339279 results about "Battery cell" patented technology

The invention discloses a battery pack thermal runaway risk identification and early warning system and method, and relates to the related field of battery risk early warning, and the method comprises the steps: collecting an operation parameter data set of a battery pack in real time through a multi-mode sensor network, carrying out the hybrid anomaly detection of the battery pack, obtaining a real-time anomaly detection result, and carrying out the thermal state prediction of the battery pack, and constructing a thermal state evolution trend chart to perform thermal runaway risk assessment, determining a thermal runaway risk level, triggering a multi-level early warning signal according to the thermal runaway risk level, and generating a thermal runaway suppression strategy. The technical problems that in existing battery thermal runaway early warning, due to monitoring limitation of a single sensor, the false alarm rate is high, response lags behind, and battery aging cannot be dynamically adapted are solved, and the technical effects that the thermal runaway early symptom detection rate is increased through multi-mode sensing, and the early warning time is shortened are achieved.

The invention relates to the technical field of battery health state analysis, and discloses a battery health state comprehensive evaluation method, system and device and a storage medium, and the method comprises the steps: carrying out the preprocessing of data collected by a battery management system, and obtaining a standardized data sequence; calculating a first battery characteristic parameter according to the standardized data sequence; performing covariance matrix adaptive Kalman filtering processing on the first battery characteristic parameter to obtain a second battery characteristic parameter; constructing a capacity-internal resistance change rate index and a temperature-efficiency correlation index based on the second battery characteristic parameters to obtain a dual-threshold evaluation matrix; inputting the dual-threshold evaluation matrix into a deep reinforcement learning model, and performing forward propagation calculation and iterative training through an Actor network and a Critic network to obtain a feature fusion weight coefficient; and carrying out weighted calculation on the dual-threshold evaluation matrix to generate a battery health state value and a residual life prediction value, and realizing multi-dimensional evaluation of the battery health state.

A wearable device including at least one sensor configured to sense a physiological parameter of a user. The wearable device including a base housing and a removable housing attachable to the base housing. The base housing and the removable housing portions each including a battery and an electronic subsystem in communication with each other. The battery of the removable housing portion charges the battery of the base housing portion when the removable housing portion is attached to the base housing portion. A second embodiment includes two or more fitness trackers each having an enclosure. The enclosures of the two or more fitness trackers having complimentary shapes that form a unified enclosure when the enclosures are placed adjacently. In a third embodiment, a wearable device includes a first screen display and a second screen display. The second screen display is transparent in at least one operational mode of the wearable device.

The invention provides a battery assembly process defect real-time detection and classification method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: carrying out the standardization preprocessing of collected battery surface images and three-dimensional point cloud data; a distributed edge computing architecture is adopted for feature extraction, the distributed edge computing architecture comprises a defect propagation prediction module based on a graph neural network, inter-process defect features are constructed into a dynamic graph structure, a defect evolution rule is learned through the graph network, and feature extraction parameters are dynamically adjusted; inputting the extracted features into a deep learning detection model, carrying out feature adaptive mapping and alignment through a multi-modal spatial-temporal feature coding layer, constructing a process association graph through a dynamic reasoning layer based on causal reasoning, and calculating a propagation influence degree; carrying out hierarchical precision detection on different defect evolution probability regions by a prediction perception detection layer with a self-adaptive threshold value, and outputting a defect detection result; and classifying defect detection results based on defect classification standards.

The invention relates to the technical field of battery management, and discloses a battery energy balanced distribution and optimization method, device and equipment and a storage medium. The method comprises the following steps: performing multi-source parameter acquisition and feature decoupling on each single battery in a target battery pack to obtain a single battery state data set, and performing multi-band excitation scanning and impedance spectrum fitting to obtain internal features of the battery and decompose an attenuation trend to generate equivalent circuit model parameters; performing state mapping and capacity calculation based on the model parameters to obtain a performance evaluation result of the battery pack, and constructing an energy transfer network and identifying unbalanced propagation characteristics through performance difference analysis and consistency calculation; and temperature coupling calculation and compensation matrix reconstruction are carried out based on propagation characteristics, multi-path parallel balance control is realized, and an optimization result of the battery pack is obtained through voltage deviation calculation and polarization compensation. According to the method, the multi-dimensional characteristic parameters and the dynamic change characteristics are combined, and accurate control over battery pack equalization is achieved.

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

The invention relates to the technical field of battery thermal management, in particular to a battery thermal management control method and system, and the method comprises the steps: obtaining the basic operation parameter data of an automobile battery in real time, and carrying out the multi-dimensional data fusion, and obtaining a multi-dimensional data set; based on the thermodynamic model and the multi-dimensional data set, the temperature distribution condition in the battery is calculated in real time, and real-time temperature distribution information is obtained; based on the historical data and the operation state, the future temperature change trend of the battery is predicted through a recurrent neural network model; dynamically adjusting a control strategy of the battery thermal management system according to the real-time temperature distribution information and the prediction result; and analyzing the temperature difference condition of the battery monomers, detecting abnormal temperature rise, and performing temperature difference balance adjustment and triggering a multi-stage protection mechanism when the abnormal temperature rise exceeds a threshold value. According to the invention, the problems of inaccurate temperature prediction and response lag in the prior art are effectively solved, the control strategy is dynamically adjusted through real-time data fusion and recurrent neural network prediction, temperature difference balance and abnormal temperature rise detection are realized, the safety of the battery is improved, and the service life of the battery is prolonged.

The invention discloses an energy self-control base station for battery replacement based on solar power supply with independent UAV take-off and landing, which comprises a UAV take-off and landing device, an automatic battery replacement device and an energy supply device. The foldable solar panel structure is driven by a motor. The invention has simple structure and is convenient for automatic battery replacement. The UAV damage caused by UAV position deviation is effectively avoided when the platform descends. The invention adopts an electric mechanical claw to cooperate with a liftable battery compartment, so as to automatically replace UAV batteries. After the electric mechanical claw is aligned with the predetermined position, the batteries can be grasped and placed by lifting the battery compartment or the annular lifting platform, which simplifies the mechanical structure of the automatic battery replacement device.

The invention relates to a battery life prediction and health assessment system based on BMS expansion parameter analysis, and the system fuses deformation quantity and electrochemical parameters, and remarkably improves the modeling precision and life prediction capability of a battery health state. The acquisition module obtains battery expansion characteristics and operation parameters through a sensor or a modeling mode, and constructs a multi-dimensional time sequence data set; the processing module performs normalization and feature reconstruction on the data to form a health track sample for learning; the prediction module outputs residual life and degradation trend scores based on a deep regression model, and introduces an expansion feature sensitivity adjustment mechanism; the evaluation module evaluates the health level of the battery according to the prediction result and a threshold rule and generates a report; the regulation and control module dynamically adjusts BMS control parameters according to an evaluation result, and optimizes energy efficiency and a safety strategy; the method breaks through the dependence of a traditional BMS system on electrochemical characteristics, realizes more comprehensive and accurate intelligent sensing and life management of the battery health state, and is suitable for various BMS architecture scenes.

The invention relates to the technical field of battery temperature control early warning, in particular to an energy storage battery temperature control early warning method and system. The method comprises the following steps: acquiring an internal structure diagram of the energy storage battery; performing internal space layout analysis and inter-unit physical connection analysis on the internal structure diagram of the energy storage battery to construct a three-dimensional battery topological structure model; acquiring real-time temperature monitoring parameters of the energy storage battery; dynamic temperature distribution visual rendering is carried out on the three-dimensional battery topological structure model according to real-time temperature monitoring parameters of the energy storage battery, so that thermal state portraits of a plurality of unit areas are constructed; performing multi-period battery operation simulation on the thermal state portraits of the plurality of unit areas, and performing sliding time window temperature change situation prediction to obtain a temperature change prediction situation of each area; and carrying out continuous abnormal temperature rise trend detection on the temperature change prediction situation of each area so as to generate an abnormal temperature rise trend characteristic. According to the invention, safe and efficient pool temperature control early warning is realized.

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.

A method for conditioning and maintaining power with predictive load compensation using an uninterruptible power supply system is disclosed. The method includes measuring an electrical input from a primary power supply using analog sensors and transmitting the input through an impedance to introduce a controlled delay. During the delay, input parameters and downstream system effects are determined based on telemetry sensor data. Digital identities are generated for the electrical input and the downstream load and compared to a stored digital identity representing historical conditions. Using the comparison, a system simulation is performed with analog twinning to predict downstream system responses. The predicted response is used to adjust a power converter parameter, modifying the electrical input to an intermediate output that compensates for voltage fluctuations and harmonic distortions. A secondary power supply with a bidirectional high-discharge battery supplements or stores energy as needed, ensuring stable power delivery to the downstream load.

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.

Systems, methods, and articles for a portable power case are disclosed. The portable power case is comprised of at least one battery and at least one PCB. The portable power case has at least two access ports and at least one USB port. The portable power case is operable to supply power to an amplifier, a radio, a wearable battery, a mobile phone, and a tablet. The portable power case is operable to be charged using solar panels, vehicle batteries, AC adapters, non-rechargeable batteries, and generators. The portable power case provides for modularity that allows the user to disassemble and selectively remove the batteries installed within the portable power case housing.

The invention discloses a data driving-based power battery safety risk assessment method and system, particularly relates to the technical field of battery safety monitoring, and is used for solving the problems of false alarm and missing detection caused by confusion of electrochemical relaxation effect and real fault signal characteristics in the existing standing stage fault detection method. Through multi-source data acquisition based on voltage, temperature and state of charge in a standing stage, a current slope extreme point is identified through charge and discharge mode weighted correction, a time-delay mapping relation with a voltage curve geometrical morphology abrupt change point is established, abnormal inflection points are screened, and fractal dimension attenuation characteristics are quantified; according to the method, abnormal distribution density and material damage indexes are fused to generate a dynamic threshold value, composite criterion analysis is carried out by combining frequency domain energy offset characteristics extracted by time-frequency transformation and heat conduction time-lag phase difference, relaxation interference signals are effectively stripped, hidden danger characteristics are locked, full-dimension safety state evaluation from microdefect evolution to macroscopic parameter abnormity is realized, and the safety state evaluation accuracy is improved. And the reliability of power battery fault detection under complex working conditions is improved.

An autonomous robotic vehicle with dynamic path planning capabilities, including: a dynamic path planning module configured to generate and adapt navigation paths in real time based on environmental changes and mission objectives; a sensor fusion module that integrates data from multiple sensors, including LiDAR, radar, cameras, ultrasonic sensors and GPS, to create a comprehensive environmental model; an obstacle avoidance module capable of detecting and predicting both stationary and moving obstacles and modifying the navigation path to avoid collisions; an artificial intelligence-based decision-making module for analyzing environmental data and learning from past navigation scenarios for improved performance; a decentralized control module that enables the independent operation of the vehicle in areas with interrupted or no connection to a central server; an energy management module configured to optimize battery usage by dynamically adjusting operating parameters based on real-time data and mission requirements; a cooperation module that enables communication and coordination between multiple autonomous robot vehicles for fleet management or the synchronized execution of tasks; a fail-safe module to ensure uninterrupted operation and data security in the event of communication failures or hardware malfunctions.

The invention relates to the technical field of battery management, and discloses a multi-dimensional parameter evaluation system and method based on cell thermal runaway risk detection, a thermal-electric coupling data set is constructed by collecting multi-dimensional parameter data of a cell in different charging and discharging states, and electrochemical impedance spectroscopy data in different aging states are obtained; constructing a refined multi-dimensional electrochemical characteristic space; training a cell health degree evaluation model based on the feature space, extracting a cell health degree index, and establishing a mapping relationship between the cell health degree index and a thermal runaway risk level; in combination with the real-time working condition parameters of the battery cell, a working condition self-adaptive battery cell health degree index is obtained by dynamically adjusting the weight coefficient of each feature in the health degree evaluation model, the thermal runaway risk level is predicted according to the working condition self-adaptive battery cell health degree index, and an early warning mechanism is triggered; according to the invention, accurate, real-time and dynamic evaluation of the thermal runaway risk of the battery cell can be realized, the accuracy and reliability of early warning of the thermal runaway of the battery cell are remarkably improved, and powerful technical support is provided for safety management of the battery.

The invention relates to the field of battery state evaluation, in particular to a new energy automobile battery health state evaluation method and system. The method comprises the following steps: acquiring a vehicle battery multi-mode state monitoring parameter and a historical vehicle endurance mileage log; performing battery state dynamic evolution on the vehicle battery multi-mode state monitoring parameters to obtain battery multi-mode state evolution characteristics; carrying out multi-time-point maximum endurance calculation on the historical vehicle endurance mileage log, carrying out time sequence trend evolution, and constructing a vehicle endurance evolution trend chart; performing nonlinear attenuation trend analysis on the vehicle endurance evolution trend graph, and performing battery aging mechanism evolution analysis so as to construct a battery state aging evolution knowledge graph; according to the battery state aging evolution knowledge graph, environment change influence analysis is carried out on the vehicle endurance evolution trend graph, dynamic vehicle state modeling is carried out, and a dynamic vehicle twinborn model is constructed. According to the invention, efficient and accurate battery health state evaluation is realized.

The invention relates to a battery monomer, a battery device and a power utilization device, the battery monomer comprises an electrode assembly and an electrolyte, and the electrode assembly comprises a positive pole piece and a negative pole piece; the positive pole piece comprises a positive pole current collecting part and a positive pole film layer; the positive pole film layer comprises lithium-containing phosphate with an olivine structure; the negative pole piece comprises a negative pole current collecting part and a negative pole film layer, and the negative pole film layer comprises a carbon-based material; the electrolyte comprises a carboxylic ester solvent, a first additive and fluorine-containing lithium sulfimide, and the mass content of the carboxylic ester solvent is 8%-60%; the total mass content of the first additive is 2.5%-10%, the first additive comprises 1, 3-propane sultone with the mass content larger than or equal to 0, an ethylene carbonate derivative with the mass content larger than or equal to 0 and vinylene carbonate with the mass content larger than 0, and the ethylene carbonate derivative comprises a compound shown in the formula A; the mass content of the fluorine-containing lithium sulfimide is 4%-8%. According to the invention, the rapid charging performance and the cycle performance of the battery monomer can be improved. # imgabs0 # formula A

The invention discloses a hybrid energy storage system optimization scheduling method based on AI intelligent regulation and control, and relates to the technical field of hybrid energy storage, and the method comprises the following steps: collecting real-time data, and carrying out the cross verification of data consistency through a multi-source data fusion technology; and identifying data abnormity caused by sensor faults, communication delay and environmental interference by combining adaptive threshold detection with a statistical analysis method, and eliminating abnormal data. According to the method, energy storage scheduling is optimized through data cleaning, time sequence prediction and reinforcement learning, and the intelligent management and dynamic adaptive capacity is improved. Multi-source data fusion and anomaly detection are adopted to ensure data accuracy, energy consumption is predicted by means of LSTM and Transform, and an energy storage strategy is optimized in advance. By combining reinforcement learning and system dynamic adjustment charging and discharging, the photovoltaic consumption rate is improved, the electricity purchasing cost is reduced, the SOC is intelligently controlled to be 40%-80%, and the service life of the battery is prolonged. Meanwhile, the system stability is improved through anomaly detection and correction, and the maintenance cost is reduced.

The invention discloses a day-ahead and intra-day economic dispatching method for a wind and light storage system, which relates to the technical field of wind and light storage and comprises the following steps of: establishing a dynamic aging model based on historical operation data of a battery, fitting actual attenuation characteristics of the battery through machine learning, acquiring the health state of the battery in different operation states and the change rule of the battery along with time, and calculating the economic dispatching result. Therefore, the basis of the dynamic adjustment strategy is formed. Energy storage charging and discharging are optimized through health factors, the system monitors parameters such as SOH and SOC in real time, tasks are dynamically adjusted, battery aging is reduced, stability is improved, the SOC strategy is optimized in combination with deep reinforcement learning, the service life is prolonged, and the maintenance cost is reduced. Rolling optimization scheduling and experience playback are adopted, a system can dynamically adjust a strategy, the wind and light absorption rate is improved, power grid balance is optimized, thermal power dependence is reduced, scheduling cost is reduced, extreme weather adaptability is enhanced, and green and low-carbon development of electric power is promoted.

An adapter plug for a power bank includes a housing, electrical connectors, prongs, and an electrical circuit. The electrical connector associated with the housing and are configured to contact an electrical connector of a power bank. The prongs are configured to engage an electrical wall outlet. The electrical circuit is disposed within the housing and electrically connects the prongs to the electrical connectors. The prongs, electrical circuit, and the electrical connector of the housing is configured to deliver electrical power from the electrical wall outlet to the power bank to recharge a battery of the power bank when the electrical connector of the housing contacts the electrical connector of the power bank and when the prongs engage the electrical wall outlet.

The invention discloses a battery charging monitoring and adjusting system based on multiple areas, and relates to the technical field of battery management. According to the system, electrochemical impedance spectroscopy, infrared thermal imaging and electric heating parameters are synchronously collected, a physical connection and heat conduction topological relation of a battery pack is constructed in combination with a graph neural network, and monomer aging distribution and a cross-regional heat diffusion path are accurately recognized. Based on multi-region data aggregation, battery health protection, temperature equalization and a power grid demand target are fused, and charging current constraint and equalization priority parameters are dynamically generated. And through a fuzzy control algorithm, an active energy transfer mode and a passive risk isolation mode are intelligently switched, the energy distribution efficiency is optimized, fault spreading is blocked, the risk level of the system is evaluated in real time, a hierarchical regulation and control instruction is triggered, and the weight is dynamically adjusted and optimized through a feedback mechanism. The problems of extensive monitoring, strategy stiffness and safety response lag of a multi-area battery system are solved, the charging safety and economy in a complex scene are remarkably improved, and the service life of the system is remarkably prolonged.

A direct current (DC) fast charger for charging a battery of an electric vehicle (EV) includes a primary circuit having seven bidirectional switches electrically linked to a primary wire of a transformer and configured to receive an alternating current (AC) voltage; and a secondary circuit comprising a plurality of diodes arranged to rectify the AC voltage into a DC voltage, electrically linked to the secondary leads of the transformer such that the DC fast charger unidirectionally converts the AC voltage received at the primary circuit into the DC voltage output by the secondary circuit.

The invention relates to the technical field of battery management, and discloses a state monitoring and predicting method of an intelligent battery pack and related equipment. The method comprises the steps of obtaining original battery pack data and historical operation data of various preset battery pack parameters in a target intelligent battery pack, and performing signal filtering and spectral decomposition on the original battery pack data; abnormal data detection, abnormal correction, multi-mode characteristic decomposition and phase space mapping of the battery pack state are carried out on the initial battery pack characteristic data; extracting corresponding state-of-charge dynamic characteristics in the state evolution characteristics of the battery pack, and performing multilayer mapping and threshold classification on the state evolution characteristics based on historical operation data to obtain a state monitoring result; and obtaining a state transition matrix through state parameter coupling mapping and transition probability calculation, performing state evolution path calculation, and outputting a prediction result. According to the invention, correlation analysis among electrochemical parameters is realized, and reliable evaluation and accurate prediction of the state of the battery pack are enhanced.

A base station can receive, while a primary cell (PCell) and a secondary cell (SCell) are in a second power state, an uplink wake-up signal from a wireless device. In response to receiving the uplink wake-up signal, the base station can transition the PCell to a first power state and maintain the SCell in the second power state.

The invention discloses a charging chip real-time power scheduling control method, and relates to the technical field of new energy automobile charging pile power scheduling, and the method comprises the following steps: S1, obtaining the operation parameters of charging equipment in real time, including the voltage fluctuation value of an input power grid, the output current of a charging interface, and the temperature of a charging chip; according to the method, the power grid voltage fluctuation, battery SOC / temperature and chip working condition multi-source data are collected in real time, a multi-target optimization model of dynamic weight distribution is combined, the service life loss of the battery is minimized, the charging efficiency and the power grid load are balanced at the same time, the user charging mode and the battery state difference are taken into consideration by adopting a priority scheduling strategy, and the charging efficiency is improved. Millisecond-level power fine control is achieved through cooperative adjustment of the PWM duty ratio and the switching frequency, personalized charging requirements are met, the overall energy efficiency is improved, a dual safety defense line is constructed through a graded load reduction protection mechanism and a fuzzy control algorithm, and a power distribution scheme is intelligently reconstructed when a power grid is abnormal or a battery is overheated.

Systems, methods, and articles for a portable power case are disclosed. The portable power case is comprised of at least one battery and at least one PCB. The portable power case is operable to supply power to a transceiver. The portable power case is operable to be charged using a DC power source (e.g., solar panel, wind turbine, water turbine). A plurality of portable power cases, DC power sources, and transceivers are operable to form a mesh network.

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.