11850 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 relates to the field of lithium battery negative electrode materials, and particularly discloses a silicon-carbon composite negative electrode material for a lithium ion battery and a preparation method of the silicon-carbon composite negative electrode material. The method comprises the following steps: preparing a porous carbon substrate, and depositing silicon nanoparticles in pores and on the surface of the porous carbon substrate by adopting a fluidized bed chemical vapor deposition process and taking silane as a silicon source to obtain a silicon-carbon core material; continuously carrying out carbon coating on the surface of the silicon-carbon core material in the fluidized bed by taking acetylene as a carbon source to form a carbon coating layer; and leading out the obtained material from the fluidized bed, taking acetylene as a carbon source, and carrying out carbon coating on the surface of the primary carbon coating layer again by adopting the rotary furnace to form a secondary carbon coating layer. According to the preparation method, a porous carbon matrix and double-layer functional carbon coating process is adopted, so that the volume expansion of silicon in the circulation process is remarkably inhibited, the interface side reaction is reduced, and the circulation stability of the material is effectively improved.

The invention discloses a 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 ion battery health prediction system and method based on microscopic image multi-scale and region adaptive fusion. The prediction system comprises a data acquisition module, a data preprocessing module, a feature extraction module, a multi-scale feature fusion module, a health state prediction module and a data enhancement and optimization module. The method overcomes the limitation based on traditional electric signal analysis, is suitable for various microscopic imaging technologies, enables the battery health state prediction to be directly evaluated based on the change of the internal microstructure of the electrode, and improves the prediction precision, applicability and physical interpretation. The method can be widely applied to battery life prediction, battery management system optimization and intelligent monitoring systems, and provides reliable technical support for battery safety management, performance optimization and industrial intelligence.

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 relates to a silicon-carbon composite material and a preparation method and application thereof.The preparation method comprises the steps that a porous graphite raw material is subjected to acid pickling and smashing to obtain pretreated porous graphite, then the pretreated porous graphite is soaked in a carbon precursor solution in a vacuum mode, carbonization treatment is conducted after centrifugal separation, pores of the porous graphite are filled with carbides, physical activation is conducted, and the silicon-carbon composite material is obtained; the preparation method comprises the following steps: forming pores in a carbide to form a graded pore structure to obtain a porous graphite composite material, then carrying out silicon deposition in a fluidized bed to obtain a uniform nano silicon layer, and then carrying out carbon coating treatment to obtain a silicon-carbon composite material; the graphite with relatively low cost is adopted as a matrix, high capacity and long cycle performance of the graphite-based silicon-carbon negative electrode material are realized through hierarchical pore structure design, carbonization-activation synergistic pore forming and interface deposition process optimization, and the graphite-based silicon-carbon negative electrode material can be applied to the fields of power batteries and high-energy-density energy storage, especially the field of lithium ion batteries and has wide application prospects. The expansion rate of the battery can be reduced, and the specific capacity, cycling stability and safety of the lithium ion battery can be improved.

The invention provides a lithium manganese iron phosphate battery. The battery comprises a positive pole piece, a negative pole piece and a non-aqueous electrolyte, the positive pole piece comprises a positive active material and a positive active coating arranged on at least one surface of the positive active material; the positive electrode active material comprises lithium manganese iron phosphate LiMnxFe1-xPO4 coated with a carbon coating layer, and x is more than or equal to 0.5 and less than or equal to 0.8; the negative pole piece comprises a negative active material and a negative active coating arranged on at least one surface of the negative active material; the battery satisfies the following relational expressions: 4.5 < = (a / 20 + d) / (b + c) < = 12.8; a is a coated diaphragm resistor of the positive pole piece; b is the compaction density of the positive pole piece; c is the mass percentage content of vinylene carbonate in the positive electrode slurry; and d is the mass percentage content of the lithium bis (fluorosulfonyl) imide in the non-aqueous electrolyte. By optimizing the composition of the lithium ion battery, the low-temperature discharge performance of the battery is improved, and meanwhile, the high-temperature cycle performance is considered.

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 provides a lithium ion battery health state prediction method, apparatus and device, and a storage medium, and relates to the technical field of lithium ion batteries, and the method comprises the steps of collecting real-time voltage, current and temperature data of a target battery system; inputting the real-time voltage, current and temperature data of the target battery system into a battery health state prediction model, and dynamically predicting the health state of the target battery system; wherein the battery health state prediction model is obtained by performing transfer learning fine tuning on a global pre-training model based on a private data set of the target battery system; the global pre-training model is obtained by performing distributed training through a federated learning framework based on operation data of a plurality of client lithium batteries under different charging strategies, federated learning and transfer learning technologies are combined, battery health prediction in a scene with privacy protection requirements can be processed, and the method and the system have high practicability. Moreover, the problem of data scarcity can be solved, and the real-time evaluation of the health state of the battery in a charging scene is realized.

The invention discloses a battery safety early warning method based on multi-signal feature fusion, and relates to the technical field of lithium ion battery thermal runaway. According to the method, multiple characteristic parameter signals are concerned at the same time, more accurate early warning is obtained, the established electric heating model can be suitable for different temperature environments, multiple parameters such as temperature, current, voltage and internal impedance can be comprehensively considered, state estimation is more accurate, and rapid prediction of the battery state is achieved. For the safety risk possibly caused by the battery temperature, the temperature is introduced into the prediction process as a constraint condition, the SOC is estimated by adopting a resistance method with temperature compensation, and meanwhile, the SOC and SOH joint estimation method is adopted, so that the accuracy of battery health estimation is improved, and the safety early warning accuracy is further improved. And finally, a multi-head attention mechanism is adopted to process parallel parameter data in parallel, so that various characteristic parameters are processed more effectively, and the depth and accuracy of data analysis are improved.

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 battery health state estimation method and device, equipment, a storage medium and a product, and relates to the technical field of battery health management, and the method comprises the steps: carrying out the preprocessing of a collected retired lithium ion battery sample, and obtaining pulse voltage response data under different battery charging states; inputting the pulse voltage response data into a physical constraint diffusion model to generate pulse voltage response characteristic data; generating a battery health state estimation model according to the pulse voltage response characteristic data and the regression model; and performing health state estimation on the to-be-evaluated battery according to the estimation model. According to the method, the pulse voltage response data is extracted from the multi-dimensional retired lithium ion battery sample, then the battery health state estimation model is generated through the physical constraint diffusion model and the regression model, and then the battery health state is estimated according to the estimation model. According to the method, the SOH of the retired power batteries in different dimensions can be quickly and accurately estimated, and the energy consumption in the estimation process is low.

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 battery state-of-charge and state-of-health joint estimation method, and belongs to the technical field of lithium ion batteries. The method comprises the following steps: performing a cyclic aging experiment on a battery under a cyclic working condition; an open-circuit voltage curve is extracted through data analysis, a first-order equivalent circuit model is selected to construct a circuit, and a state equation is written; carrying out parameter identification by adopting a multi-forgetting factor recursive least square algorithm, and constructing an SOC estimator of an unscented Kalman filter based on a state equation and an observation equation to carry out SOC estimation; constructing an SOH estimation observer based on an extended Kalman filter, and estimating the SOH by using the estimated SOC; carrying out segmented dynamic identification on the open-circuit voltage curve by adopting an average moving method in combination with recursive least squares; and coupling the SOC estimator and the SOH estimator, and realizing SOC and SOH joint online estimation by applying the dynamically identified open-circuit voltage curve.

A battery system includes a lithium ion battery configured to couple to an electrical system, and a battery management system configured to electrically couple to the lithium ion battery and to control one or more recharge parameters of the lithium ion battery. The battery management system is programmed with an electrochemical model, and the battery management system is configured to monitor parameters of the lithium ion battery, and to control the one or more recharge parameters of the lithium ion battery based on the electrochemical model and the one or more monitored parameters. The electrochemical model determines lithium plating reaction kinetics at an anode of the lithium ion battery, determines a quantity of plated lithium at the anode of the lithium ion battery, or both, and indicates a relationship between the one or more monitored parameters and the lithium plating reaction kinetics, the quantity of plated lithium, or both.

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 belongs to the field of lithium resource recovery, and particularly relates to preparation, application and a recovery method of a high-hydrophilicity lithium extraction electrode. The preparation method of the lithium extraction electrode comprises the following steps: sequentially adding a hydrophilic polymer and a cross-linking agent into a solvent, heating and stirring to a homogeneous phase to obtain a binder solution, adding a conductive agent and a lithium extraction active material into the binder solution, uniformly stirring to obtain lithium extraction slurry, coating a current collector with the prepared lithium extraction slurry, and drying to obtain the lithium extraction electrode. And drying to obtain the high-hydrophilicity lithium extraction electrode. The hydrophilic polymer is used for directly bonding the electrode material, so that the electrode is wholly hydrophilic from inside to outside, the prepared electrode material greatly improves the hydrophilicity of the lithium extraction electrode and accelerates the lithium extraction reaction rate, and the electrode material has the characteristics of simple preparation method, low cost, high lithium extraction efficiency, easiness in recovery and environmental protection; and efficient selective lithium extraction can be realized in various lithium-containing solutions.

The invention discloses a lithium ion battery health life assessment method and system based on a feature image, and the method comprises the steps: pre-training a battery health life assessment model through the sample data of a source domain, and building a mapping relation between the feature image and the health life of a lithium ion battery, and the feature image is generated based on an incremental capacity feature; introducing transfer learning and utilizing sample data of a target domain to optimize a battery health life evaluation model; and extracting incremental capacity features for the to-be-evaluated lithium ion battery in the target domain, generating a feature image of the lithium ion battery, and inputting the feature image into the optimized battery health life evaluation model to obtain the corresponding health life. According to the method, the lithium ion battery health life evaluation model obtained based on source domain data training can better adapt to the battery characteristics and working conditions of the target domain so as to solve the problems of battery aging and differences influenced by various external environment factors, and therefore the accuracy of lithium ion battery health life evaluation on the target domain is improved.

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 discloses a battery thermal runaway early warning method based on multi-sensing information fusion, which is suitable for safety management of a lithium ion battery. A fiber bragg grating sensor is embedded into a battery, temperature and strain multi-dimensional physical parameters of the battery are monitored in real time, external electrochemical parameters are combined, and a multi-source sensing information fusion system is constructed. And by analyzing a square curve of an internal strain derivative, temperature gradient change and voltage drop characteristics, a thermal runaway early-stage signal is extracted, and graded early warning is realized. According to the method, the hysteresis of traditional surface parameter monitoring is broken through, an early warning signal is provided before thermal runaway, and the safety and reliability of a battery system are remarkably improved.

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 relates to the field of lithium ion battery detection, in particular to a lithium ion battery internal temperature estimation method based on surface temperature. Firstly, a control equation and boundary conditions are constructed based on a one-dimensional heat conduction model; then, performing time discretization on the control equation by adopting a display Euler method, and performing spatial discretization on the control equation by adopting a central difference method; and finally, initializing the heat production rate, and substituting the initialized heat production rate into the control equation to obtain a battery surface temperature calculated value. If the difference between the calculated value and the measured value of the surface temperature of the battery is less than 0.001, increasing the heat production rate, otherwise, reducing the heat production rate; and continuously iterating the heat production rate until the absolute value of the difference between the calculated value and the measured value of the surface temperature of the battery is less than 0.001, and stopping iteration to obtain the heat production rate and the internal temperature of the battery in the current time step length. The heat production rate and the internal temperature of the battery are reversely deduced through numerical solution, the limitation of the prior art on the temperature equalization hypothesis is overcome, and the calculation precision under the large temperature difference working condition is improved.

The invention discloses a lithium supplement additive and a preparation method and application thereof. The lithium supplement additive comprises a lithium sulfinate compound, and the lithium sulfinate compound contains an electron withdrawing group. An electron withdrawing group contained in the lithium sulfinate compound in the lithium supplement additive can effectively change electron cloud distribution around sulfur atoms, so that the lithium sulfinate compound is endowed with relatively high lithium supplement capacity; meanwhile, due to the existence of the electron withdrawing group, the electron energy of the lowest unoccupied molecular orbital (LUMO) of the lithium sulfinate compound is reduced, and the energy gap between the highest occupied molecular orbital (HOMO) and the LUMO of the lithium sulfinate compound is reduced, so that the lithium supplement additive can be arranged in an electrode plate to exert the lithium supplement capacity and also can be used as an electrolyte additive; and the material can participate in electrode / electrolyte interface formation.

The invention relates to a data-driven lithium ion battery state prediction method fusing physical information, and belongs to the technical field of batteries. The method comprises the steps that S1, a lithium ion battery multi-working-condition operation database is established, and battery current, terminal voltage and operation temperature data are collected through charging and discharging experiments; s2, a lithium ion battery state prediction model fusing a physical model and a neural network is constructed, physical-prediction joint loss of the model is calculated, the model is trained through a gradient descent algorithm, and physical information is fused into the model through a mathematical relationship between equivalent circuit model parameters and neural network intermediate variables and the joint loss of the model; and S3, inputting real-time battery state data based on the trained model, and predicting a battery state value at the next moment, including SOC or SOT and the like. According to the invention, accurate prediction of the state of the lithium ion battery driven by data fused with physical information can be realized.

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 relates to the technical field of lithium batteries, and provides a lithium ion battery. The battery comprises a positive plate and a negative plate; a lithium iron phosphate (LFP) material in the positive plate is formed by mixing particles with different particle sizes, the negative plate adopts a double-layer coating design, the upper layer uses first graphite particles with smaller particle sizes, and the lower layer uses second graphite particles with larger particle sizes. The positive plate is matched with a double-layer coated negative plate, so that the lithium ion battery has relatively high energy density, good quick charge performance, rate capability and high-temperature storage performance.