35357 results about "Battery pack" patented technology

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 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.

Described herein is an electric aircraft comprising a battery pack to power the aircraft. The battery pack comprises a housing, an arrangement of battery cells within the housing and a crumple structure associated with the battery pack. The crumple structure is beneath the arrangement of battery cells and is adapted to compress in the event of an impact from beneath the aircraft. A venting channel is fluidically coupled to the battery cells and arranged to vent gasses away from the battery cells. The venting channel passes through the crumple structure and is adapted to deform with the crumple structure in the event of an impact from beneath the aircraft.

A dual-motor mixed-hybrid powertrain system, by performing pulse modulation control, i.e., series-hybrid intelligent start-stop control or parallel-hybrid intelligent power switching control, on the instantaneous power time-varying functions of an engine and a battery pack, can convert the surface working condition of an analog electric control engine into a simpler line working condition of a digital pulse control (DPC) engine, either a pre-determined high-state line working condition in the high-efficiency combustion area or a pre-determined non-combustion low-state line working condition with zero fuel consumption and zero pollutant emissions multiplexed in time. The traditional fixed one-to-one bidirectional mapping between an engine working condition and a vehicle working condition is converted into a dynamically adjustable many-to-many bidirectional mapping to achieve the full coverage of any overall vehicle working condition, achieving decoupling between a DPC engine working condition and the overall vehicle working condition and decoupling between software and hardware of a hybrid powertrain.

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.

The invention discloses a lithium ion battery thermal runaway multi-dimensional characteristic parameter early warning method, which comprises the following steps: S1, collecting internal multi-dimensional characteristic parameters of a battery pack in real time, including an expansion force signal, a voltage signal, a characteristic gas concentration signal and a temperature distribution signal; s2, constructing a multi-parameter collaborative analysis model, performing dynamic fusion analysis on the expansive force signal, the voltage signal, the characteristic gas concentration signal and the temperature distribution signal, and outputting a thermal runaway risk value through a weighted decision algorithm; s3, comparing the thermal runaway risk value with preset different early warning thresholds in an extremely early stage, determining a thermal runaway risk level, and outputting a multi-signal collaborative early warning instruction according to the thermal runaway risk level; and S4, linkage triggering is carried out on the battery pack to carry out cooling, smoke exhausting, fire extinguishing and explosion suppression prevention and control treatment. According to the invention, the problems of manual early warning disposal, missing of an optimal prevention and control window and high false alarm rate of single-parameter early warning are solved.

An electric vehicle battery pack with a reinforced cover to better support vehicle cabin loads such as seat submerge loads, seat pitch loads, and the like. The cover has a number of reinforcing members affixed thereto and positioned over cross members of the battery pack frame. Loads imposed by the cabin are thus transferred to the reinforcing members and then to the rigid cross members. In this manner, battery packs of embodiments of the disclosure better support cabin loads applied thereto, improving their strength and better protecting more delicate components within the battery pack.

The invention relates to the technical field of new energy automobile thermal management, in particular to a direct cooling and heating management system and method in an automobile power battery. The method comprises the following steps: acquiring vehicle real-time operation condition data and battery pack state monitoring data; working condition mode recognition processing is conducted according to the vehicle real-time operation working condition data, and vehicle working condition mode feature data are generated; performing thermal load demand analysis on the battery pack state monitoring data based on the vehicle working condition mode characteristic data to obtain dynamic thermal load demand data of the battery pack; and according to the dynamic thermal load demand data of the battery pack, refrigerant demand quantity calculation and analysis are carried out, the refrigerant supply capacity of the current direct cooling system is evaluated, and refrigerant supply and demand matching feature data are obtained. The specific requirements of three heat sources of the battery, the motor and the cockpit for the refrigerant flow are quantified, and a refrigerant distribution strategy is formed according to the proportional relation, so that the safety of the battery is improved, and the driving comfort and the working stability of the electric driving system are also guaranteed.

The invention relates to the technical field of lithium battery safety monitoring, and discloses a lithium battery pack thermal runaway early warning system based on multi-mode sensing. The system comprises multi-source sensing data acquisition, thermal field feature tensor construction, thermal field reconstruction and thermal coupling association network generation. The multi-source sensing data acquisition module acquires multi-dimensional heterogeneous data from a temperature sensor, a voltage and current monitoring unit, a gas component detector and an acoustic emission sensor, and generates a standardized data bin through timestamp alignment and missing value compensation; the thermal field feature tensor construction module extracts features such as temperature gradient and electrochemical response from the data bin in a multi-scale manner, and constructs a tensor in combination with time continuity; the thermal field reconstruction module generates association diagrams according to the feature space-time distribution and fuses the association diagrams into a lithium battery pack three-dimensional thermal field reconstruction map; and the thermal coupling association network generation module extracts a feature vector cluster, calculates an entropy weight value, and generates a network according to a high-entropy node space adjacency relationship. According to the system, multi-dimensional data fusion is realized, and the thermal runaway evolution law can be comprehensively described.

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 a linkage protection method of a battery energy storage system and the battery energy storage system. The linkage protection method of the battery energy storage system comprises the following steps: S1, monitoring multi-dimensional operation parameters of a battery pack in real time; s2, performing parallel feature extraction on the multi-dimensional operation parameters based on a preset dynamic association model; s3, establishing a three-dimensional safety assessment coordinate system, performing vector overlay analysis on each representation value and a corresponding axial threshold boundary, and outputting four-stage dynamic safety level criteria; and S4, activating the hierarchical protection coprocessor according to the dynamic security level criterion. According to the method, through the core design of multi-dimensional dynamic correlation modeling and hierarchical cooperative control, the problems of response delay and protection blind areas of a traditional method under complex working conditions are solved, an innovative three-dimensional safety evaluation system recognizes potential risks in advance, the cooperative efficiency of a hierarchical protection mechanism is improved, and the false triggering rate of disconnection decision is reduced.

The invention provides a health state monitoring and service life prediction method and system for an energy storage battery pack, relates to the field of energy storage batteries, and solves the problems that prediction models in the prior art mostly adopt a single machine learning algorithm and lack adaptability to a battery degradation mechanism and actual working conditions, and the prediction efficiency is poor. And the battery health state evaluation and residual life prediction precision is low. The method comprises the following steps: preprocessing an original data set, analyzing a multi-dimensional health feature vector, and constructing a health feature matrix; constructing a health state evaluation model based on the improved CNN-LSTM hybrid model and by fusing battery degradation physical mechanism constraints; inputting the health feature matrix into a health state evaluation model, and outputting a current SOH value; and predicting residual life information based on the current SOH value and the load fluctuation correction coefficient. The method is used in the process of health state evaluation and residual life prediction of the energy storage battery pack.

The invention relates to the technical field of electric forklifts and energy storage, and discloses an intelligent battery management system and prediction method based on optical fiber sensing, the system comprises a distributed optical fiber sensing network, a data acquisition and processing module, an intelligent analysis decision module, a battery management control module and a cloud platform interaction module; the method comprises the following steps: collecting a battery signal through a snakelike-spiral optical fiber network, reconstructing a temperature / strain field through noise reduction and filtering, extracting multi-modal features, inputting the multi-modal features into an LSTM model to predict thermal runaway, generating a risk level in combination with an improved equation, dynamically adjusting charging, discharging and heat dissipation strategies, and realizing three-dimensional visual monitoring through a cloud platform. According to the invention, high-precision distributed monitoring of the temperature / strain field of the battery pack is realized through the BOTDR optical fiber sensing network, the LSTM prediction model is constructed by fusing multi-physical field characteristics, and thermal runaway is warned in advance. The improved algorithm reduces the false alarm rate, charging and discharging parameters are dynamically adjusted to prolong the service life of the battery, and a three-dimensional visual platform improves the operation and maintenance efficiency.

The invention discloses a storage battery capacity attenuation trend prediction method, and belongs to the technical field of storage battery prediction. By collecting voltage, current and temperature data of each monomer in real time and combining historical capacity attenuation and internal resistance growth data, the method identifies a voltage and capacity difference value, evaluates cyclic stress non-uniform distribution, and determines a current sharing proportion and a load unbalance degree. Identifying an abnormal mode of new battery overload and aged battery deep discharge, constructing a mixing abnormal working condition identification mode, if the unbalance degree exceeds the standard, adaptively adjusting the charging and discharging time and the current switching frequency, establishing a load balance control framework, predicting the capacity attenuation rate and the residual cycle index of each monomer, and determining the capacity matching degree and the life matching degree; finally, a comprehensive residual life estimation value and a credibility interval are generated through fusion; the performance balance of the mixed battery pack is remarkably improved, the overall service life is prolonged, and the method is suitable for real-time monitoring of a battery management system.

The invention relates to the technical field of batteries, and discloses a single battery and a battery pack, the single battery comprises a shell, a first cover plate, a battery cell and a first pole, along a first direction, one end of the shell is provided with a first opening, the battery cell is arranged in the shell, a first tab of the battery cell extends out from one end part of a battery cell body of the battery cell, and a second tab of the battery cell extends out from the other end part of the battery cell body; the first electrode lug is bent towards the first cover plate and forms a first bending point and a second bending point, the first electrode column and the first electrode lug are welded to form a first welding printing area, the first welding printing area is located between the second bending point and the end part of the first electrode lug, the length of the first electrode lug between the first bending point and the second bending point is L, in a second direction perpendicular to the first direction, a first spacing distance between the edge of the first welding printing area and the edge of the first cover plate is d, in the first direction, a second spacing distance between the inner side of the first cover plate and the root of the first tab is c, and L is larger than or equal to 0.6 mm and smaller than or equal to 20 mm. And the risks of tab tearing and battery monomer short circuit are reduced.

The invention relates to the technical field of battery management systems, and particularly discloses a multi-dimensional parameter fused multi-battery-pack parallel dynamic balance control method and system, and the system comprises a multi-dimensional space-time parameter deep fusion module, an aging driving parameter weight self-adaption unit, a twin closed-loop strategy iteration system and a battery pack dynamic balance execution layer. Utilizing the state data output by multi-dimensional parameter fusion to drive aging weight adjustment; a weight strategy is verified and optimized through a twin system, and then feedback parameter fusion and weight adjustment are performed, so that the system continuously adapts to the battery state change. Multi-dimensional parameters such as voltage, current, temperature, internal resistance, SOC, SOH and the like are collected, time-space correlation characteristics are constructed in combination with timestamps and spatial position information, electrochemical, thermal and life states and time-space scene correlation of the battery are comprehensively covered through preprocessing and time-space attention mechanism neural network fusion, the one-sidedness of traditional single parameter evaluation is solved, and the evaluation accuracy is improved. Potential imbalance risks are identified in advance.

The invention relates to the technical field of battery fault detection, and particularly discloses a new energy automobile battery fault intelligent diagnosis method and system, and the method comprises the steps: collecting the three-dimensional time sequence data flow of the voltage, temperature and internal resistance of a battery pack through a distributed sensor array; the environment temperature, the number of charge and discharge cycles and the vehicle operation condition parameters are synchronously integrated as auxiliary diagnosis dimensions, and a multi-source heterogeneous data sensing system is constructed; the method comprises the following steps: preprocessing original data by adopting a dual-channel hybrid filtering architecture based on a multi-source heterogeneous data sensing system, and establishing an adaptive filtering parameter adjustment mechanism to generate multi-modal data; according to the method, multi-dimensional time sequence data and auxiliary diagnosis parameters of the battery pack are comprehensively collected through a constructed multi-source heterogeneous data sensing system, a rich and accurate data basis is provided for fault diagnosis, noise interference is reduced through a two-channel hybrid filtering architecture and a self-adaptive filtering parameter adjustment mechanism, and the fault diagnosis accuracy is improved. And a solid foundation is laid for subsequent feature extraction and model training.

Provided are an electric wagon and an electronically power assisted wagon. The electric wagon includes a wagon body including at least a foldable frame; a handle device connected to the wagon body; a walking assembly including at least driving wheels; a drive motor configured to drive the driving wheels to rotate; a power supply connection portion configured to be detachably connected to a battery pack for supplying power to the drive motor, and a controller electrically connected to at least the drive motor to control the drive motor to rotate.

The invention relates to the technical field of electrochemical energy storage, and discloses a method and a system for predicting the residual life of a battery of an electrochemical energy storage system. According to the method, multi-dimensional operation data such as temperature distribution, current ripples and voltage attenuation curves in the charge-discharge cycle process of a target battery pack are collected, and feature weight parameters are generated in combination with real-time working conditions and historical database matching results; a degradation characteristic mode in a full life cycle is extracted as a reference template, and a dynamic degradation characteristic space is established; constructing a multi-scale coupled life prediction model, and constructing an adaptive evaluation function by iteratively updating model parameters and reference template physical field coupling parameters; triggering cross-cycle data verification, and carrying out working condition compatibility verification; and generating a feedback matrix based on the prediction error index and the verification result, and dynamically adjusting the mapping relation between the feature weight and the reference template. The method improves the accuracy and adaptability of battery residual life prediction, and is suitable for an electrochemical energy storage system under complex working conditions.

The invention relates to the technical field of solar cells, and particularly provides a four-end laminated cell module, preparation thereof and a photovoltaic module. The four-end laminated battery assembly comprises a bottom battery pack and a perovskite battery pack, the perovskite battery pack comprises a plurality of perovskite sub-batteries, and each perovskite sub-battery comprises a first electrode layer, a first transmission layer, a perovskite light absorption layer, a second transmission layer and a second electrode layer which are stacked in sequence; the perovskite sub-cell further comprises a first groove penetrating through the first electrode layer, a second groove penetrating through the first transmission layer, the perovskite light absorption layer and the second transmission layer, and a third groove penetrating through the second electrode layer. The total width of the second trench and the third trench is 500-770 [mu] m. According to the technical scheme of the invention, the optical distribution can be adjusted, and the optical performance of the module can be improved.

The invention discloses a lithium battery thermal runaway control and safety response strategy system, which realizes the real-time monitoring and prediction of the internal temperature and voltage state of a battery through thermal-electric coupling modeling and multi-sensing data fusion, and realizes the real-time monitoring and prediction of the internal temperature and voltage state of the battery based on a model prediction control and threshold enhancement strategy. And triggering graded safety response in the early stage of thermal runaway. The system adopts a multi-stage response mechanism including measures of primary early warning, active cooling intervention, emergency power-off fire extinguishing and the like, a gas suppression path and cooling mode switching function is designed, and heat diffusion and spreading and toxic gas harm are effectively suppressed. Compared with a method depending on complex digital twinning or deep learning, the method is simple in structure, rapid in response and low in data dependence, has good system stability and engineering applicability, and can be widely applied to the field of lithium battery safety management of electric vehicle battery packs, energy storage power stations and the like.

The invention relates to the field of power battery energy management, in particular to a power battery pack charging and discharging efficiency dynamic optimization system and method, and the system comprises a central control unit, a multi-dimensional sensing module, a dynamic charging and discharging control module, a working condition-heat dissipation cooperation module, a parallel cell equalization module and an aging adaptation optimization module. The multi-dimensional sensing module is used for collecting basic parameters, polarization characteristics, parallel state, aging degree and environmental parameters of a battery pack, the dynamic charging and discharging control module is used for collecting data, the working condition-heat dissipation coordination module adopts a three-stage heat dissipation framework, and the parallel battery cell balancing module is used for balancing the battery pack through a dynamic current distribution and voltage active balancing double-coordination mechanism. The aging adaptation optimization module is used for grading and differentially optimizing charge and discharge parameters and heat dissipation strategies based on the aging degree of the battery cell, so that the problems of low charge and discharge efficiency, poor wide temperature range adaptability, weak parallel consistency, fast aging attenuation and multi-factor coupling deterioration of the power battery pack are solved.

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

A work apparatus includes: a housing; a motor support unit designed separately from the housing, the housing being fixed to the motor support unit. A drive motor is designed as an electric motor for driving a tool and is attached to the motor support unit. A receptacle housing designed separately from the motor support unit for receiving at least one battery pack for supplying the drive motor with electrical power is fixed to the housing. The receptacle housing and the motor support unit are designed and arranged relative to each other in such a way that the receptacle housing is able to move relative to the motor support unit in order to reduce, in particular to prevent, direct transmission of inertial forces emanating from the receptacle housing to the motor support unit in the event of an impact load on the work apparatus.

A battery charging system includes a charging rack comprising multiple receptacle, multiple battery assemblies. Each of the battery assemblies includes a housing having a handle, rechargeable battery cells disposed within the housing, and a mating feature protruding away from the housing, wherein the mating feature is configured to selectively connect the battery assembly with the receptacle of the charging rack and includes multiple ports electrically connected to the battery cells and configured receive power from the charging rack to charge the battery cells, and wherein the handle comprises a release button configured to selectively disengage the battery assembly from the receptacle of the charging rack.

The invention relates to the technical field of electrical testing, in particular to a battery testing method and system, and the method comprises the steps: obtaining a voltage data sequence and historical log data of each cell in a to-be-tested battery pack; determining a dynamic instability score of each battery cell according to the parameter volatility index of each battery cell and the spatial correlation weight between each battery cell and the adjacent battery cell; on the basis of the historical log data, analyzing a repeated cumulative effect of all historical damage events corresponding to each battery cell, and determining a historical damage degree of each battery cell; performing deep fusion on the dynamic instability score and the historical damage degree through a nonlinear function to obtain a comprehensive risk index of each battery cell; and based on the extreme values and distribution of the comprehensive risk indexes of all the cells, determining a fault degree capable of representing the overall health risk of the battery pack, and determining a health test result of the battery pack according to the fault degree. According to the method, the accuracy and reliability of battery pack testing are improved.

The invention provides a battery dynamic equalization method and system based on cloud computing, relates to the field of battery management, and solves the technical problem of insufficient utilization of battery equalization resources in the prior art. The method comprises the following steps: a cloud calculates a fault probability index of each monomer based on acquired operation data and historical equilibrium data of each monomer in a battery pack, and marks the monomer of which the fault probability index is smaller than a preset fault threshold as a normal monomer; according to the battery state data and the power grid load data of the normal monomers, calculating the dynamic balance priority of each normal monomer; distributing equalization current and equalization electric quantity of each normal monomer based on the dynamic equalization priority to obtain an equalization strategy, and sending the equalization strategy to a local end; and the local end executes the equalization strategy, and when a re-triggering condition of the equalization strategy is met, an equalization calculation request instruction is sent to the cloud end, and the calculation process is repeated. The method is used in the dynamic balance control process of the battery pack under the scenes of energy storage, power and the like.

The invention relates to the technical field of BMS and the like, and provides a battery management system based on adaptive digital twinning, a physical layer of the battery management system comprises a battery pack, a sensor network and an edge computing node, and is responsible for data acquisition and preprocessing; the digital twin layer comprises a self-adaptive multi-scale model and a real-time data engine, battery behaviors are dynamically simulated by coupling electrochemical, thermal and aging models, a future state trajectory prediction result is output, and the real-time data engine fuses sensor data, historical data and simulation data to drive model updating; the intelligent decision-making layer comprises a reinforcement learning controller and a fault prediction module which are deployed in a local server, the reinforcement learning controller dynamically optimizes a charging and discharging strategy according to a prediction result of the digital twinborn layer and issues and executes the charging and discharging strategy, and the fault prediction module analyzes multi-source time sequence data based on an LSTM network so as to early warn thermal runaway and short circuit risks in advance. According to the invention, long-term accurate mapping and adaptive adjustment between the battery physical entity and the digital model can be realized.

The battery pack includes an enclosure including interconnected walls, one or more battery cells contained within the enclosure, and a heat-conducting plate defining one or more of the interconnected walls and being structurally load-bearing. The heat-conducting plate has a first section, the one or more battery cells in heat exchange relationship with the first section, and a second section in heat exchange relationship with the first section. The second section is in heat exchange relationship with an environment outside the enclosure for providing heat exchange between the one or more battery cells and the environment.

The invention discloses a power battery active equalization control method, which comprises the steps of extracting characteristic information of a battery pack, generating quantitative state change trend description, and obtaining potential unbalanced risk point distribution; analyzing operation constraint conditions related to load changes, generating trigger weights of unbalanced risks, screening high-priority risk points, generating a preliminary equalization path planning scheme, calculating equalization operation current intensity of each single battery in different time periods in combination with battery pack state data collected in real time, and generating an equalization operation schedule; obtaining feedback state data in real-time execution, and comparing the feedback state data with the predicted risk point to obtain a final control decision instruction; and for the final control decision instruction, acquiring executed battery pack state update data, analyzing the consistency of the battery pack state update data and a target equalization state, judging whether an equalization path or time arrangement needs to be finely adjusted, and generating a continuously optimized equalization control scheme. Accurate prediction and balance control of the state of the battery pack are realized.