configured to interconnect the plurality of battery cells. The battery pack also includes a cell header cover disposed adjacent to the battery cells and covering the weld strap. The cell header cover is configured to prevent battery cell particulate from circulating within the outer housing. The cell header cover and the cell frame cooperate to surround the battery cells.

The invention discloses an integrated scheduling system for realizing a PCS, an EMS and a BMS, and relates to the technical field of power control, and the system comprises a multi-dimensional performance evaluation module which constructs a battery aging dynamic model, carries out the training, carries out the health state pre-judgment through the battery aging dynamic model based on a standardized state vector, and generates a multi-dimensional performance evaluation index; the multi-objective optimization module is used for generating a collaborative scheduling strategy set by combining a fuzzy analytic hierarchy process with a multi-objective optimization solver of an improved genetic algorithm based on the multi-dimensional performance evaluation indexes; the dynamic derating module is used for generating an executable instruction queue with security constraints by combining an industrial internet of things protocol stack with a dynamic derating coefficient algorithm based on the collaborative scheduling strategy set; according to the invention, through the physical driving characteristic layer and the dynamic parameter calibration layer, the nonlinear coupling modeling of the cyclic attenuation and calendar aging mechanism in the battery aging dynamic model is realized.