13402 results about "Lithium battery" patented technology

The invention relates to a method for preparing a lithium iron phosphate / carbon composite material of a lithium ion battery, which belongs to the technical field of lithium ion batteries. The method for preparing the lithium iron phosphate / carbon composite material of the lithium ion battery comprises the following steps of: 1) preparing a suspending graphene-dispersed aqueous solution system, namely, crushing graphite to 1 to 5 microns, adding the crushed graphite into distilled water or purified water, adding 0.1 to 5 percent of surfactant, heating with stirring the mixed solution to 180 to 250 DEG C in a sealing way, performing stirring for 2 to 6 hours and reducing the temperature; 2) crushing lithium iron phosphate to the particle size of 1 to 5 microns, adding the crushed lithium iron phosphate into the distilled water or the purified water, adding with stirring 0.01 to 1 percent of coupling agent, performing uniform stirring, adding the graphene-dispersed aqueous solution, and performing stirring and filtration; and 3) vacuum-drying solid powder obtained by the filtration, and calcinating the dried solid powder for 2 to 12 hours to obtain the graphene-coated lithium iron phosphate cathode material. The method has the advantages of simple process, high material performance, high conductivity, high bulk density, high compacted density and the like.

The present invention relates to the technical field of lithium battery state estimation, and in particular to a fuzzy EKF-AH algorithm-based state of charge (SOC) estimation and correction method for a lithium iron phosphate battery. The method comprises: by taking a certain startup of an energy storage device as a start, a BMS reading an SOC and a state of health (SOH) at the previous shutdown, and on the basis of a standby time, selecting an open circuit voltage (OCV) or an EKF algorithm to correct the SOC; using an EKF-AH algorithm to estimate the SOC, establishing, on the basis of fuzzy control, a fuzzy rule library associated with the SOC and the SOH, and dynamically adjusting a weight and a measurement noise deviation of the EKF-AH algorithm; and calculating estimation differences between the EKF algorithm and an ampere-hour integration method, and if the sum of the estimation differences is greater than a corresponding threshold, issuing an SOH correction warning. The present invention improves the estimation accuracy of the entire life cycle of the lithium iron phosphate battery, and assists the correction of the SOH.

The invention relates to the technical field of lithium battery diagnosis, in particular to a lithium battery fault diagnosis system based on real-time impedance monitoring, which comprises a disturbance signal acquisition module, an impedance track extraction module, a polarization response comparison module, an abnormal period judgment module and a structural risk output module. According to the method, a dynamic comparison mechanism of polarization response time difference and structure state parameters is introduced, periodic risk grading and characteristic quantity marking are linked, tiny response variation in the lithium battery operation process is subdivided, quantitative output of key nodes is achieved by fusing pole piece dynamic characteristics, periodic risk state traceability and abnormal node tracking are supported, and the reliability of the system is improved. According to the invention, the method can achieve the fine structural abnormality marking and multi-stage alarm, improves the timeliness and foresight of structural risk management, can achieve the parallel collection and processing of multi-element response signals, promotes the high-reliability operation and distributed fault management and control of a lithium battery system, expands a safety monitoring boundary, and improves the precision and granularity of active early warning and management.

The invention relates to the technical field of equalization control of a battery management system, and discloses a lithium battery hierarchical equalization control method based on a hierarchical model predictive control algorithm, comprising the following steps: constructing a hierarchical control architecture which comprises a state monitoring layer, an equalization decision layer and an execution control layer, each layer realizes cooperative control through closed-loop data interaction; the state monitoring layer collects multi-dimensional state parameters of the lithium battery system, pre-processes the collected data and then transmits the data to the equalization decision-making layer, the equalization decision-making layer constructs a multi-target optimization model based on a hierarchical MPC algorithm, and the model takes SOC consistency, equalization energy consumption minimization and cycle life maximization of the lithium battery system as optimization targets. The abnormal state of the sensor or the balancing module can be identified in time through a fault diagnosis mechanism, and when a fault occurs, a standby model is automatically switched or a balancing task is shared through an adjacent module, so that the system is ensured not to generate abrupt reduction of balancing performance due to the fault of a single component.

The invention discloses a lithium battery residual life prediction method and system and terminal equipment, and relates to the technical field of lithium battery health management. The method comprises the following steps: receiving a battery capacity attenuation sequence as an original input sequence, detecting and filtering abnormal data by adopting a 3 sigma criterion, and carrying out noise suppression processing on the battery capacity attenuation sequence through a Dropout mask; and carrying out normalization processing on the preprocessed battery capacity attenuation sequence, dividing the battery capacity attenuation sequence into a training set, a verification set and a test set through a sliding window algorithm, and generating a time sequence characteristic matrix and a corresponding residual service life label. According to the method, a neural network structure fusing trend prior perception and dynamic attention regulation is constructed, a multi-scale capacity modeling strategy is introduced to separate a degradation trend, fluctuation disturbance and high-frequency noise, and compared with a traditional time sequence neural network or a single attention model, pseudo fluctuation characteristics caused by capacity regeneration can be more effectively recognized, and the method is more efficient and more reliable. And the judgment accuracy of the model in a complex degradation scene is improved.

The invention discloses a control system for lithium battery power management, and relates to the field of lithium battery control, and the system comprises a voltage input module which is used for externally connecting voltage and supplying power to a battery power supply module and a time delay module; the battery power supply module is used for charging the battery; receiving the battery voltage information, and adjusting the voltage output to the battery according to the battery voltage; the battery module is used for a battery to store electric energy; compared with the prior art, the system has the beneficial effects that the output voltage of the battery power supply module is adjusted through the feedback module, the output voltage of the battery power supply module is relatively large at the beginning, and the charging voltage is reduced along with the increase of the battery voltage, so that a quick charging effect is constructed; whether battery charging is abnormal or not is detected through the time delay module and the charging fault detection module, the battery power supply module is adjusted to perform constant-voltage charging when charging is abnormal, and the battery charging effect is guaranteed.

The invention relates to the technical field of lithium battery safety management, and discloses a lithium battery fault diagnosis method based on a BMS, and the method comprises the steps: obtaining multi-dimensional battery operation data and real-time data, firstly extracting a local feature vector, generating a preliminary fault signal indication, then extracting an abnormal feature vector according to the preliminary fault signal indication, and carrying out the fault diagnosis according to the abnormal feature vector; and if the preset threshold is exceeded, compressing and transmitting to the adjacent management unit to form a shared data packet. According to the local feature vector and the shared data packet, updating a diagnosis model parameter to obtain an optimized fault recognition model for analyzing real-time data and calculating a fault matching degree, and determining a potential fault type if a threshold value is exceeded; and generating a collaborative query request to the distributed network to obtain a historical fault empirical data set, integrating the historical fault empirical data set, refining parameters to obtain an accurate fault probability, activating an alarm and recording a log if a warning threshold is exceeded, and finally updating the global shared knowledge base. According to the method, the problem of insufficient lithium battery fault diagnosis accuracy in a distributed scene is solved, and collaborative optimization of diagnosis accuracy and distributed collaboration is realized.

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 discloses a lithium battery temperature state estimation method based on a physical information neural network, and relates to the technical field of lithium ion battery temperature state estimation, and the method comprises the following steps: obtaining multi-working-condition lithium battery charging and discharging data, and carrying out sliding window denoising and abnormal data elimination preprocessing; a battery thermal model containing total irreversible heat, reversible heat and heat dissipation is constructed by combining heat production and heat dissipation mechanisms, and a temperature change thermodynamic equation is obtained; establishing a physical information neural network, based on a residual network, embedding a time-varying internal resistance module and a time sequence feature extraction module based on the Arrhenius law, constructing a multi-component total loss function, and optimizing parameters through adaptive weight adjustment and an Adam algorithm; temperature state estimation is realized in training and prediction stages, the model is optimized and parameters are stored in the training stage, and a temperature result is output and verified in the prediction stage. The method gives consideration to both physical consistency and data fitting precision, and can support thermal management of the battery.

The invention discloses a lithium battery health state estimation method based on transfer learning, and the method comprises the steps: firstly carrying out the normalization and time sequence reconstruction of a battery charging voltage-capacity curve, and constructing a unified input sequence; extracting long-time-sequence degradation characteristics based on a Mama network, and completing SOH regression prediction through a two-stage full connection layer; in the cross-domain adaptation stage, in combination with an alignment strategy of dynamic time warping and weighted maximum mean difference, time sequence matching and feature distribution alignment in the degradation stage are realized; meanwhile, on the basis of a sample weighting mechanism of a Wasserstein distance, the effectiveness of migrating source domain knowledge to a target domain is improved; through a two-stage strategy of source domain pre-training and source-target joint training, a relatively low prediction error and a relatively high fitting degree can be kept under the condition that a target domain is not labeled; the method shows good generalization ability and robustness under different battery types and different working conditions, and can provide reference for health management of the electric vehicle.

The invention provides a lithium battery health state estimation method based on a long short-term memory network, and relates to the technical field of battery health state estimation. The method comprises the following steps: firstly, carrying out a charge-discharge cycle test on a high-health-degree sample battery, collecting operation and working condition data, extracting indirect and composite health factors including voltage differential, charging time, temperature change rate and the like, and constructing a health factor set; key health factors strongly related to the health state of the battery are screened out by adopting a dynamic double-threshold correlation analysis method, and a time sequence sample set is formed; a multi-output prediction model combining a double-layer gating circulation unit, an attention mechanism and output uncertainty estimation is constructed, and a sparrow search algorithm is introduced to carry out adaptive global optimization on model hyper-parameters. And predicting the health state of the target lithium battery by using the optimized model, and analyzing the deviation between a predicted value and a true value to complete the judgment of the final health state of the target lithium battery.

The invention discloses a multi-modal fusion lithium iron phosphate battery thermal runaway early warning method and system, and the method comprises the steps: collecting multi-source heterogeneous data, such as temperature, voltage, gas concentration and shell strain pressure, in real time through deploying a heterogeneous sensor network, and carrying out the noise reduction and time sequence feature extraction through employing a sub-linear time low-rank approximation algorithm of a Hankel matrix; constructing a cross-modal feature association network by applying a secondary time algorithm of a maximum weight sparse subgraph problem, inputting a fused feature vector into a Bayesian network health degree evaluation model for probabilistic reasoning calculation to obtain a battery health degree score and a thermal runaway risk level, generating a graded early warning signal through multi-level early warning threshold comparison, and performing early warning on the battery health degree score and the thermal runaway risk level. And corresponding prevention and control suggestions are matched. The method solves the technical problems that single physical quantity monitoring is difficult to comprehensively reflect the complex change in the battery and the response delay of a centralized processing architecture causes the early warning lag, and achieves the timely capture and accurate early warning of the early weak characteristics of thermal runaway.

The invention belongs to the technical field of solid-state lithium batteries, and relates to a fluorinated double-salt cross-linked solid-state electrolyte adaptive to a lithium-free negative electrode, a solid-state lithium battery and a preparation method thereof.The preparation method comprises the steps that double-lithium salt is dissolved in a fluorinated solvent to obtain an electrolyte, then a monomer, a cross-linking agent and an initiator are added, the mixture is stirred to be uniform to obtain a precursor solution, and the precursor solution is prepared; and assembling the precursor solution, a positive electrode, a negative electrode and a solid-state electrolyte composite diaphragm into a battery, and performing gradient polymerization to form the solid-state lithium battery. According to the invention, the ionic conductivity of the solid electrolyte is more than 5 * 10 <-4 > S / cm and the oxidation potential of the solid electrolyte is more than 5.8 V by adopting a fluorinated solvent consisting of fluorinated carbonate and fluorinated carboxylate and a double-lithium-salt synergistic system and combining an in-situ interface fusion technology, so that the solid electrolyte can be matched with a 4.8 V high-voltage lithium-rich manganese-based positive electrode and a lithium-free negative electrode; and the energy density and the safety of the solid-state lithium battery are improved. The electrolyte scheme is low in cost, high in safety and good in positive and negative electrode matching performance, can be compatible with an existing battery production line, and is convenient for large-scale production.

The invention discloses a system and method for estimating the charging capacity of an energy storage lithium battery, and particularly relates to the technical field of energy storage battery management, and the method comprises the following steps: collecting dynamic parameters in the charging process of the energy storage lithium battery, and forming a dynamic data set; calculating an environmental disturbance influence index and a charging response consistency factor based on time sequence analysis and feature decoupling; constructing a two-dimensional working condition mapping matrix, identifying a current working condition area and generating a scene label; determining parameter weights of the plurality of estimation models by adopting a probabilistic reasoning mode; according to the scene state, selecting a single model output result or fusing a plurality of model output results for estimation; according to the method, the environment disturbance influence index and the charging response consistency factor are constructed, so that the complex working condition is accurately identified; scene labels are automatically generated based on two-dimensional working condition mapping and a clustering algorithm, and a plurality of estimation models are dynamically selected or fused in combination with model confidence, so that the accuracy, robustness and intelligent level of estimation are improved.

The invention discloses a lithium battery diaphragm production quality tracing method and system based on the Internet of Things, and relates to the field of lithium battery diaphragm production quality tracing, and the method comprises the steps: collecting historical diaphragm production information and real-time diaphragm production information; key nodes of lithium battery diaphragm production are determined, and a unique quality safety traceability code is set for each key node; constructing a quality anomaly knowledge graph in combination with historical diaphragm production information and quality safety traceability codes; inputting the real-time diaphragm production information into the quality anomaly knowledge graph to obtain quality anomaly parameters; carrying out credibility verification on the quality abnormal parameters; determining a quality abnormality cause by using the root cause analysis model, and generating a compensation parameter according to the quality abnormality cause; and performing parameter compensation on the quality anomaly parameter based on the compensation parameter, and performing quality tracing in the quality anomaly knowledge graph according to the compensated quality anomaly parameter. The management efficiency of lithium battery diaphragm production can be effectively improved.

The invention provides a lithium battery internal short circuit fault early warning and positioning method and system based on deep learning. The early warning and positioning method comprises the following steps: step S10, multi-dimensional data semantic acquisition and working condition self-adaptive preprocessing; step S20, collaborative extraction and alignment of cross-scale spatio-temporal features; step S30, performing dynamic threshold adaptive internal short circuit early warning and feedback optimization; s40, fault accurate positioning and verification of topology perception are carried out; and step S50, performing embedded collaborative optimization and self-diagnosis deployment. According to the invention, early weak signals of an internal short circuit fault can be found in time, and the efficiency and timeliness of battery safety monitoring are improved; the sensitivity of early warning is improved; the robustness in high-temperature, low-temperature and rapid charging and discharging environments is enhanced; and the positions of the single battery and the electrode with the fault can be accurately identified.

The invention discloses a hydrogen-lithium dual-power system structure optimization design and integrated control method applied to an unmanned aerial vehicle. The method comprises the following steps: constructing a hydrogen-lithium dual-power system of the unmanned aerial vehicle; setting an integrated control strategy according to the hydrogen-lithium dual-power system; the integrated control strategy comprises an energy management strategy and a life and safety management strategy; the energy management strategy comprises a rule-based strategy, a power following strategy and a self-adaptive optimization strategy; the service life and safety management strategy comprises a hydrogen fuel cell protection strategy, a lithium battery health management strategy, a thermal management strategy, a dual-power coupling safety management strategy and a redundancy and fault processing strategy. By optimizing the power system design and the energy management strategy, the mass of the unmanned aerial vehicle device is reduced, the cruising ability, the load capacity and the environmental adaptability of the unmanned aerial vehicle are improved, and optimization processing of the control strategy is achieved.

The invention discloses a refined disassembly and recovery production line for retired lithium batteries. Comprising an automatic battery feeding station, a battery pretreatment station, a battery discharging station, an electrolyte collecting station, a battery shell disassembling station, a roll core film removing station, a pole piece separating station, a pole piece conveying station, a crushing feeding station, a crushing station, a crushing conveying station, a first-stage separating station, a high-temperature drying station, a drying discharging station and a second-stage separating station. The automatic battery feeding station is connected with the battery pretreatment station, and the battery pretreatment station is connected with the battery discharging station. The refined disassembly and recovery production line for the retired lithium battery is provided with deep discharge equipment, the safety problem of electrified crushing or manual disassembly is avoided, full-process automation is achieved, the recovery efficiency is high, refined disassembly and recovery are achieved, an aluminum shell, a positive plate, a negative plate and a diaphragm are separated, the recovery rate and purity of valuable metal are improved, the production line process is short, and the production line cost is low. And no secondary pollution is generated.

The invention discloses a multi-mode hybrid energy storage cooperative control method and system for a megawatt-level network-building type energy storage power station, and the method comprises the steps: S1, collecting the frequency, voltage amplitude and phase of a power grid, and the charge state and temperature of each energy storage branch, and generating real-time monitoring data; s2, determining an operation mode of the energy storage power station based on a comparison result between the frequency deviation and the voltage deviation in the real-time monitoring data and a preset threshold value; s3, based on the operation mode of the energy storage power station, performing double-layer decomposition on the power instruction through a high-pass filter and a low-pass filter to generate a super-capacitor power instruction and a lithium battery power instruction; s4, generating an internal potential amplitude and a phase angle by adopting a virtual synchronous machine algorithm; and S5, on the basis of the amplitude and the phase angle of the internal potential and the detection result of the power grid connection state, executing switching control of a network construction type control mode or a network following type control mode. The millisecond-second level multi-time scale power optimal distribution is realized; self-adaptive inertia and voltage support is carried out; and island / grid-connected seamless switching is realized.

The invention discloses a wide-temperature-range sodium-lithium hybrid energy storage system and a control method thereof. According to the system, the working temperature range of the energy storage system is effectively expanded through sodium-lithium hybrid energy storage; the sodium ion battery unit bears a low-temperature working condition, and the lithium ion battery unit bears a normal-temperature basic load, so that the low-temperature and power characteristics of a sodium battery and the high-energy density advantage of a lithium battery are exerted, and complementation of energy density and temperature adaptability is realized; and the independent bidirectional DC / DC converter is adopted for power coupling, so that the problem of voltage mismatching possibly caused by direct parallel connection is effectively avoided, and the reliability and the control precision of the system are improved. The system has intelligent environment perception and adaptive control capabilities, can adaptively adjust a working mode and a power distribution strategy according to environment conditions, and is high in intelligent degree. The modular design is adopted, system expansion and maintenance are facilitated, and the operation and maintenance cost is reduced.

The invention discloses a lithium battery health assessment method based on an adaptive extended particle filter algorithm. The method comprises the following steps: constructing a second-order RC equivalent circuit model of a lithium battery; determining all parameters in the lithium battery second-order RC equivalent circuit model by using a recursive least square algorithm with a forgetting factor to obtain a determined lithium battery second-order RC equivalent circuit; estimating SOC parameters of the lithium battery at the moment by combining a particle filtering algorithm with an adaptive extended Kalman filtering algorithm; correcting the terminal voltage of the second-order RC equivalent circuit of the lithium battery at the moment to obtain the terminal voltage of the equivalent circuit; and based on the lithium battery second-order RC equivalent circuit and the corrected terminal voltage, estimating the SOE parameter of the lithium battery at the moment k by using a particle filtering algorithm in combination with an extended Kalman filtering algorithm. The SOC and the SOE of the lithium battery are accurately estimated, and the sampling efficiency and the estimation stability of particle filtering are improved. Cooperative joint estimation of SOC and SOE complements each other, and the accuracy of overall state estimation is further improved.

The invention discloses a dynamic thermal management and risk pre-judgment system for a lithium battery energy storage system, and relates to the technical field of lithium battery energy storage, and the system comprises a thermal field monitoring module which generates a real-time three-dimensional thermal field distribution map of a battery pack; the dynamic regulation and control module is used for self-adaptively regulating the heat dissipation power and the refrigerant flow in combination with the charging and discharging states of the battery and the environmental parameters; the risk assessment module is used for constructing a thermal runaway risk grade assessment model and generating a risk probability value; the early warning response module triggers a multi-stage early warning mechanism according to the risk probability value, and is linked with the dynamic regulation and control module to execute an emergency heat dissipation strategy to generate a risk disposal scheme; and the data archiving module is used for storing data. According to the invention, the whole-process intelligent design of monitoring, regulation and control, evaluation, early warning and archiving is provided, the thermal management precision, the risk prevention and control capability and the operation economy of the lithium battery energy storage system are comprehensively improved, and a key technical guarantee is provided for large-scale energy storage application.

The invention relates to the technical field of battery management, in particular to an intelligent monitoring control method and system for a lithium battery BMS. According to the application, through the multi-mode sensing array integrated on the surface of the inactive correlation structure of the single cell, the limitation of traditional voltage and current monitoring is broken through, and microcosmic parameter signals such as high-frequency impedance, microstrain and characteristic steam spectrum are synchronously acquired; an empirical mode decomposition algorithm is innovatively adopted to separate electrochemical and mechanical process intrinsic modes, and fusion with spectral signal time domain features is carried out, so that deep fusion of multi-physical field information is realized; a channel coupling deep learning network is constructed, a process fingerprint spectrum is constructed through a dynamic updating algorithm, and full-life-cycle state tracking is achieved; based on failure mode recognition of dynamic time warping distance and similarity matching, a target control strategy is generated in combination with multi-physics coupling simulation; according to the application, multi-dimensional microscopic parameter fusion monitoring is realized, and the fault early warning precision and the battery safety protection capability are remarkably improved.

The invention provides a ship shaft power generation and lithium battery coordinated multi-working-condition energy optimization management system, which is applied to the technical field of ship power systems and energy management and comprises a data acquisition module, an energy optimization processing module and a power regulation and control module, wherein the energy optimization processing module is provided with a prediction unit, a working condition identification unit, a loss evaluation unit and a decision-making unit, the prediction unit is used for predicting a ship load demand in a preset period, the working condition identification unit is used for identifying a ship operation working condition, and the loss evaluation unit is used for determining a life loss factor by combining a prediction result and an energy storage unit state; the decision-making unit generates a regulation and control instruction based on the life loss factor and the state of the power generation unit to realize power distribution of the power generation unit and the energy storage unit; according to the system, multi-working-condition energy consumption requirements of the ship are met, meanwhile, comprehensive optimization of fuel consumption cost and energy storage unit service life loss cost is achieved, the energy utilization rate is increased, the service life of the energy storage unit is prolonged, and the full-life-cycle operation cost of the ship is reduced.

The invention provides a coordination control method and device of a photovoltaic energy storage inverter for automobile charging based on artificial intelligence, and belongs to the technical field of automobile photovoltaic energy storage coordination control, and the method comprises the steps: monitoring the output power of a photovoltaic array, the state of charge (SOC) of an energy storage system, the state of a power grid and the power demand of a load in real time; a reinforcement learning algorithm is introduced, weather forecast and historical load data are combined, photovoltaic output and load demands in a future time period are predicted, and a power distribution instruction is generated; deciding a current operation mode and generating a mode switching instruction; seamless switching between grid connection and grid disconnection is controlled, and after switching is completed, the charging and discharging proportion of the lithium battery and the super capacitor is coordinated; integrating the energy storage real-time state and the power distribution instruction, and optimizing and adjusting the control parameters of the inverter in real time; continuously monitoring a running state and a switching process, and starting a standby mode when a fault is detected; and the data is uploaded to a monitoring platform. The system operation is efficiently optimized, the service life is prolonged, and stability is guaranteed.

The invention discloses a hybrid energy storage capacity configuration method based on an energy storage characteristic and instruction difference dynamic matching strategy and a related device, and relates to the technical field of power system energy storage, and the method comprises the steps: carrying out the mode division according to the amplitude of a total power instruction signal of a hybrid energy storage system, and carrying out the configuration of the hybrid energy storage capacity based on the dynamic matching strategy; distributing the total power instruction signal to a super capacitor and a lithium battery, and calculating the charge states of the super capacitor and the lithium battery; constructing a control configuration integrated model by taking the total power command signal as input and the charge state as feedback; determining value range constraints of the configuration parameters and the control parameters; and under constraint conditions, solving the comprehensive multi-objective optimization function of the control and configuration integrated model by adopting a traversal algorithm to obtain an optimal configuration parameter and control parameter combination, and determining a hybrid energy storage capacity configuration scheme of the hybrid energy storage system. The problems of insufficient energy storage utilization and insufficient frequency modulation precision in a traditional method can be solved.

The invention relates to the field of lithium battery intelligent monitoring, in particular to a low-temperature-resistant lithium battery BMS monitoring method and system based on cooperative sensing. The method comprises the following steps: acquiring surface temperature distribution, environment temperature and electrochemical parameters of the low-temperature tolerant lithium battery, and constructing a multi-source state characteristic matrix through space-time alignment and data fusion; applying a composite current excitation signal, and combining impedance analysis and spectrum analysis to extract impedance parameters, nonlinear distortion components and electrochemical stress factors to form a multi-dimensional feature vector; obtaining a state monitoring result by utilizing decision level fusion; through multi-sensor fusion and multi-dimensional feature fusion, the special internal state of the low-temperature tolerant lithium battery is comprehensively represented, early risk identification and early warning are carried out aiming at unique electrochemical behavior features of the low-temperature tolerant lithium battery, and compared with a common lithium battery monitoring method, the method has the advantages that the safety is high; and the accuracy, the robustness and the real-time performance of monitoring the state of the low-temperature tolerant lithium battery in the low-temperature environment are remarkably improved.

The invention discloses a lithium battery thermal runaway risk dynamic regulation and control method and system, and the method comprises the steps: collecting temperature data in a new energy automobile battery module, and constructing a temperature field matrix; judging whether the lithium battery has a temperature risk based on the temperature field matrix; when the temperature risk exists in the lithium battery, risk grades are divided; and based on the divided risk levels, dynamically regulating and controlling the thermal runaway risk of the lithium battery. The differential cooling parameters are determined according to the risk level and the hot spot position, and the control strategy is optimized in real time through feedback data, so that the flow velocity adjustment and heat dissipation power distribution of the liquid cooling system are more scientific and effective, the overall performance of battery thermal management is improved, the risk of thermal runaway of the lithium battery is effectively reduced, and the service life of the lithium battery is prolonged. The method has wide application prospects in the fields of new energy automobiles and the like, and stable operation of the system and personnel safety guarantee can be remarkably enhanced.

The invention provides an intelligent fire prevention and control fire-fighting management system and method for a lithium battery energy storage power station, and relates to the technical field of data processing, and the method comprises the steps: arranging a multi-level temperature sensor, a gas detector and a smoke detector in and outside a battery module, and collecting and forming multi-source monitoring data; performing time sequence rate analysis, and if a preset condition is met, generating early warning data; in combination with sensor matrix distribution in the battery module, spatial cross comparison is executed to obtain abnormal positioning data, and false anomalies are eliminated through multi-point consistency verification; generating a directional processing instruction corresponding to the injection unit, and setting injection data in the processing instruction; the spraying unit is controlled to release an atomized fire extinguishing medium, so that the medium forms a permeation flow field in the gaps of the battery cells, and treatment feedback data is acquired and formed; performing trend analysis, and if the trend is not enough to recover to the preset safety range, generating a secondary processing instruction; according to the invention, the autonomy and accuracy of fire control management are improved.

The invention relates to the technical field of lithium batteries, in particular to a safe sealing combined structural member of a cylindrical battery, which comprises a shell, an inner separator and a sealing ring, the middle part of one end of the shell is riveted with a pole, the inner separator is attached to the inner wall of the shell, the part of the inner separator close to the pole is pressed by the pole and then is filled between the turnup and the straight section of the pole, and the sealing ring is arranged in the shell. The part, far away from the straight section, of the turned edge of the pole gradually inclines towards the inner wall of the shell so as to form a non-flat riveting lower buckling structure on the inner separator, and meanwhile, an R-angle structure is pre-formed on the bent part, matched between the turned edge of the pole and the straight section, of the inner separator, so that extrusion between the turned edge of the pole and the inner separator in the riveting process is reduced; and the part, close to the pole, of the sealing ring is pressed by the pole, is filled between the pole ring and the straight section of the pole, and is in contact fit with the inner separator. The technical problem that in the prior art, after a safety sealing combined structural part of a cylindrical battery of a lithium battery is riveted, electrolyte can permeate into a sealing ring, and performance hidden danger is caused is solved.