24883 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 invention discloses an electric bicycle lithium battery data real-time monitoring and early warning system and method, and relates to the technical field of battery management, and the system comprises a multi-dimensional data collection module which is used for obtaining a multi-source heterogeneous data set of a lithium battery system; the collaborative feature extraction module is used for generating a comprehensive evaluation parameter set; the dynamic threshold generation module is used for constructing a self-adaptive early warning boundary model according to the comprehensive evaluation parameter set; the intelligent decision module is used for generating a hierarchical control instruction set based on a multi-objective optimization algorithm; and the cloud collaboration module is used for synchronizing the hierarchical control instruction set to the edge computing node and the cloud management platform, and triggering a multi-level linkage protection mechanism based on the game theory when the thermal runaway risk index is detected to exceed a first dynamic threshold value. According to the electric bicycle lithium battery data real-time monitoring and early warning system and method provided by the invention, the safety and reliability of a battery system are improved.

The invention relates to a lithium battery energy storage system fire-fighting ventilation and explosion venting safety assessment method based on multi-dimensional simulation. The method comprises the following steps: constructing an energy storage system digital twinborn model fusing structure parameters, material attributes and environmental parameters; generating a multi-mode failure scene set covering multiple temperature domains and aging states through mode recognition; simulating and quantifying dynamic interaction of a temperature field, a flow field and a stress field in the thermal runaway evolution process based on thermal-fluid-solid multi-physics field coupling; constructing a space-time associated dynamic safety evaluation matrix, and combining fuzzy comprehensive evaluation and Monte Carlo sampling to generate risk quantitative indexes; and iteratively correcting parameters of the fire-fighting ventilation and explosion venting system through a multi-objective optimization algorithm to form a graded safety assessment conclusion. According to the method, the technical bottlenecks of environmental parameter splitting and single failure scene in a traditional method are broken through, the thermal runaway suppression efficiency is improved, the combustible gas concentration control error is reduced, and collaborative optimization of explosion venting pressure fluctuation suppression and ventilation response is realized through a closed-loop evaluation mechanism.

The invention discloses a lithium battery health state prediction method and system based on multi-parameter fusion, and relates to the technical field of lithium batteries, and the method comprises the following steps: collecting multi-dimensional parameter data in a lithium battery operation process, preprocessing the multi-dimensional parameter data, and constructing a time sequence data set; extracting multi-source degradation characteristics from the time sequence data set, and generating degradation influence factors; building a hybrid model based on an LSTM network and a Transform encoder, inputting the degradation impact factors into the hybrid model for model training until the model converges, and obtaining a lithium battery health state prediction model; and performing health detection on a to-be-detected lithium battery based on the lithium battery health state prediction model, and outputting an SOH prediction value. Multi-source battery parameters can be fused to predict the health state of the lithium battery, and the prediction precision and the real-time performance are improved.

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

The invention relates to the field of lithium battery negative electrode materials, and particularly discloses a silicon-carbon composite negative electrode material for a lithium ion battery and a preparation method of the silicon-carbon composite negative electrode material. The method comprises the following steps: preparing a porous carbon substrate, and depositing silicon nanoparticles in pores and on the surface of the porous carbon substrate by adopting a fluidized bed chemical vapor deposition process and taking silane as a silicon source to obtain a silicon-carbon core material; continuously carrying out carbon coating on the surface of the silicon-carbon core material in the fluidized bed by taking acetylene as a carbon source to form a carbon coating layer; and leading out the obtained material from the fluidized bed, taking acetylene as a carbon source, and carrying out carbon coating on the surface of the primary carbon coating layer again by adopting the rotary furnace to form a secondary carbon coating layer. According to the preparation method, a porous carbon matrix and double-layer functional carbon coating process is adopted, so that the volume expansion of silicon in the circulation process is remarkably inhibited, the interface side reaction is reduced, and the circulation stability of the material is effectively improved.

The invention discloses a lithium battery health state assessment method and system, and particularly relates to the technical field of battery health state assessment. The method comprises the following steps: performing time sequence alignment and structured preprocessing on multi-source operation data of a target lithium battery in a plurality of historical work cycles to construct a structured data set; constructing a spatial-temporal characteristic residual error map based on residual error mapping analysis, and extracting a spatial heterogeneity index; in combination with a spatial heterogeneity index, generating regional degradation feature mapping; through high-dimensional feature embedding and evolution path clustering, a heterogeneous aging mode is identified, and a classification result is generated; evaluating the health state grade of the target lithium battery according to the regional degradation characteristic mapping and heterogeneous aging mode classification result; whether the battery has a local potential thermal runaway risk or not is judged based on the evaluation result, and a corresponding risk early warning signal and a safety disposal suggestion are generated, so that the nonlinear influence of the lithium battery aging heterogeneity can be accurately identified, and the health state evaluation precision and the safety risk early warning capability are effectively improved.

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

The invention discloses a multi-level feature fusion and time sequence modeling combined lithium battery life prediction method, and relates to the technical field of lithium ion battery health management and life prediction. The method comprises the steps of receiving time sequence observation data in a battery operation process, inputting the time sequence observation data into a pre-constructed local feature extraction model, and introducing a one-dimensional convolutional neural network into the local feature extraction model to perform feature extraction on the time sequence observation data to obtain local feature representation. According to the method, three structures of local feature extraction, global context modeling and bidirectional time sequence modeling are fused, and the battery degradation modeling capability and prediction precision are effectively improved. The TFN adopts an end-to-end architecture design, has good feature perception capability and time-dependent modeling capability, and can adapt to various degradation modes and complex time sequence environments. The method is suitable for life evaluation and health state monitoring in an intelligent battery management system, and has relatively high practical value and popularization prospect.

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 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 solid-state lithium batteries, in particular to a cellulose acetate modified PEO-based solid-state electrolyte membrane, a solid-state lithium battery and a preparation method thereof.The preparation method comprises the steps that lithium bis (trifluoromethanesulfonyl) imide, cellulose acetate and polyethylene oxide are dissolved in a solvent, and a mixed solution is obtained; and curing the mixed solution in a mold to obtain the cellulose acetate modified PEO-based solid electrolyte membrane. According to the invention, the cellulose acetate is introduced to modify the polyethylene oxide solid electrolyte, and the prepared cellulose acetate modified PEO-based solid electrolyte membrane can generate a stable solid electrolyte interface rich in Li2O and LiF on the surface of a lithium negative electrode in situ in the application of a solid lithium battery, so that the growth ability of lithium dendrites is effectively inhibited, and the performance of the lithium battery is improved. The interface impedance is reduced, and the operating temperature range of the solid-state lithium battery is widened.

The invention belongs to the technical field of SOC and SOH estimation, and particularly relates to a park lithium battery SOC and SOH joint estimation method and device, and the method comprises the steps: collecting the multi-dimensional feature data of a lithium battery in the charging and discharging process, extracting a health factor, and carrying out the preprocessing of the data; constructing a CNN-BiLSTM model, combining the CNN-BiLSTM model with a LightGBM model, constructing a joint estimation model, and training the joint estimation model; and inputting the health factor into a CNN-BiLSTM model to estimate the SOH, and inputting the multi-dimensional feature data and an estimated value of the SOH into a LightGBM model to estimate the SOC, thereby obtaining a final SOC and SOH joint estimated value. According to the method, the time sequence features of the SOH are modeled through the BiLSTM and the CNN, and SOC estimation is performed in combination with the LightGBM, so that SOC calculation can dynamically consider the change of the SOH, and accumulation of SOC estimation errors is reduced.

The invention relates to the technical field of lithium battery management, and discloses a cloud-based lithium battery management method and system, and the method comprises the steps: constructing a cloud collaborative management framework, and obtaining a dynamic adjustment factor and a degradation compensation coefficient; performing multi-dimensional state joint estimation, and predicting the health state and the residual life of the battery; a dynamic balance control strategy is set, and energy differences among the monomers are compensated; and closed-loop feedback and model iteration are carried out. The system comprises a distributed data acquisition unit, an edge computing unit, a cloud analysis platform, a communication gateway and a balance execution unit. According to the method, technologies such as cloud collaborative management, multi-dimensional data processing, dynamic balance control and model iterative optimization are utilized, accurate management of the lithium battery is realized, the battery state prediction accuracy can be improved, the service life of the battery can be prolonged, the system safety and stability can be enhanced, and the method is suitable for various lithium battery application scenes.

The invention discloses a lithium battery charge state estimation method based on a Bayes-TLCO optimization deep neural network, and belongs to the technical field of battery state monitoring. The method comprises the following steps: firstly, preprocessing a lithium battery charging and discharging data set; then, constructing a deep neural network model comprising a convolutional neural network, a long-short-term memory network and a multi-head attention mechanism, dynamically optimizing hyper-parameters of the model by using a Bayesian optimization-assisted termite life cycle optimization algorithm, introducing Bayesian optimization during iteration stagnation in a TLCO algorithm iteration process, and finally obtaining a termite life cycle optimization model; fitting historical data through a Gaussian process to construct a search empirical model, generating high-value sampling points, and accelerating model hyper-parameter convergence to a globally optimal solution; and finally, estimating the state of charge of the lithium battery. The method breaks through the limitation of a single algorithm, achieves the high-precision estimation of the state of charge of the lithium battery under a complex working condition, effectively improves the model training efficiency, is suitable for electric vehicles, energy storage systems and other scenes, and provides a key technical support for the intelligent upgrading of battery management.

The invention discloses a lithium iron phosphate battery pack SOE dynamic prediction and life evaluation method and system fused with a P2D model, and relates to the technical field of battery management, and the method comprises the steps: collecting operation data, building a multi-physical field coupled P2D model, carrying out the SOE dynamic prediction based on the P2D model, building a life evaluation model in combination with a performance attenuation mechanism, and optimizing an operation strategy. According to the method, on the basis of historical operation data and real-time state parameters, an SOE dynamic prediction result of the battery pack is generated by using an electrochemical-thermal-mechanical multi-physics field coupled P2D model; and analyzing the prediction result in combination with a long-term performance attenuation mechanism, evaluating the residual life of the battery pack, and outputting an evaluation result. The SOE prediction precision is improved, the performance change rule is comprehensively reflected by using the life evaluation model, and the operation efficiency is remarkably improved and the service life is prolonged through real-time monitoring and optimization. According to the invention, efficient and reliable battery management requirements under complex working conditions can be met.

The invention provides a lithium battery internal short circuit fault intelligent detection method based on electrochemical impedance spectroscopy. According to the system, battery data are obtained through electrochemical impedance spectroscopy testing, and innovative feature extraction is carried out. The core technology is to extract four key features from a Nyquist graph: a minimum value point of an intermediate frequency region, a straight slope of a low frequency region, a radius of a circle of a high frequency region and an intersection point of the high frequency region and a real part axis. The characteristics effectively characterize the short circuit state in the battery. And the system inputs the extracted features into a long short-term memory (LSTM) network deep learning model to realize accurate identification and classification of the internal short circuit fault. The model is deployed in a cloud after being trained, and monitors the battery state in real time and performs graded early warning. According to the invention, the accuracy and efficiency of short-circuit fault detection in the lithium ion battery are obviously improved, and powerful support is provided for battery safety management of an electric vehicle and an energy storage system.

The invention provides a lithium battery equalization thermal management method. Real-time temperature data is obtained by constructing a thermal distribution monitoring network, the temperature difference between a center and an edge area is analyzed, a thermal load distribution coefficient is calculated, and a dynamic heat dissipation intensity adjustment model is established; driving the multi-channel cooling system to operate according to the heat dissipation demand priority, and adjusting the flow distribution proportion of a cooling medium; a dynamic nonlinear feedback correction mechanism is adopted, secondary adjustment is carried out for uneven temperature distribution, and an optimal heat dissipation parameter combination is determined in combination with historical data. Uniformity control over temperature distribution of the battery module is achieved, the problem of heat accumulation in a center area is effectively solved, the heat dissipation efficiency under the rapid charging working condition is improved, and the safety performance and the service life of the battery module are guaranteed.

The invention discloses a series lithium battery pack power supply equalization management method. The method comprises the following steps: acquiring an original data set of a series lithium battery pack; calculating a voltage change rate, a temperature sudden rise rate and an internal resistance fluctuation amplitude, and obtaining a dynamic feature set of battery operation; when the dynamic feature set exceeds a corresponding preset threshold value, obtaining a short-circuit risk assessment result; matching a pre-established battery pack topological structure diagram with the short-circuit risk assessment result, determining a target battery identifier needing to be isolated, generating an isolation instruction by adopting a relay control algorithm, and completing the isolation of the short-circuit battery; adjusting the charging current distribution of the updated battery pack through an equalization management algorithm to obtain equalized battery operation parameters; and if the voltage, temperature and internal resistance data of the residual single batteries are all in a normal range, determining that the system stability is recovered, and obtaining a final running state of the battery pack. The short-circuit risk of the lithium battery pack can be effectively prevented, and intelligent management and safety improvement of the battery pack are realized.

The invention relates to a lithium battery health degree detection method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the lithium battery health degree detection method comprises the steps: evaluating vehicle basic features according to vehicle source data, and obtaining a battery feature data table; taking the charging state of the battery as a research object, analyzing a charging segment, determining a sampling time threshold value, and judging data missing and interpolation filling according to the residual charge of the battery; calculating the battery capacity based on the charge charging time, and analyzing the battery capacity transversely and longitudinally to obtain an initial capacity value; extracting effective capacity data in all the initial capacity values according to a preset travel constraint condition; training a single-pack health degree evaluation matrix or a double-pack health degree evaluation matrix, predicting a charging fragment sequence, and constructing a battery health degree evaluation model; the single-pack health degree evaluation matrix or the double-pack health degree evaluation matrix is corrected, an optimal health degree evaluation weight matrix is obtained, and lithium battery health degree detection is completed in combination with a physical compensation mechanism; according to the method, upward fluctuation of the SOH is avoided, and the SOH estimation precision and the model adaptability are improved.

The invention relates to a lithium battery electrochemical impedance spectroscopy online measurement and estimation method and system based on a charging pile, and belongs to a battery detection technology. The system comprises a measurement module, an estimation module and a result output module. The measurement module comprises a signal generation sub-module, a waveform amplification sub-module and a response processing sub-module; a pulse control signal is inserted into the signal generation sub-module during charging, and is amplified into a measurement excitation signal by the waveform amplification sub-module to be applied to a battery; the response processing sub-module samples the response signal and calculates an impedance spectrum. The estimation module comprises a neural network sub-module and an impedance spectrum estimation sub-module; the neural network sub-module is used for training a physical information neural network by using impedance response to output Randles circuit model parameters to the estimation sub-module, and the change of the electrochemical impedance spectrum is predicted. And the result output module displays and stores the measured and estimated values of the impedance spectrum. According to the invention, the impedance values of the to-be-measured battery pack under multiple frequencies can be measured and estimated on line, and the estimation of the impedance spectrum is physically interpretable.

The invention relates to the technical field of new energy power systems, and discloses an energy storage coordination system and control method based on meteorological prediction and multi-energy complementation, and the system comprises a power generation end module which carries out the dynamic adjustment according to a power adjustment instruction of a power generation end; the meteorological prediction and data acquisition module is used for acquiring meteorological data in a future time period in real time and sending the meteorological data to the intelligent control and scheduling platform; the energy storage end module sends data including lithium battery SOC and hydrogen storage tank pressure to the intelligent control and scheduling platform and performs charging and discharging according to an energy storage end charging and discharging priority strategy; and the intelligent control and scheduling platform receives the electricity price data of the weather prediction and data acquisition module, the energy storage end module and the power grid in real time, generates a power generation end power regulation instruction and an energy storage end charging and discharging priority strategy, and meanwhile, improves the income and maximizes the hydrogen energy use proportion through electricity price peak-valley arbitrage and green electricity transaction premium. The power grid stability and the energy utilization rate are greatly improved, the energy storage life is prolonged, and the investment payback period is shortened.

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 provides a method for preferentially extracting lithium from battery black powder, and relates to the field of battery recovery. The method comprises the following steps: mixing battery black powder with water and acid liquor, carrying out lithium extraction reaction on the battery black powder under the conditions of pressurization and heating to obtain reacted slurry, and carrying out solid-liquid separation to obtain a lithium-rich solution and residues; the battery black powder comprises at least one of waste ternary lithium ion battery positive electrode powder, waste ternary lithium ion battery positive and negative electrode mixed powder and waste lithium iron phosphate battery black powder. According to the method for preferentially extracting the lithium from the battery black powder, operation steps are simplified, energy consumption is reduced, efficient and preferential recovery of the lithium is achieved, the recovery rate of the lithium and the separation efficiency of the lithium and nickel / cobalt / manganese are remarkably improved, the lithium leaching rate can reach 96.5% or above, and the nickel-cobalt-manganese leaching rate is lt; and 0.5%.

The invention provides an efficient lithium battery life prediction system based on machine learning, and relates to the technical field of safety monitoring and fault diagnosis of a power system. The prediction main system comprises a data acquisition and preprocessing module, a machine learning model construction module, a model training and optimization module, a life prediction module, a result evaluation and feedback module and a data storage and management module. By means of an advanced algorithm and accurate data preprocessing, limitation of a traditional empirical model is broken, reliable battery remaining life guidance is provided for equipment in different application scenes, operation stability and use planning rationality are improved, and meanwhile, by means of a pre-training model fine tuning technology and a scene specific optimization strategy, the service life of the battery is optimized. And the prediction model adaptive to each scene is quickly customized, an accurate result is output, and the universality and the practicability are enhanced.

The invention discloses an energy storage power station fire early warning method and system based on multi-parameter fusion, and the method comprises the following steps: collecting the temperature, characteristic gas concentration, cell expansion force, voltage fluctuation and environment temperature and humidity data of a lithium battery of an energy storage power station in real time through a distributed sensor, and carrying out the cleaning, denoising and standardization processing of the collected parameters, temperature and gas concentration monitoring values are corrected through an environment temperature and humidity compensation algorithm, abnormal data caused by environment interference are eliminated, the preprocessed data are input into a preset intelligent early warning module, and the model is based on a random forest algorithm. Through multi-parameter fusion and intelligent algorithm deep analysis, in combination with environment compensation, interference elimination, early warning accuracy improvement, graded early warning and linkage response, full-stage accurate disposal is achieved, timeliness is enhanced, sensor redundancy, multi-cabin cooperation and other mechanisms guarantee reliability, a closed loop from monitoring to disposal is formed, and the fire risk and loss are effectively reduced.

The invention discloses a ternary internet-of-things battery management system, particularly relates to the technical field of lithium battery management, realizes lithium battery thermal safety closed-loop management through multi-sensor fusion, and comprises the following steps: deploying a digital sensor array to obtain time-space associated data; constructing a spatio-temporal feature vector including fusion of a local temperature gradient, a neighborhood temperature gradient and a stress coupling ratio; a density clustering algorithm is adopted to dynamically divide spontaneous heating, radiant heat and stress coupling clusters, and cluster boundaries are updated in real time; calculating a dual thermal coupling coefficient based on a clustering result, and eliminating the interference of temperature reading through a dual temperature compensation formula; a three-level alarm mechanism of a radiant heat proportion, a stress coupling degree and a compensation error is established, a side gateway is linked to realize parameter adaptive optimization, and the thermal runaway early warning precision and the structural failure detection rate are improved; strain monitoring resource optimization is realized through physically-driven key point screening and dynamic graph neural network prediction.

The invention provides a preparation method of a porous carbon-silicon composite negative electrode material of a lithium battery. The preparation method comprises the following steps: constructing a multi-stage template system; carrying out carbon precursor impregnation and gradient temperature carbonization on the multistage template system; removing the template to form a gradient porous carbon carrier; carrying out gradient temperature zone silane deposition; and plasma-assisted carbon coating and nitrogen doping are carried out. According to the method, a silicon dioxide sphere hard template is combined with cetyltrimethylammonium bromide and a block copolymer soft template to form a porous carbon carrier with a micropore-mesopore-macropore three-stage structure, and gradient distribution of silicon in porous carbon is realized by utilizing a three-temperature-zone fluidized bed reactor and a pulse type deposition technology, so that the silicon-based porous carbon composite material is obtained. The problem of volume expansion of the silicon material is effectively relieved and the material cycling stability is improved. The porous carbon-silicon composite negative electrode material prepared by the invention has high specific surface area, excellent ion transmission channel and good structural stability, and can be applied to a high-energy-density lithium ion battery.

The invention relates to the technical field of lithium battery safety management, and particularly discloses a lithium battery thermal runaway early warning and protection method based on an adaptive neural network, which comprises the following steps: acquiring temperature, internal resistance and voltage data of a lithium battery in real time, extracting multi-dimensional features based on discrete wavelet transform, singular value decomposition and principal component analysis algorithms, and performing early warning and protection on the lithium battery thermal runaway. The influence of internal resistance and voltage on abnormal temperature fluctuation is quantified; constructing a feature vector through the comprehensive influence coefficient, inputting the feature vector into an adaptive fuzzy neural network prediction model, outputting a temperature anomaly fluctuation coefficient, and accurately predicting the thermal runaway risk of the lithium battery; according to the lithium battery thermal runaway prediction method, multi-level protection measures such as early warning response triggering, system automatic charging and discharging strategy adjusting, power output reducing and heat dissipation system starting are taken based on the prediction result, development of the thermal runaway risk is restrained, multi-dimensional data fusion and an intelligent algorithm are combined, and the lithium battery thermal runaway prediction accuracy and the risk control real-time performance are improved.

The invention belongs to the field of lithium battery residual life prediction, and discloses a lithium battery residual life real-time prediction method based on dynamic uncertainty modeling, and the method comprises the steps: cooperatively collecting the full life cycle data of a lithium battery through multiple sensors, constructing a dynamic health score in combination with a convolutional neural network, and effectively capturing the non-stationary and non-linear characteristics of the data. The uncertainty of health scoring is quantified by further adopting a non-stationary random process, and related parameters are synchronously adjusted through joint optimization of an objective function to enhance the adaptability of the model to a complex degradation mode. And dynamically updating non-stationary random process parameters based on a Bayesian reasoning framework, and combining conjugate prior distribution of historical data and real-time observation values to realize parameter adaptive adjustment and dynamic prediction of the residual life of the lithium battery. According to the method, the defect that a traditional data driving method lacks physical interpretability is overcome, and the problem that precision is insufficient due to the fact that a battery degradation mechanism is complex in a single model driving method is solved.