7668 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 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.

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 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 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 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 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 provides a lithium ion battery health state lightweight detection method based on a physical information neural network, and the method comprises the steps: collecting the time, voltage, current, temperature and state-of-charge data of a battery in a takeoff and landing stage discharge process, processing the data into takeoff and landing stage discharge time sequence data, and carrying out the detection of the lithium ion battery health state based on the takeoff and landing stage discharge time sequence data. The method comprises the following steps: designing characteristic factors related to battery aging, screening the characteristic factors by utilizing a Pearson's correlation coefficient and a grey relational degree algorithm to obtain optimal characteristic sequence data, inputting the optimal characteristic sequence data into a physical information neural network model constructed by two serially connected neural networks for training, and in the training process, obtaining the optimal characteristic sequence data. And performing hyper-parameter tuning on the two neural networks by adopting a Bayesian optimization algorithm, then performing fine tuning on the second neural network by adopting a hierarchical transfer learning strategy, and finally applying the trained physical information neural network model to battery health state detection. The method improves the quality of feature data, reduces the calculation complexity of features and models, and achieves the accuracy and reliability of the detection of the health state of the battery under the airborne condition.

The invention discloses a silicon-carbon negative electrode material, a preparation method thereof and a lithium ion battery, and relates to the technical field of lithium ion battery negative electrode materials. The material sequentially comprises a porous silicon core, a transition layer and a shell from inside to outside, the transition layer is a nitrogen-doped SiC / C composite layer, the shell comprises a graphene / carbon nanotube skeleton and MXene quantum dots, the MXene quantum dots are embedded in the graphene / carbon nanotube skeleton, boric acid ester bonds exist between carbon nanotubes and graphene, and the MXene quantum dots are embedded in the graphene / carbon nanotube skeleton. The silicon-carbon negative electrode material is based on dual-network dynamic bonding and stress gradient regulation and control, the cycle performance of the material can be remarkably improved, and the service life of the material can be remarkably prolonged.

The invention relates to the field of battery thermal management, in particular to an AUV lithium ion battery thermal state prediction method. Comprising the following steps: constructing an electrothermal coupling reduced-order thermal model, and generating initial temperature estimation with physical consistency; a physical guidance space-time dynamic graph convolutional network PG-STDGCN is constructed as an error correction model, the model constructs a static and dynamic fused adjacency matrix by embedding physical priori such as a battery topological structure and circuit characteristics into dynamic graph learning, and a correction value of initial temperature estimation is output; and adding the initial temperature estimation and the correction value to obtain a final battery thermal state prediction result. According to the method, organic fusion from physical modeling to data-driven correction is realized, interpretability, precision and adaptability are considered under the dynamic working condition of the AUV, and the battery pack-level multi-cell temperature prediction performance is remarkably improved.

The invention provides a phenolic resin-based spherical silicon-carbon composite material as well as a preparation method and application thereof, and belongs to the technical field of lithium battery materials. The preparation method comprises the following steps: preparing spherical phenolic resin by adopting a suspension polymerization method, curing to obtain phenolic resin microspheres, preparing porous carbon spheres by taking the phenolic resin microspheres as a carbon precursor in a carbonization and alkali activation manner, carrying out CVD vapor deposition on nano silicon, and carrying out carbon coating to obtain the phenolic resin-based spherical silicon-carbon composite material. The phenolic resin is utilized to form a highly cross-linked network structure after curing, the carbon spheres obtained after carbonization have good mechanical strength and rigidity, the porous structure of the porous carbon spheres provides a volume change buffering structure for nano-silicon, the lithium ion path is shortened, the spherical structure is beneficial to uniform dispersion, nano-silicon particles are prevented from gathering, and the performance of the lithium ion battery is improved. And the porous carbon spheres with high specific surface area can also improve the interface reaction activity, so that the problems of high volume expansion, low conductivity and slow ion diffusivity of silicon are solved.

The invention relates to a fragmented charging data-based lithium ion battery health state estimation method, which comprises the following steps of: acquiring voltage, current and timestamp original data of a constant current charging stage of a lithium ion battery, and intercepting a 100-600-second standardized fragmented charging fragment based on a window parameter consisting of an initial voltage threshold Vstart, a sampling interval delta t and a sampling point number n; the method comprises the following steps: calculating an accumulated charging capacity through a coulomb counting method, generating a voltage-capacity curve, deducing a capacity increment-voltage curve and a voltage increment-capacity curve, and carrying out smooth filtering, multi-modal data splicing and normalization processing to obtain a 3 * n multi-channel characteristic tensor; according to the method, complete charging and discharging data are not needed, different battery types and complex working conditions are adapted, the lowest SOH estimation root-mean-square error can reach 0.00355, the highest determination coefficient (R) can reach 0.99485, and the practicability and estimation efficiency in practical application are greatly improved.

The invention discloses an intelligent sorting method and device for a positive electrode material of a retired lithium ion battery based on a spectrum method, and the device is connected in series with a pretreatment module, a cleaning module, an identification module and a sorting module through a conveying belt in a supporting module to form an automatic sorting assembly line. Pre-treating the retired battery to obtain a clean positive plate; an X-ray fluorescence spectrophotometer in the recognition module collects the spectrum of the positive plate and transmits the spectrum to an industrial personal computer, and the spectrum is preprocessed and then input into a classifier library; the classifier library comprises a main classifier and an increment classifier based on element and spectral shape dual channels, if the output confidence reaches a preset threshold value, the material category is judged, otherwise, the spectrum is temporarily stored as an unknown spectrum; after unknown spectrums are accumulated to a set number, new categories are identified through a clustering algorithm, and a lightweight dichotomy increment classifier is trained and added into a classifier library; and the sorting module drives the hydraulic pushing hand to sort the positive plates according to the identification result. Through cooperative evolution of the main classifier and the increment classifier, the sorting precision is effectively improved.

The application discloses a high-energy-density lithium ion battery, which comprises a positive electrode sheet and a negative electrode sheet, the positive electrode sheet comprises a positive electrode active material, the positive electrode active material comprises a lithium iron phosphate material and a lithium-rich material, the mass ratio of the lithium-rich material in the positive electrode active material is 0.2%-8%, and the chemical formula of the lithium iron phosphate material is Li 1+x Fe 1‑ y M y (PO4) 1+z , 0<=x<0.1, 0<=y<0.01, 0<=z<0.04, M comprises at least one of Ti, V, Zr, Nb, Mg, Mo, W and Al, the lithium-rich material comprises at least one of LFO and LNO, the chemical formula of the LFO is Li 5+a Fe 1‑b Z b O 4+c , 0<=a<0.5, 0<=b<0.01, 0<=c<0.03, Z comprises at least one of Ti, V, Zr, Nb, Mg, Mo, W and Al, and the chemical formula of the LNO is Li 2+d Ni 1‑ e Y e O 2+f , -0.5<=d<=0.5, e>=0, -0.5<=f<=0.5, Y comprises at least one of Ti, V, Al, Mg, Mn and Fe, and the negative electrode active material comprises a silicon negative electrode material, the mass ratio of the silicon negative electrode material in the negative electrode active material is 1%-50%. The application has the advantages of improving the energy density, rate performance and cycle performance of the lithium battery.

The invention discloses a composite diaphragm, a preparation method thereof and a lithium ion battery. The composite diaphragm comprises a base membrane and a composite functional layer arranged on one side of the base membrane, and the composite functional layer is formed by compounding an independent high-temperature-resistant polymer porous membrane and a polytetrafluoroethylene porous membrane. Preferably, the high-temperature-resistant polymer porous membrane comprises one or more of an aramid fiber membrane and a polyimide membrane. By compounding the high-temperature-resistant polymer porous membrane and the polytetrafluoroethylene porous membrane, the overall thinning of the diaphragm is realized on the premise of ensuring excellent mechanical strength and thermal stability, and the ion migration impedance is effectively reduced, so that the rate capability, the cycle life and the safety performance of the lithium ion battery are remarkably improved.

The invention provides a diaphragm and a preparation method thereof and a lithium ion battery, the diaphragm comprises a base membrane layer and a composite functional layer, the composite functional layer comprises hydrophobic SiO2 aerogel modified by a hydrophobic agent, a conductive ion polymer and a solid electrolyte, and the composite functional layer respectively accounts for 3-12wt%, 0.5-1.0 wt% and 0.5-1.5 wt% of the total mass of the diaphragm; the hydrophobic agent comprises trimethylchlorosilane, hexamethyldisilazane, perfluorooctyltriethoxysilane, polyvinylidene fluoride, polydimethylsiloxane and the like, the conductive ionic polymer comprises polyvinylpyrrolidone, lithium polymethacrylate, lithium polystyrene sulfonate, polyacrylamide and the like, and the solid electrolyte comprises lithium lanthanum titanium oxide, lithium lanthanum zirconium oxide, lithium lanthanum zirconium tantalum oxide and the like. The wettability is improved by the high porosity of the hydrophobic SiO2 aerogel, the stability and strong conductivity of a physical conductive network of the solid electrolyte and the ion affinity and amphoteric binding capacity of the conductive ion polymer are optimized, the electrochemical performance and thermal stability of the interface are optimized, and comprehensive optimization of the thermal-electric-interface performance of the diaphragm is realized through combination.

The invention provides a lithium ion battery and an electric device, the lithium ion battery comprises a positive electrode, a negative electrode, a diaphragm and an electrolyte, the positive electrode comprises a positive electrode active material layer, the positive electrode active material layer comprises a positive electrode active material and an oxide solid electrolyte, and the surface of the diaphragm is provided with an oxide solid electrolyte layer; the electrolyte comprises a solvent, a lithium salt, a first additive and a second additive, the first additive comprises fluoroethylene carbonate, and the second additive comprises 1, 3-propane sultone; the lithium ion battery satisfies the following conditions: 0.72 < = (H + X) / P < = 8; 0.5 < = (A + B) / (H + X) < = 8.89; 0.16 < = H / X < = 6.4; 0.5 < = A / B < = 12. According to the lithium ion battery provided by the invention, the safety and the high and low temperature performance of the positive electrode of the lithium ion battery are remarkably improved through multi-component cooperative regulation and control, further, when the lithium ion battery meets the limitation of the relational expression at the same time, the interface impedance can be reduced to the maximum extent, and the performance of the lithium ion battery can be improved through cooperation of multi-component parameters. And synchronous optimization of thermal safety, low-temperature discharge capacity and high-temperature cycle life of the battery is realized.

The invention discloses a cellulose coated diaphragm and a preparation method thereof in the field of lithium ion batteries, the diaphragm comprises alpha-cellulose pulp, a cellulose grafted phenylboronic acid-co-oligomeric ethylene oxide copolymer, a thermal response star zwitterionic polymer and nano aluminum oxide, and an N-methylmorpholine-N-oxide aqueous solution is adopted as a solvent system. The preparation method comprises the following steps: firstly synthesizing a cellulose grafted copolymer with dynamic borate bonds through atom transfer radical polymerization, then preparing a star polymer with thermal response arms and zwitter-ion terminals by taking cyclodextrin as a core, and finally mixing all the components to prepare coating slurry. And the composite diaphragm is prepared through the processes of coating, phase conversion, ultraviolet curing and drying. The diaphragm combines the thermal stability and lyophilic property of cellulose, forms a dynamic self-adaptive interface layer through the synergistic effect of two functional compounds, has the advantages of active safety protection, dendritic crystal inhibition, long cycle life and the like, and remarkably improves the comprehensive performance of the lithium ion battery.

The invention discloses a lithium ion battery health state prediction method based on a double-branch feature fusion network, and the method comprises the steps: employing a unified feature analysis method to extract a plurality of health indexes based on NASA and CALCE battery data sets, and employing a Pearson correlation analysis method to select M health indexes highly related to the battery health state; based on the selected M health indexes, performing denoising processing on the health index data by adopting a variational mode decomposition method to obtain denoised health index data; constructing a dual-branch feature fusion network, and training by using the de-noised health index data to obtain a trained dual-branch feature fusion network; and utilizing the trained double-branch feature fusion network to predict a battery health state prediction value of the next round. According to the invention, by effectively integrating the multi-scale battery degradation characteristics, the accuracy, robustness and prediction precision of battery health state prediction are improved, and the adaptability and generalization ability of the network are enhanced.

The invention relates to the technical field of nano materials, and particularly discloses a preparation method of surface modified nano calcium carbonate for a battery diaphragm coating. According to the method, the technical paths of microemulsion confinement crystal form regulation and control, gradient surface modification and boehmite compounding and porosity regulation and control are fused, and the functional nano calcium carbonate adaptive to the battery diaphragm coating is prepared. According to the technical scheme, limestone is used as a raw material, after calcination and digestion, the particle size of nano calcium carbonate is controlled through microemulsion carbonization (a cyclohexane-span 80-n-butyl alcohol system); a silane coupling agent KH560-KH570 is adopted for gradient modification to introduce an electrophilic functional group, boehmite is compounded to form a composite coating, and the porosity is regulated and controlled by adjusting the solid content in coating slurry and the coating speed. The method is stable in process and high in compatibility, and meets the requirements of diaphragm coatings of lithium ion batteries and solid-state batteries.

In order to solve the problems of poor high-temperature and low-temperature cycle performance and poor safety performance of a lithium ion battery in the prior art, the invention provides a lithium ion battery, which comprises a positive electrode, a negative electrode, a diaphragm and an electrolyte, the positive electrode comprises a positive electrode active material layer, the positive electrode active material layer comprises a positive electrode active material and an oxide solid electrolyte, the surface of at least one side of the diaphragm is coated with an oxide solid electrolyte coating, the electrolyte comprises a first additive and a second additive, the first additive comprises a fluorosulfonyl amide compound, and the second additive comprises mannitol carbonate sulfate; the lithium ion battery meets the following relational expressions: (H + X) / P is more than or equal to 0.8 and less than or equal to 8; formula 2: 0.75 < = (A + B) / (H + X) < = 20; formula 3: 0.16 < = H / X < = 6.43; formula 4: 1.25 < = A / B < = 36.

The invention discloses a lithium battery dynamic rapid charging track generation method based on multi-state estimation and risk criterion, belongs to the technical field of electric vehicle lithium ion battery charging, and aims to solve the problem that the charging speed, the energy efficiency and the battery life are difficult to consider in the rapid charging process of a lithium ion battery in a wide temperature range. And a relatively high lithium plating safety risk exists. The method comprises the following steps: firstly, constructing an electrochemical thermal aging coupling model, and establishing a lithium plating risk criterion and risk map based on the model; then, on-line multi-state estimation is realized based on an electrothermal coupling equivalent circuit model, and a multi-objective optimization function and a safety constraint system which comprehensively consider charging time, energy efficiency and life loss are constructed; and finally, a model prediction control framework is utilized, a state estimation result and a risk criterion are fused, and an optimal charging current track is generated in a real-time rolling manner.

The invention discloses a negative pole piece, a negative pole piece and a secondary battery, and belongs to the technical field of electrochemical energy storage, the product takes porous carbon in which an aromatic heterocyclic polymer and a carbon nanotube are arranged as a matrix, a silicon carbon material is arranged on the surface, and meanwhile, the pore volume and crushing force of the negative pole material are regulated and controlled, so that relatively high capacity contribution can be reserved, and the service life of the negative pole piece is prolonged. And the volume expansion rate is obviously reduced in the lithium deintercalation process, and the cycle performance is excellent when the lithium ion battery is applied to a secondary battery.

The invention provides a self-assembled hypha flexible carbon electrode material and a preparation method and application thereof, and belongs to the technical field of battery packs. The preparation method of the self-assembled hypha flexible carbon electrode material comprises the following steps: treating basswood with white rot fungus mycelia, crushing and roasting to obtain porous basswood / hypha composite carbon powder; and putting the porous basswood / hypha composite carbon powder and a carbon nanotube into a sterilized conical flask containing a potato glucose culture solution, inoculating gloeophyllum trabeum, performing activation culture, adding a dopamine hydrochloride aqueous solution, performing polymerization after ultraviolet irradiation, taking out and tabletting to obtain the self-assembled hypha flexible carbon electrode material. In the preparation process of the self-assembled hypha flexible carbon electrode material, the flexible carbon electrode material is synthesized by taking porous basswood / hypha composite carbon powder as a matrix, taking gloeophyllum trabeum hypha as an adhesive, taking polydopamine as a reinforcing agent and adding carbon nanotubes as a conductive reinforcing material, so that the technical problem of poor cycle performance of a lithium ion battery is solved.