48967 results about "Battery pack" 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 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 power battery packs, and discloses a power battery pack state evaluation method and system, and the method comprises the steps: collecting an operation characteristic data set of a power battery pack under various working conditions, and constructing a hybrid evaluation model; performing parameter configuration on the double-extended Kalman filter to obtain an improved filter; acquiring real-time voltage, current and temperature data, and inputting the real-time voltage, current and temperature data into the improved filter for state calculation to obtain a charge state value and an internal parameter value; inputting the charge state value and the internal parameter value to perform multi-branch adversarial neural network analysis to obtain a fault mode type and a fault severity; according to the method, the multi-factor health index is calculated, particle swarm optimization fitting is carried out on the degradation curve of the power battery pack, the residual service life prediction value is obtained, more accurate residual service life prediction is further achieved, the precision in a wide temperature range is remarkably improved, and the difference between monomers in the power battery pack can be effectively processed.

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 wearable pouch operable to hold at least one portable battery pack and other power or communications equipment. The wearable pouch includes a main body with a front side, a back side opposite the front side, at least one sealable opening, and at least one opening for at least one lead from the at least one portable battery pack secured within the wearable pouch.

The invention discloses an energy storage battery pack equalization control method, which belongs to the field of energy storage battery packs, and comprises the following steps: S1, constructing a multi-physics field-aging coupling model; s2, based on a multi-physical field-aging coupling model, space-time-frequency domain joint state estimation and unbalanced pattern recognition are realized; s3, formulating optimal equalization topology and control parameters according to space-time-frequency domain joint state estimation and an unbalanced mode recognition result; s4, establishing an electric-thermal-fluid multi-physics field coupling control model, and optimizing equalization current regulation and control, liquid cooling flow velocity and cooling fan rotating speed based on the optimal equalization topology and control parameters; and S5, optimizing the full life cycle of the digital twin drive. By adopting the equalization control method for the energy storage battery pack, the efficient coordination of the energy storage battery pack from dynamic modeling, accurate state estimation and intelligent equalization decision to full life cycle management is realized, and the equalization efficiency and the system reliability are remarkably improved.

A battery pack includes a battery housing; a battery module disposed in the battery housing, where the battery module includes multiple battery cells, and at least one of the battery cells is a solid-state battery; and a control circuit disposed in the battery housing and configured to use the battery module to supply the electric power to the power tool. The energy W of the battery pack and the volume V1 of the battery pack satisfy the following: when the energy W is greater than or equal to 200 Wh, the volume V1 is less than or equal to 400 cm3; or when the energy W is greater than or equal to 300 Wh, the volume V1 is less than or equal to 800 cm3; or when the energy W is greater than or equal to 700 Wh, the volume V1 is less than or equal to 2500 cm3.

The invention discloses a long-term failure prediction and fault early warning method in a storage battery pack, and relates to the technical field of data prediction, and the method comprises the following steps: S1, obtaining key characteristic parameters of the storage battery pack in real time; s2, constructing a health state evaluation model of the storage battery pack; s3, analyzing short-term dynamic change and long-term degradation trend of the battery; s4, a fault early warning model is constructed, and the model sends out early warning information when the battery is abnormal; s5, identifying and deducing a fault mode; and S6, making an optimization strategy. According to the long-term failure prediction and fault early warning method for the storage battery pack, the accuracy and timeliness of fault prediction of the storage battery are greatly improved through dynamic modeling of the health state. Compared with the prior art, the method has the advantages that the abnormal change of the battery can be quickly detected in a short time, the residual life of the battery is accurately predicted through long-term decline analysis, and the problems of misinformation and missing report in a traditional method are remarkably reduced.

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.

A tracking and adaptive compensation method for power fluctuation of a pulse load of a ship is provided. The method includes: based on a microgrid topology model of the ship, a pulse load model of the pulse load, and an interharmonic power and pulse parameter relationship model, performing power fluctuation tracking, frequency selection and a power spectrum analysis to obtain filtering parameters of a low-pass filter and filtering parameters of a band-pass filter; performing waveform adaptation to eliminate a phase delay caused by the low-pass filter and the band-pass filter, and obtaining a control command for a power compensation strategy; and generating, by a power loop, a pulse width modulation (PWM) signal to control a switching tube in a bidirectional direct current (DC) / DC converter of each of an energy storage battery pack and a supercapacitor of a hybrid energy storage system.

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.

To enable detection of temperature rise of electronic equipment disposed in a battery pack due to radiation heat in an exhaust passage.SOLUTION: A battery pack 50 disposed on the underfloor of a hybrid vehicle 1 includes: a battery stuck 55 where battery cells are laminated; a battery ECU51 and a monitoring unit 52. A temperature sensor 53 fitted to a flue gas passage 94 is disposed at a position between the battery ECU51 and the monitoring unit 52, and an exhaust pipe 21. The temperature sensor 53 is disposed between the battery ECU51 and the monitoring unit 52, and the exhaust pipe 21, and therefore favorably detects the temperature rise of the battery ECU 51 and the monitoring unit 52 due to radiation heat from the exhaust pipe 21.SELECTED DRAWING: Figure 2

The invention relates to the technical field of motor train unit battery pack evaluation, and provides a motor train unit battery health state evaluation and life prediction method and system, and the method comprises the steps: building a database through voltage fluctuation and temperature data in a floating charge state, and analyzing a correlation mechanism between target parameters, such as a battery capacity retention ratio and active lithium stock, and internal chemical substance loss; respectively extracting driving behavior characteristics, temperature parameters and electrochemical impedance spectroscopy by adopting second-level, minute-level and hour-level time granularity, and aligning multi-frequency data by utilizing a dynamic time warping algorithm; constructing a graph structure model taking the electrochemical parameters as nodes and cross-level association as edges, and extracting a battery aging characteristic topological relation through a graph neural network; and finally, establishing a prediction model fused with multi-dimensional features, and realizing accurate prediction of the residual life and the capacity recession inflection point of the battery. And the multi-scale characteristics are combined with an electrochemical mechanism, so that the accuracy of battery health state evaluation under a complex working condition is improved.

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 relates to the technical field of mobile energy storage vehicle energy management, and discloses a mobile energy storage vehicle energy management method based on an intelligent algorithm, and the method comprises the steps: collecting the charging and discharging rate of a battery pack, the environment temperature, the power grid load fluctuation and other multi-dimensional energy state data; a heterogeneous layered architecture of edge computing nodes, a cloud collaboration platform and a vehicle-mounted control terminal is constructed, energy partitions are divided according to peak valleys of a power grid, and low-delay response, global optimization and local closed-loop control modes are configured; fusing the multi-source heterogeneous energy data streams and removing abnormal values; a dynamic optimization decision model is constructed through a battery degradation model and a power grid supply and demand balance equation, and a multi-parameter collaborative constraint relation is solved through simultaneous solving of a multi-target iteration solver and a parallel gradient descent algorithm; and generating a three-level adaptive response instruction sequence of local battery overload alarm, regional power grid frequency modulation early warning and global energy scheduling imbalance pre-judgment based on constraint boundary triggering conditions. According to the method, the accuracy, the collaboration and the robustness of energy management are improved.

The invention relates to the technical field of battery management, and discloses an equalizing charging method, device and equipment for a power battery pack. The method comprises the following steps: performing parameter acquisition on a power battery pack, measuring terminal voltage values, temperature values and charge and discharge current values of each single battery, and calculating a state-of-charge value and an internal resistance change rate of each single battery; inputting into a multi-head self-attention network for feature extraction, and generating a state correlation feature matrix; constructing an Actor network and a Critic network based on the state correlation characteristic matrix, and outputting an equalization current value, a constant-current or constant-voltage equalization mode and equalization time through the Actor network; according to the equalization current value, the equalization mode and the equalization time, the DC-DC converter in the main equalization loop is controlled to generate constant current output, or the Buck-Boost converter in the secondary equalization loop is controlled to generate constant voltage output, the equalization efficiency of the power battery pack is guaranteed, meanwhile, energy loss is reduced, and temperature self-adaptive control over the equalization circuit is achieved.

The invention discloses a battery energy storage safety management system based on a BMS, and relates to the technical field of battery energy storage safety management, the system comprises a data acquisition module, a data analysis processing module, an equalization strategy generation module, an equalization execution module and a safety monitoring early warning module; operation parameters of the battery pack and the single batteries are comprehensively acquired through the data acquisition module, the actual operation state of the battery pack is accurately sensed, health factors are calculated by comprehensively considering multiple factors, weight coefficients are dynamically adjusted, the characteristics of the single batteries are comprehensively considered, and the charging and discharging states of the battery pack are calculated according to the health factors, the charging and discharging stages and the environment temperature. The equalization strategy and current are determined through a self-adaptive algorithm, dynamic response to the running state of the battery pack and environment change is achieved, equalization operation is accurately executed through the equalization execution module, accurate equalization control is achieved, and through real-time monitoring of the safety monitoring and early warning module, safe running of the battery in different environments is guaranteed, the performance of the battery is improved, and the service life of the battery is prolonged.

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.

This application relates to the technical field of battery packs, and a battery pack lower box body, a battery pack, an apparatus, and an assembly method of battery pack lower box body are provided. The battery pack lower box body includes a box frame, a base plate, a stiffening beam, and a stiffener. The base plate is arranged at the bottom of the box frame; the stiffening beam is arranged inside a space enclosed by the box frame and on top of the base plate; and the stiffening beam is fixed to the box fame via the stiffener, and the stiffener is welded to the box frame to form a weld seam, where a length of the weld seam is kept greater than a sum of side lengths of a projection of the stiffening beam onto the box frame.

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.

A box structure includes a cooling member and a frame. The cooling member includes a main pipe and at least one branch pipe connected to the main pipe. The main pipe is configured to pass a coolant into the branch pipe, and the branch pipe is configured to contact the battery pack to cool the battery pack. An accommodation groove is created at a bottom of the frame, at least a part of the main pipe is located in the accommodation groove, and the main pipe is bonded and fixed to the accommodation groove.

The invention provides a battery state of health (SOH) cooperative control method and system of a dynamic reconfigurable energy storage system, and belongs to the field of battery health. The method comprises the following steps: designing a multi-time-scale SOH real-time identification model based on transient characteristic quantification, and realizing second-level updating of an electric core-level health state map; and by adopting power-SOH dynamic matching double-layer reconstruction control, the health and balance of the battery pack are ensured. In addition, the SOH tracking error is less than 1.5% by utilizing a distributed collaborative observer and topology adaptive state estimation; dual-mode control is realized through model predictive control and grouping dynamic optimization; and finally, topology is optimized by using a deep learning strategy and a life loss entropy objective function, series-parallel mode life equalization is realized, and the combined life cycle of the system is prolonged. According to the invention, the service efficiency of the battery can be effectively improved, the service life of the battery is prolonged, and the reliability and stability of the whole energy storage system are greatly enhanced.

The invention discloses a dynamic control method of a lithium battery module intelligent equalization management system, and relates to the field of lithium battery module equalization, and the method comprises the steps: constructing a hierarchical data fusion platform, and generating a battery state data set containing SOC, SOH and an inconsistency evaluation value; a credibility reasoning algorithm is adopted to calculate a single body imbalance credibility factor value to divide an equalization stage, and equalization current is dynamically adjusted; a charge and discharge curve health factor is extracted through an LAB health factor algorithm, SOH is updated online in combination with extended Kalman filtering, and an SOH attenuation rate is fed back to the battery model; in-module equalization is carried out through a bidirectional energy transfer circuit, inter-module equalization is carried out through a ring bus topology, and layered topology linkage control is formed. The method has the advantages that the consistency and the heat management efficiency of the battery pack are improved through the hybrid equalization strategy and the layered topology linkage control, the service life of the battery pack is prolonged, and the overall performance and the safety of the electric vehicle are improved.

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 provides a photovoltaic energy storage power station battery pack temperature intelligent regulation and control and performance optimization method and system, and relates to the technical field of energy storage battery packs, and the method comprises the steps: obtaining temperature sensor array data, working state data and environment parameter data, and predicting the temperature field distribution through a deep neural network model; and constructing a temperature field topological relation graph by adopting a graph neural network, constructing a hierarchical progressive temperature control strategy based on the temperature field feature vector, generating a cooling system control instruction, and realizing temperature field intelligent optimization control of the battery pack. The method can accurately predict the temperature field distribution, improves the working efficiency and service life of the battery pack, and reduces the energy consumption.

A method for online diagnosis of power battery voltage fault based on entropy algorithm, in which voltage time-series values of individual cells in a battery pack of a to-be-diagnosed vehicle are obtained to construct a first voltage data matrix; the first voltage data matrix is intercepted by a sliding time window to form a second voltage data matrix; voltage values of the second voltage data matrix are subjected to data exclusion and reconstruction to form a third voltage data matrix; Shannon entropy values of cells are calculated; each Shannon entropy value is transformed into an abnormal fluctuation evaluation coefficient of individual cells; whether there is a abnormal cell is determined; if yes, position and occurrence time of the abnormal cell are determined, and an abnormality degree is evaluated; otherwise, the time window is moved downward, and the above steps are repeated for the next iteration.

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.

The invention relates to the technical field of battery control, and discloses an energy storage battery output power self-adjusting system, which comprises a data acquisition module used for acquiring working parameters of an energy storage battery in real time; the historical database is used for storing previous operation data of the energy storage battery and the load equipment; the data processing and analyzing module is used for cleaning and normalizing the historical data; a self-adaptive adjustment strategy generation module; the invention discloses an energy storage battery output power self-adjusting method. The method comprises the following steps: S1, collecting battery pack data through a CAN bus; s2, establishing a load equipment priority list; s3, constructing a multi-time scale optimization model; s4, implementing dynamic impedance matching; s5, generating a power distribution instruction set; according to the invention, the output power prediction model is established by using the machine learning algorithm in combination with the previous operation data in the historical database, and the load demand trend in a period of time in the future can be accurately predicted.