9736 results about "Lithium electrode" 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 a battery safety risk grade early warning and evaluation method, and relates to the technical field of lithium ion batteries, and the method comprises the steps: collecting battery operation data, carrying out the processing to obtain a capacity attenuation characteristic and an internal resistance growth characteristic, and obtaining a degradation characteristic parameter related to the temperature through an Arrhenius model; estimating capacity loss trends at different temperatures and time; inputting the multi-source time sequence data into a long short-term memory network, and predicting the capacity and internal resistance change of a plurality of cycles in the future; and in combination with a life end criterion, calculating the remaining service life, fusing the remaining service life with the degradation index of the aging model and the degradation index of the capacity model, generating a safety state index, and outputting a battery safety risk grade and corresponding early warning information. According to the invention, overall safety risk assessment can be provided for the whole battery system, accurate monitoring and early warning can be carried out on the single batteries or local modules, and the safety and reliability of battery operation can be improved.

The invention relates to a model predictive control charging optimization method based on a dynamic power state, which initiates a'dynamic power state collaborative optimization 'mechanism, takes a real-time power upper limit as an active optimization target instead of a fixed constraint condition, and breaks through the technical bottleneck of power limitation passive response in a traditional charging strategy. The method specifically comprises the following steps: constructing an electric-thermal-aging multi-physics field coupling model of the lithium ion battery, updating electric-thermal characteristic parameters in real time through an online parameter identification algorithm, and synchronously estimating a core temperature and an aging state in combination with a double-Kalman filtering state observer; innovatively establishing a four-dimensional objective function optimization model containing a dynamic power state, and performing multi-objective collaborative optimization on a power upper limit, a charging speed, a capacity fading rate and a current fluctuation rate; and designing a dynamic rolling optimization algorithm based on a model prediction control framework, and solving the optimal charging current meeting the dynamic power distribution requirement of the power grid in real time under the hard constraint of ensuring the maximum core temperature and terminal voltage.

The invention discloses a lithium ion battery internal short circuit fault detection method based on time-frequency fusion, and the method comprises the steps: firstly constructing a fractional order equivalent circuit model fused with an electrochemical aging mechanism, and simulating the paths of conductivity reduction, active material loss and lithium inventory reduction in combination with an aging empirical formula; joint modeling of the aging process and the random internal short circuit is achieved, and multi-cycle voltage and current and electrochemical impedance spectroscopy data are obtained; secondly, extracting time domain features by using a time domain attention enhanced long-short-term memory network, and analyzing frequency domain information by using a multi-scale frequency sensing convolutional neural network; and then weighting and screening two types of modal features through a dynamic gating fusion module, introducing a cross attention mechanism to establish dependency mapping between time-frequency domain features, and finally outputting an internal short circuit fault detection result. The method can solve the problems that in the lithium ion battery aging process, due to lithium dendrite growth, the internal short circuit early fault is high in concealment, and single time-frequency characteristics are difficult to detect, and early high-precision recognition of the internal short circuit fault can be achieved.

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.

A lithium ion battery thermal management control method based on joint state estimation comprises the following steps: establishing a lithium ion battery second-order RC equivalent electric model, and performing electric model parameter identification by adopting an improved robust adaptive multi-forgetting factor recursive least square method; establishing a dual-state lumped parameter thermal model of the lithium ion battery, and performing thermal model parameter identification by adopting an improved goat optimization algorithm; constructing an electrothermal coupling model based on the identified electrical model and the parameters of the dual-state lumped parameter thermal model, then constructing a minimum entropy adaptive Kalman filter based on a near-end strategy optimization algorithm, and then carrying out joint estimation on the SOC and SOT of the battery; and based on the joint estimation result, constructing a deep reinforcement learning optimized adaptive model prediction control algorithm, and performing thermal management control on the lithium ion battery through the deep reinforcement learning optimized adaptive model prediction control algorithm. According to the invention, more accurate and effective thermal management control of the lithium ion battery can be realized.

The invention provides a diaphragm and a preparation method thereof and a lithium ion battery, the diaphragm comprises a porous support layer and a gradient functional layer, the porous support layer comprises a base membrane with the porosity of 45-60% and active groups modified on the surface of the base membrane, and the gradient functional layer comprises a heat conduction strengthening layer and an ion channel layer, the heat conduction strengthening layer comprises a first polymer and a modified boron nitride nanosheet, and the ion channel layer comprises a second polymer and a solid electrolyte nanowire. The porous supporting layer and the gradient functional layer are overlapped to form the composite diaphragm, so that high thermal stability, high wettability and high ion conductivity are synergistically realized, and formation of lithium dendrites can be inhibited.

The invention relates to a lithium ion battery life prediction method, and belongs to the field of battery life prediction and intelligent maintenance. The method comprises the steps that S1, a battery capacity degradation sequence is collected, and integrity is checked and normalized; s2, decomposing the sequence by using an improved complete set empirical mode decomposition algorithm, and dividing the sequence into a high-frequency component and a low-frequency component according to a zero-crossing rate; s3, modeling the high-frequency component: fusing multi-scale channel interactive attention, a time sequence convolutional network and a hybrid expert model, and extracting short-term fluctuation and capacity recovery features; s4, modeling a low-frequency component: introducing a two-way gating circulation unit network constrained by a double-index degradation model, and simulating a long-term trend; and S5, constructing a high-frequency migration module through tensor decomposition, improving cross-battery generalization, and fusing high and low frequency results to output a residual life prediction value. According to the method, a dual-channel framework combining signal decomposition, deep learning and physical modeling is combined, the prediction precision and adaptability under complex degradation are improved, and the method is suitable for various battery systems.

The invention discloses a lithium ion battery life prediction method and system based on health state detection, and relates to the technical field of batteries, and the method comprises the steps: collecting a plurality of real-time operation data of a lithium ion battery in a charge-discharge cycle process, and obtaining a battery state data set; traversing the battery state data set to extract a health characteristic parameter set for health assessment, and obtaining a health state value of the battery; performing cycle use prediction according to the health state value, obtaining a battery residual cycle prediction frequency, performing conversion according to the use duration in combination with the battery use frequency, and generating battery residual use prediction time; and calculating a battery life decline rate and combining with the battery residual use prediction time to perform battery life prediction, generating a residual use life estimation value to perform health state evaluation on the battery, and generating a battery life report. The technical problems of inaccurate battery health assessment and insufficient life prediction precision in the prior art are solved, and the technical effects of improving the battery health assessment accuracy and the life prediction reliability are achieved.

The invention relates to a lithium ion battery thermal runaway early warning method and device and a medium, and the method comprises the steps: setting an AC excitation power supply parameter, and outputting a sine excitation current with a preset frequency and amplitude; injecting the polymer into a single battery through a four-probe method; synchronously acquiring an injection current signal and a response voltage signal; through fast Fourier transform analysis, the amplitude and the phase of a signal fundamental frequency component are extracted; and calculating the electrochemical impedance under the frequency. And in combination with an electrochemical impedance-thermal runaway precursor mapping database established by a pre-experiment, converting the impedance value into a corresponding thermal runaway risk level, and outputting corresponding early warning information. According to the method, lithium dendrite growth and internal micro short circuit signs can be captured in advance, and the early warning time efficiency is remarkably improved compared with traditional voltage / temperature monitoring.

The invention relates to the technical field of lithium ion battery testing, and discloses a lithium ion battery life loss evaluation method and device, a medium and equipment, and the method comprises the following steps: S1, collecting multi-modal dynamic data of a lithium ion battery in a charge-discharge cycle process; s2, performing time-frequency domain conjoint analysis on the multi-modal dynamic data, and extracting a battery aging sensitive feature set; s3, constructing a multi-scale coupling model of battery life loss; by constructing a multi-modal data fusion mechanism and a dynamic feature extraction system, during lithium ion battery life loss evaluation, change trends of electrochemical impedance spectroscopy and heat distribution key parameters are captured in real time based on time-frequency domain conjoint analysis, sensor signal distortion and drift problems can be identified, the extraction precision of aging sensitive features is improved, and the accuracy of lithium ion battery life loss evaluation is improved. The problem of characteristic errors caused by signal interference in traditional evaluation is solved, and the accuracy and reliability of life loss evaluation are ensured.

The preparation method comprises the following steps: S1, mixing a hydrochloric acid solution with organic sodium sulfonate, then adding aniline and water, stirring and dissolving, then adding an oxidizing agent to carry out doping reaction, and carrying out suction filtration, washing, drying and grinding on a reaction product to obtain sodium-modified polyaniline; s2, uniformly mixing a negative electrode active material, a conductive agent and polytetrafluoroethylene, and then adding sodium polyaniline and an organic sodium supplement agent in a nitrogen or inert atmosphere for fibration treatment to obtain fibration powder; and S3, performing hot pressing on the fiberized powder onto the current collector to obtain the lithium ion battery. The prepared pre-sodium-modified negative electrode dry-method electrode has relatively good peeling strength and pole piece flexibility, and the condition that a traditional wet-method thick electrode is easy to crack is relieved; meanwhile, the dry-method electrode subjected to sodium modification treatment can induce uniform deposition of sodium ions, so that the dynamic performance of the whole sodium battery is improved, the problem of cyclic sodium precipitation is avoided, and the service life of the battery is further prolonged.

The invention relates to the technical field of lithium ion battery safety, and discloses a lithium ion battery thermal runaway multi-cascade emergency response prevention system. The system comprises a thermal signal monitoring module, a thermal diffusion prediction module, an emergency suppression module, a pressure balance module and a multi-mode linkage module. The thermal signal monitoring module obtains multi-dimensional temperature data, identifies abnormity and generates a thermal runaway early warning signal; the thermal diffusion prediction module combines the early warning signal and the battery module structure parameters to calculate the thermal propagation path and rate and generate a prediction result; the emergency suppression module matches a suppression medium according to a prediction result, and calculates a dose putting strategy to form a suppression scheme; the pressure balance module obtains pressure data, analyzes the change rate and distribution, adjusts a pressure release valve strategy in combination with a suppression scheme, and generates a pressure regulation and control instruction; and the multi-mode linkage module integrates data and a scheme, and generates a system-level emergency response instruction based on a pressure regulation and control instruction to realize thermal runaway multi-level joint prevention.

The invention provides a biomass-derived spherical porous carbon material and a preparation method thereof, and a silicon-carbon negative electrode material and a preparation method thereof, and relates to the field of lithium ion batteries. The preparation method of the biomass-derived spherical porous carbon material comprises the following steps: carrying out pre-carbonization treatment on vinasse to obtain pre-carbonized vinasse, mixing the pre-carbonized vinasse with an acidic solution, and washing with water until the mixture is neutral to obtain pre-treated vinasse; mixing the pretreated vinasse with a pore-forming agent, a binder and water to prepare slurry, and performing spray drying on the slurry to obtain a spheroidized precursor; carrying out carbonization treatment on the spheroidized precursor; washing the carbonized spheroidized precursor with water until the spheroidized precursor is neutral, and drying the spheroidized precursor to obtain an intermediate; and activating the intermediate with water vapor to obtain the biomass-derived spherical porous carbon material. The material with a regular spherical and porous structure can be prepared, the material can be used for preparing a silicon-carbon negative electrode material with a stable structure, and the electrochemical performance of the silicon-carbon negative electrode material is improved.

The invention belongs to the technical field of design and preparation of solid electrolyte materials, and particularly relates to a LiBH4-based composite solid electrolyte material and a preparation method thereof.The LiBH4-based composite solid electrolyte material is obtained by performing high-energy ball milling on a mixture of LiBH4 and one or two metal halides MX2 in a certain molar ratio in an argon atmosphere; m is Mg, Zn, Ni or Bi; x is F, Cl, Br or I. According to the LiBH4 composite solid electrolyte prepared by the preparation method disclosed by the invention, the ionic conductivity at room temperature is improved to 10 <-4 > S cm <-1 > or above, and the ion diffusion activation energy is only 0.16 eV; the process is simple, the preparation time is short, the repeatability is high, no pollution is caused to the environment, and the industrial production requirement is met, so that the all-solid-state lithium ion battery electrolyte is expected to realize commercialized application.

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 ion battery composite diaphragm, a preparation method and a lithium ion battery, and relates to the technical field of lithium ion batteries. The lithium ion battery composite diaphragm comprises a base membrane and a functional coating arranged on the negative electrode side of the base membrane, the functional coating sequentially comprises an ion conducting layer, a thermal barrier layer and a self-repairing layer from the position close to the base film to the position far away from the base film; wherein the base membrane is a polyimide and aramid nanofiber blended electrostatic spinning membrane; the ion conducting layer comprises a compound of a modified fast ion conductor and polyvinylidene fluoride-hexafluoropropylene; the thermal barrier layer comprises a cross-linked network of boron nitride nanosheets and polybenzimidazole; the self-repairing layer comprises polyurethane microspheres loaded with dynamic disulfide bonds. The composite diaphragm provided by the invention can remarkably inhibit a purple area on the surface of a negative electrode, and enhance high-temperature self-protection and interface self-repairing of the lithium ion battery, so that the cycle and safety performance of the lithium ion battery are remarkably improved.

The embodiment of the invention discloses a lithium ion battery life prediction method and a training method of a cooperative driving model. The prediction method comprises the following steps: acquiring a trained cooperative driving model and multi-modal data of a target battery; constructing a feature matrix including time sequence features, mechanism features and material features based on the multi-modal data; a weighted fusion vector is obtained based on the feature matrix by using a self-attention mechanism, and the weighted fusion vector is used as the input of a collaborative driving model; extracting mechanism features based on the mechanism model, and extracting data features based on a deep learning model; and obtaining a predicted life value of the target battery corresponding to the mechanism characteristic and the data characteristic based on the full connection layer. The defect that physical and chemical data in the battery and battery operation data are not fully utilized in a traditional lithium ion battery life prediction method is overcome, and the adaptive capacity and prediction precision of the prediction method under the dynamic working condition are improved.

The invention belongs to the technical field of battery management, and discloses a lithium ion battery state-of-charge estimation method for heterogeneous sensor data missing. The space-time mask auto-encoder comprises a parallel multi-branch channel encoder, a feature level mask module and a space-time collaborative attention decoder; the parallel multi-branch channel encoder performs time-frequency domain feature decoupling on signal sequences from the plurality of heterogeneous sensors, and extracts and fuses transient response of voltage and periodic load characteristics of current; learning and accurately reconstructing missing sensor channel information in a high-dimensional feature space through a feature level mask module; and cascading the estimated value output by the space-time mask auto-encoder with the extended Kalman filtering unit to obtain a final result. According to the method provided by the invention, the robustness, the accuracy and the generalization capability of SOC estimation in a sensor channel part missing scene are remarkably improved, and a key technical support is provided for realizing accurate state monitoring and intelligent safety management of the lithium ion battery under a complex working condition.

The invention relates to the field of batteries, in particular to a preparation method and system of a silicon-carbon material, and the preparation method comprises the following steps: pretreating porous carbon, conveying the pretreated porous carbon to a fluidized bed reactor, sequentially introducing a silicon source gas and a carrier gas for silicon deposition treatment, heating, introducing a carbon source gas, adjusting the flow of the carrier gas for carbon coating treatment, and discharging and cooling to obtain a finished product, meanwhile, reaction tail gas is recycled. The preparation system comprises a gas inlet unit, a fluidized bed reactor unit, a tail gas circulation unit, a feeding preheating unit and a discharging unit. The fluidized bed reactor comprises an outer shell, an up-down stirring device, a heat exchange assembly and an air distribution assembly, and efficient reaction and temperature control can be achieved. The silicon-carbon material prepared by the method has excellent electrochemical performance and is suitable for a lithium ion battery negative electrode material. The unique porous structure and the uniform carbon coating layer effectively improve the conductivity and the structural stability of the material, and meanwhile, the volume expansion effect of silicon in the charging and discharging process is reduced.

The invention belongs to the technical field of lithium ion batteries, and particularly relates to a self-repairing type lithium ion battery positive electrode adhesive, a preparation method, positive electrode slurry and a positive electrode plate. The adhesive comprises a mixed solvent, and a first copolymer serving as a main component and a second copolymer serving as a cross-linking agent are dissolved in the mixed solvent; the first copolymer is an acrylate-based multipolymer containing carboxyl or hydroxyl; the second copolymer is isocyanate terminated UPy functional polyurethane; the adhesive is in a liquid state at room temperature, when the adhesive is subjected to vacuum heat treatment at 60-180 DEG C, carboxyl or hydroxyl in main components of the adhesive reacts with isocyanate groups of a cross-linking agent to form amido bonds along with volatilization of a mixed solvent, and UPy groups are associated through quadruple hydrogen bonds to form dual dynamic cross-linking points. The method can effectively adapt to the volume change of the positive electrode material in the lithium removal / insertion process, and maintains the integrity of the electrode structure, thereby improving the cycling stability and rate capability of the lithium ion battery.

The invention discloses a composite coated graphite negative electrode material, a preparation method thereof and a lithium ion solid-state battery, and belongs to the technical field of lithium ion solid-state batteries, the composite coated graphite negative electrode material comprises a graphite matrix and a composite coating layer, and the composite coating layer comprises an inner layer amorphous lithium chloride and an outer layer nanocrystalline LiF / Li3N composite phase. Mixing graphite, lithium chloride containing crystal water and ammonium fluoride to obtain a mixed material; placing the mixed material in a microwave reaction cavity, heating the mixed material to 600-700 DEG C at the microwave power of 800-1200 W in an H2 / Ar mixed atmosphere, and keeping the temperature for 20-60 minutes; and cooling the sintered mixed material, grinding, and sieving with a 100-300 mesh sieve to obtain the composite coated graphite negative electrode material with a coating layer thickness of 10-50 nm. The invention provides an innovative amorphous LiCl / nanocrystalline LiF-Li3N gradient coating layer design, the performance of the lithium ion battery is remarkably improved by optimizing an ion migration path, enhancing the interface stability and reducing the process cost, and a new engineering way is provided for realizing high magnification and long cycle performance of the sulfide all-solid-state battery.

The invention provides a non-aqueous electrolyte, a lithium ion battery, a battery module, a battery pack and a power utilization device, the non-aqueous electrolyte comprises a lithium salt, an organic solvent and an electrolyte additive, and the electrolyte additive comprises 1, 3-propane sultone, fluoroethylene carbonate, vinylene carbonate, lithium tetrafluoroborate and a compound I shown in the formula I. The non-aqueous electrolyte is applied to the lithium ion battery, and the cycle life of the lithium ion battery is prolonged.

The invention relates to the technical field of lithium ion batteries, in particular to a composite layered oxide positive electrode material, a preparation method and a lithium ion battery. The composite layered oxide positive electrode material provided by the invention comprises a ternary material core, wherein the ternary material core is a high-nickel ternary oxide doped with Zr and Mg in a gradient manner; the coating layer shell comprises a first coating layer and a second coating layer, the outer side of the first coating layer is coated with the second coating layer, the first coating layer is a composite layer of fluorine-terminated MXene and sulfide solid electrolyte, and the second coating layer is a porous high-entropy oxide. The composite layered oxide positive electrode material provided by the invention has high specific capacity, excellent cycling stability, first coulombic efficiency and high rate performance, is compatible with existing industrial production, and remarkably improves the comprehensive performance and practical value of the lithium ion battery.

The invention provides a lithium ion battery core temperature prediction method based on a finite element model, and the method comprises the following steps: 1, designing an electrochemical test experiment, and constructing a one-dimensional electrochemical model containing a lithium ion battery electrochemical heat production mechanism; 2, designing a lithium ion battery temperature test experiment, obtaining electrochemical parameters, heat transfer characteristic parameters, internal thermophysical parameters and environmental parameters of the battery under different working conditions, and obtaining a temperature change curve of the center position and the surface of the battery; and step 3, based on the heat balance equation, establishing a three-dimensional heat transfer model having the same geometric characteristics as the battery used in the experiment. According to the experimental result in the step 2, the three-dimensional heat transfer model is subjected to non-uniform region division, each region corresponds to one one-dimensional electrochemical model, and the heat production rate per unit volume is calculated by the one-dimensional electrochemical model in the step 1; introducing the heat production rate of each area into a three-dimensional heat transfer model to calculate the temperature distribution of the battery, and designing an experiment to correct a one-dimensional electrochemical model; 4, modifying the operation conditions and heat dissipation conditions of the battery, and calculating and analyzing the temperature difference delta T between the center position and the surface position of the battery under different operation conditions and heat dissipation conditions by using the three-dimensional heat transfer model in the step 3; and 5, measuring the surface temperature of the battery, and calculating the temperature of the center of the battery according to the working condition of the battery in the step 4 and the delta T corresponding to the environment temperature, thereby realizing monitoring of the temperature of the center position of the battery. According to the method, a high-precision algorithm model is constructed and verified by utilizing data accumulated in an earlier-stage experiment, the central point temperature which is difficult to directly measure in the battery is predicted only through real-time and easily-acquired battery surface temperature information, operation condition parameters and heat dissipation conditions, key thermal state information is provided for a battery management system, and the battery management efficiency is improved. The method is used for real-time safety monitoring and thermal management optimization.

The invention provides a lithium ion battery health state estimation method and system based on a dual-drive interpretable integrated model, and belongs to the field of lithium ion battery health state estimation. The problems that an existing lithium battery health state monitoring method is single in feature source, insufficient in model generalization ability and poor in interpretability are solved. According to the method, based on an incremental capacity curve and a first-order RC equivalent circuit model, IC peak value features and ohmic internal resistance features are extracted, and a multi-source health feature space is constructed in combination with voltage statistical features; an integrated learning model based on Stacking is established, three basic models of a random forest, kernel ridge regression and an interpretable enhancement machine are integrated, and collaborative optimization of model hyper-parameters is realized by adopting a tree structure-based Bayesian optimization algorithm.

The invention belongs to the technical field of battery state-of-charge estimation, and provides a lithium ion battery state-of-charge and state-of-health joint estimation method and system, and the method comprises the steps: obtaining the capacity of a battery in a charge-discharge cycle process and stress and temperature data at different positions; the obtained data is preprocessed; respectively calculating and analyzing Spearman level correlation coefficients of each stress and temperature point and the state of charge and the state of health after preprocessing, and screening out stress and temperature characteristics of which the change exceeds a set degree and the correlation degree with the battery state exceeds a set value as key characteristics; taking the screened key features as input, taking the charge state value and the health state value as output, setting a model loss function, and training a joint estimation model; and inputting key features of stress and temperature data obtained in real time into the trained joint estimation model to obtain a charge state estimation value and a health state estimation value. According to the invention, high-precision estimation of SOC and SOH is realized.

The invention discloses a battery capacity prediction and state evaluation method and system based on multi-model collaborative learning, and belongs to the technical field of battery management. The method comprises the following steps: constructing a database containing multiple lithium ion battery long-term cycle data, and classifying according to a capacity attenuation trend; cleaning and preprocessing short-term cycle data of the to-be-tested battery; matching the to-be-tested data with the long-term attenuation trend in the database by using a clustering algorithm, and determining an optimal matching trend; distributing weights for the data in the matching trend by adopting a correlation algorithm, and generating initial capacity attenuation prediction; performing sequence correction on the preliminary prediction in combination with meta-learning and a related model, and generating a smooth future attenuation trend conforming to a physical law; and outputting a capacity prediction and health state evaluation result, and evaluating the prediction precision through a root-mean-square error and an average absolute percentage error. The method significantly improves the precision and generalization ability of long-term capacity prediction, and is suitable for various scenes such as electric vehicles, energy storage systems, consumer electronics and the like.

The invention relates to the technical field of batteries, and discloses a preparation method of a lithium nickel manganese oxide positive electrode active material, a battery monomer, a battery device and a power utilization device. The battery monomer comprises a positive pole piece, the positive pole piece comprises a positive pole film layer, the positive pole film layer comprises a lithium nickel manganese oxide positive pole active material, in an XRD diffraction pattern of the lithium nickel manganese oxide positive pole active material, the full width at half maximum of a characteristic diffraction peak of a (111) crystal face is 0.1-0.2 degree, the full width at half maximum of a characteristic diffraction peak of a (311) crystal face is 0.1-0.2 degree, and the full width at half maximum of a characteristic diffraction peak of a (311) crystal face is 0.1-0.2 degree. And (400) the full width at half maximum of the characteristic diffraction peak of the crystal face is 0.1-0.2 degree. According to the battery monomer provided by the embodiment of the invention, lattice defects of the positive electrode active material are few, capacity fading can be delayed in the cycle process, and the cycle performance of the lithium ion battery can be remarkably improved.

A ternary polycrystalline positive electrode material is a secondary ball formed by agglomerating primary particles, the secondary ball is equally divided into four parts in the direction from the center to the surface of the secondary ball, the four parts are an inner core layer, a first middle layer, a second middle layer and a shell layer, and the section of the ternary polycrystalline positive electrode material is observed through a scanning electron microscope. The porosity is sequentially reduced from inside to outside. The preparation method comprises the following steps: mixing a positive electrode material precursor, a lithium source and a Li-containing polyanion compound, and sintering at a high temperature to obtain a primary sintered matrix; and washing and drying the primary sintering substrate, mixing the primary sintering substrate with a coating agent, and sintering to obtain the ternary polycrystalline positive electrode material. The invention also discloses a lithium ion battery. The porosity of the section of the positive electrode material is sequentially reduced from inside to outside, so that the positive electrode material can be in more sufficient contact with an electrolyte, more diffusion paths are formed, the transmission obstruction of lithium ions is reduced, and the transmission rate of the lithium ions is effectively improved, so that the discharge performance of the positive electrode material is improved, and the cycle performance of the material is improved.