11667 results about "Lithium-ion 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 relates to the field of battery fault prediction, in particular to a lithium ion battery fault prediction method and system based on a BMS. The method comprises the following steps: extracting multi-dimensional operation monitoring parameters of a battery through a BMS (Battery Management System), carrying out multi-state evolution perception and label mapping processing, and constructing a global multi-state perception map of the battery; short-term abnormal sudden change detection is carried out according to the multi-dimensional operation monitoring parameters of the battery, and normal characteristic deviation trend analysis is carried out, so that an abnormal fluctuation deviation evolution trajectory is constructed; and performing deep topological correlation learning on the global multi-state sensing map of the battery based on the abnormal fluctuation deviation evolution trajectory, performing heterogeneous node global sensing, performing abnormal behavior causal relationship mining on heterogeneous deviation nodes in the battery, and performing multi-causal fission simulation to generate a battery behavior deterioration chain under an abnormal trend. According to the method, accurate and efficient fault prediction is realized, transfer learning is carried out, and the perspectiveness of subsequent BMS fault prediction is improved.

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 relates to the technical field of battery electric quantity estimation, in particular to a lithium ion battery electric quantity accurate estimation method and system based on a BMS. The method comprises the following steps: collecting BMS original data, carrying out voltage current interval slicing, constructing a charging and discharging period node map, detecting node interruption joints, comparing slope differences to identify charging and discharging turning points, dividing independent charging and discharging periods according to the turning points, and determining current change time sequence data of each period. The actual charging and discharging electric quantity is calculated through the time sequence data, the charging and discharging difference is compared, the historical input and discharging electric quantity is further inquired, the fuzzy electric quantity range of the battery is deduced, and finally the real-time remaining available electric quantity is accurately calculated according to the fuzzy electric quantity range and the actual charging and discharging electric quantity difference and is uploaded to the BMS system. According to the invention, real-time monitoring and management of the electric quantity estimation result are realized, and the intelligent level of the battery management system is improved.

The invention discloses a lithium ion battery immersed liquid cooling regulation and control method and device, terminal equipment and a storage medium, and relates to the field of lithium ion batteries, and the method comprises the steps: obtaining the current temperature data, historical temperature data and cooling liquid fluid parameters of a distributed array in a lithium ion battery, and determining the current temperature field distribution and predicted temperature field distribution; constructing a state space, and inputting the state space into the reference decision model, so that the model outputs corresponding actions according to the current state space by taking the minimum temperature difference of the battery module and the minimum cooling energy consumption as awards; according to the corresponding action, the micro electromagnetic valve and the cooling liquid pump are subjected to benchmark regulation and control; acquiring temperature data after reference regulation and control, and determining temperature deviation in combination with a preset temperature requirement; and regulating and controlling according to the temperature deviation. By implementing the method and the device, the stability of global temperature control is improved, so that the problem of temperature interference on other areas due to cooling liquid flow coupling caused by directly adjusting the opening degree of the valve according to local temperature difference in the prior art is solved.

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 silicon-carbon composite material capable of improving the cycling stability of a battery, a preparation method of the silicon-carbon composite material, a negative pole piece and a lithium ion battery. The carbon-silicon composite particle comprises a silicon-carbon inner core and a carbon coating layer, nano-silicon is attached in holes and / or on the surface of a porous carbon matrix to form the silicon-carbon inner core, and the carbon coating layer is coated on at least part of the surface of the inner core; the porous carbon matrix and the carbon coating layer are both doped with halogen. According to the silicon-carbon composite material, the electron conductivity of the silicon-carbon composite material is improved by doping the halogen in the porous carbon matrix inside the silicon-carbon composite material and the carbon coating layer outside the silicon-carbon composite material; and meanwhile, the porous carbon matrix, the inner core composed of the nano silicon and the carbon coating layer are mutually matched and have synergistic interaction, so that the volume expansibility and defects of the silicon-carbon composite material are reduced, and the cycle stability is improved.

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 field of lithium battery detection, and particularly discloses a lithium ion battery fault detection method, which comprises the following steps of: firstly, acquiring total current data of a battery pack in real time, and judging the current running state of a battery system according to the total current of the battery pack and the duration time; and when the battery system is in a standing state, calculating the voltage drop rate and the temperature rise rate of each single battery in a preset time window, and comparing the voltage drop rate and the temperature rise rate with corresponding thresholds to realize self-discharge detection and fault identification. And when the battery system is in a charging or discharging state, a deep learning algorithm is further introduced, temperature-pressure coupling time sequence fluctuation characteristics of each single battery are extracted, and temperature-pressure coupling time sequence fluctuation state consistency analysis is performed on each single battery, so that intelligent detection of faults of the single batteries in the charging and discharging process is realized. According to the method, the abnormal temperature and pressure fluctuation of the single battery in the charging and discharging process can be effectively captured, so that early warning and accurate positioning of a fault are realized.

The invention provides a lithium ion battery health state estimation method, and belongs to the field of battery management, and the method comprises the steps: obtaining the EIS data, the current temperature and the current SOC of a to-be-detected lithium ion battery; the EIS data are analyzed through a DRT method, a DRT curve is obtained, health indexes are extracted from the DRT curve, and the health indexes comprise the peak amplitude, the time constant corresponding to the peak value, the peak area, the full width at half maximum, the weighted average time constant, the weighted standard deviation time constant, the time constant skewness and the time constant kurtosis; combining the health index, the current temperature and the current SOC to obtain an input feature vector, inputting the input feature vector into the trained SOH estimation model, and estimating the SOH of the lithium ion battery to be measured; the invention further provides an estimation system. The eight health indexes are closely related to aging mechanisms such as SEI membrane growth, active substance loss and impedance increase of the battery, high-quality input is provided for the model, and SOH estimation precision is improved; the temperature and the SOC serve as key input, and the influence of working condition changes on EIS measurement is effectively compensated.

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 discloses a LiAlO2 fast ion conductor coated silicon-carbon composite material as well as a preparation method and application thereof, and belongs to the technical field of lithium ion battery materials. The LiAlO2 fast ion conductor coated silicon carbon composite material has a core-shell structure; an inner core of the composite material is a silicon-carbon composite material, the silicon-carbon composite material is formed by coating a three-dimensional carbon material with an amorphous carbon layer and loading a nano silicon compound, a shell of the composite material is a LiAlO2 fast ion conductor layer, and the silicon-carbon composite material with a three-dimensional network structure is constructed, and the surface of the silicon-carbon composite material is coated with the LiAlO2 fast ion conductor layer, so that the composite material is obtained. According to the present invention, with the LiAlO2, the volume expansion of the nanometer silicon during the charge-discharge process can be effectively relieved, the stability of the silicon-carbon composite material structure can be improved so as to significantly improve the cycle performance of the battery, and the LiAlO2 can provide the rapid channel for the transmission of the lithium ion during the charge-discharge process so as to improve the rate performance of the battery;

The invention discloses a lithium ion battery capacity inflection point prediction method and system based on a multi-parameter data fusion decision. The method comprises the following steps: collecting multi-parameter data of a lithium battery and preprocessing the multi-parameter data; constructing an inflection point prediction model based on deep learning, and extracting time sequence data characteristics of current, voltage and temperature; based on an attention-enhanced graph convolutional neural network AGCN, an attention mechanism is introduced into a graph convolutional neural network GCN to dynamically learn the association weight of a multi-parameter feature matrix, and multi-parameter data fusion features are obtained; dynamic decision making is carried out on the battery multi-parameter data fusion features, linear transformation is carried out on a dynamic decision making result to obtain a predicted value of an inflection point, and construction of an inflection point prediction model is completed; carrying out training optimization on the whole model, and predicting the residual cycle period of the battery to the inflection point; according to the method, inflection point high-precision prediction of any stage of the battery can be realized by depending on relatively short cycle period data.

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.