18486 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 relates to the technical field of power supply devices, in particular to an energy-saving optimization method for an electric energy storage device, and the method comprises the steps: fusing a lithium ion battery solid-liquid phase change kinetic equation, a flow battery fluid mechanics equation and a flywheel rotor rigid-flexible coupling equation through a neural differential equation; constructing a dynamic meta-knowledge base containing cross-time scale nonlinear interaction features; performing dynamic parameter compensation on the polarization voltage hysteresis effect and the electrolyte flow resistance coupling effect by using a physical information neural network to generate a decoupled residual feature vector; according to the method, multi-physics field coupling modeling is achieved through the dynamic meta-knowledge base, and the energy efficiency optimization effect of the energy storage device is improved by combining the physical information neural network and the quantum optimization algorithm.

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

The invention discloses a lithium ion battery thermal runaway multi-dimensional characteristic parameter early warning method, which comprises the following steps: S1, collecting internal multi-dimensional characteristic parameters of a battery pack in real time, including an expansion force signal, a voltage signal, a characteristic gas concentration signal and a temperature distribution signal; s2, constructing a multi-parameter collaborative analysis model, performing dynamic fusion analysis on the expansive force signal, the voltage signal, the characteristic gas concentration signal and the temperature distribution signal, and outputting a thermal runaway risk value through a weighted decision algorithm; s3, comparing the thermal runaway risk value with preset different early warning thresholds in an extremely early stage, determining a thermal runaway risk level, and outputting a multi-signal collaborative early warning instruction according to the thermal runaway risk level; and S4, linkage triggering is carried out on the battery pack to carry out cooling, smoke exhausting, fire extinguishing and explosion suppression prevention and control treatment. According to the invention, the problems of manual early warning disposal, missing of an optimal prevention and control window and high false alarm rate of single-parameter early warning are solved.

The invention discloses a lithium ion battery thermal runaway early warning method, and belongs to the technical field of battery safety monitoring, and the method comprises the steps: synchronously obtaining a low-frequency sound wave signal, a temperature signal, a voltage signal and a stress-strain signal of a lithium ion battery; noise reduction processing is carried out on the low-frequency sound wave signals, time-frequency feature extraction is carried out on the low-frequency sound wave signals after noise reduction processing, and feature frequency band energy corresponding to a thermal runaway early event is obtained; a thermal runaway sensitivity coefficient is calculated based on the characteristic frequency band energy, and when the thermal runaway sensitivity coefficient is larger than a first coefficient threshold value, thermal runaway early warning is triggered; and on the basis of the thermal runaway sensitivity coefficient, the temperature signal, the voltage signal and the stress-strain signal, constructing a feature vector, and inputting the feature vector into a trained long-short-term memory network and attention mechanism hybrid model for classification to obtain a thermal runaway early warning level. The method can solve the problems of installation difficulty, detection lag and high false alarm rate in the prior art.

The invention discloses a lithium iron phosphate positive electrode material, a preparation method thereof and a lithium battery, and relates to the technical field of lithium ion batteries. According to the method, the pressing index of the lithium iron phosphate positive electrode material is tested by adopting a specific method, so that the pressing index meets a specific range, meanwhile, the average circularity of larger particles in a Dv50-Dv100 range meets a proper range, the lithium iron phosphate positive electrode material has better particle movement capability, and the particles can move at a higher speed to be rearranged during pressing, so that the particle size of the lithium iron phosphate positive electrode material is reduced, and the quality of the lithium iron phosphate positive electrode material is improved. Therefore, the positive pole piece with high compaction density can be obtained, the charge-discharge capacity of the battery can be improved, and meanwhile, the particles can be prevented from being broken when being pressed, so that the positive pole piece has good anti-pressure capability. By adopting the lithium iron phosphate positive electrode material provided by the invention, the lithium battery has relatively high energy density and capacity retention ratio.

The invention discloses a lithium ion battery health state evaluation method and device based on deconstruction physical information, a medium and a product, and relates to the technical field of lithium ion battery health state evaluation, and the method comprises the steps: extracting a multi-dimensional health factor, inputting the multi-dimensional health factor into a deep physical information neural network fusing a self-attention mechanism module and a Koopman neural operator module, and obtaining a deep physical information neural network; and outputting the SOH estimation value. By extracting the universal health factors suitable for multiple working conditions, the problem that the universality of the health factors is insufficient is solved; a self-attention mechanism is utilized to enhance features and reduce redundancy, and the perception ability of the model to key information is enhanced; physical information is deconstructed by means of a Koopman neural operator, a physical mechanism of battery degradation is fused into the model, and the physical interpretability of the model is enhanced; the deep physical information neural network is fused with multi-dimensional information for estimation, individual differences and complex working conditions of different batteries are effectively dealt with, and therefore high-precision and high-robustness lithium ion battery SOH estimation is achieved.

The invention relates to a waste lithium iron phosphate battery recycling method, in particular to a graded recycling method for lithium, iron, phosphorus and graphite in a waste lithium iron phosphate battery, and belongs to the technical field of lithium ion batteries and resource recycling of the lithium ion batteries. According to the method, the mode that oxidizing roasting and selective leaching are combined is adopted, the leaching rate and selectivity of lithium are effectively improved, the leaching rate of the lithium element is close to 100% under the optimized condition, and limit recovery of the lithium element is achieved; meanwhile, aiming at the problem that iron and phosphorus in the leaching residues are difficult to utilize, a wet ball milling and secondary acid leaching method is combined, phosphorus and iron elements are separated from negative electrode graphite firstly, and graded recovery of iron, phosphorus and graphite is achieved. According to the method, the high recovery rate of lithium is realized, and meanwhile, high-added-value products such as ferric oxide and sodium phosphate are prepared by efficiently recovering ferric phosphate.

The invention discloses a lithium ion battery state of health (SOH) estimation method based on feature optimization, and the method comprises the following specific steps: obtaining the experimental data of a lithium ion battery, extracting initial health features reflecting the degradation of the battery, and carrying out the normalization processing of the initial health features; performing screening and dimension reduction on the preliminary health features by adopting a Spearman correlation coefficient and a grey correlation analysis method, and selecting six features with the highest SOH correlation degree; using an improved fully adaptive noise set empirical mode decomposition and sample entropy method to decompose and reconstruct health features, adopting t-SNE to perform transformation and dimension reduction on sample entropy features, using k-means + + clustering analysis, optimizing feature data and dividing the feature data into a training set and a test set; establishing a mapping relation between the optimized health features and the SOH through a bidirectional long short-term memory network (BiLSTM); a sparrow search algorithm (SCSSA) fusing sine, cosine and Cauchy variation is adopted to optimize BiLSTM parameters; and constructing an SOH estimation model based on SCSSA-BiLSTM, and obtaining a battery health state estimation value.

The invention provides a lithium ion battery health state estimation method and system based on an electrochemical model, and relates to the technical field of battery health state estimation, and the method specifically comprises the steps: collecting key parameters of a lithium ion battery in real time, and calculating an open-circuit voltage through an electrochemical reaction kinetic formula; calculating a health index, calculating a temperature influence index according to a set optimal working temperature and a current working environment temperature mean value, and calculating an internal resistance influence index according to the battery capacity, the cycle index and the internal resistance data of the lithium ion battery; calculating a battery health score for reflecting the health state of the lithium ion battery to be tested according to the health index, the temperature influence index and the internal resistance influence index; and comparing the battery health score with a preset health assessment threshold, and assessing the health state of the lithium ion battery according to the comparison result, thereby providing a scientific battery health assessment mechanism.

The invention provides a lithium ion battery health state evaluation method and system, and the method comprises the steps: obtaining tangent slopes of different voltage points in a lithium ion battery IC curve, voltage values corresponding to extreme points, constant-current charging and discharging current features and constant-voltage charging voltage features, and taking the features as key features of a to-be-detected lithium ion battery; carrying out correlation calculation by adopting a Pearson's correlation coefficient, a Spearman level correlation coefficient and a maximum information coefficient to obtain a final health assessment factor for assessment; the extracted key features can effectively reflect the health state degradation performance of the battery, and the feature noise can be effectively reduced by adopting three different correlation methods for screening, so that the accuracy of later evaluation is improved.

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

The invention 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 the technical field of battery model construction, in particular to a construction method and system of a lithium ion battery equivalent circuit model and a medium. The method comprises the following steps: collecting initial battery electrochemical parameters, obtaining ion migration characteristic data through charge density gradient decoupling, further reconstructing a polarization potential field to generate a lithium ion battery charge-discharge curve, performing potential migration analysis on the curve, mapping the curve into time domain charge mapping data, and calculating the time domain charge mapping data according to the time domain charge mapping data. Voltage distribution data are captured through transient voltage, circuit topology is reconstructed, circuit temperature distribution data are obtained, electric field-temperature field cross locking is carried out to determine a temperature influence coefficient, an initial equivalent circuit model is corrected based on the temperature influence coefficient, charging and discharging simulation is carried out on the corrected model, and aging factors are embedded. And finally generating an equivalent circuit model of the lithium ion battery. The method for constructing the equivalent circuit model of the lithium ion battery is more reliable and more intelligent.

The invention discloses slurry, a battery diaphragm, a preparation method of the battery diaphragm and a battery, and belongs to the technical field of batteries. The slurry comprises the following components: ceramic particles, carbon nanofibers, a binder I and a solvent I, the ceramic particles comprise ceramic particles I and ceramic particles II; the particle size of the ceramic particles I is 0.55-1 [mu] m, and the particle size of the ceramic particles II is 0.05-0.45 [mu] m. The battery diaphragm comprises a base membrane and a composite coating, the composite coating is attached to at least one surface of the base film; the composite coating is formed by curing the slurry provided by the invention. The battery diaphragm provided by the invention has good thermal stability and mechanical strength, the passing rate of the lithium ion battery in coping with safety tests such as extrusion, puncture and heavy object impact is greatly improved, and meanwhile, the electrochemical performance of the lithium ion battery is also improved.

The invention discloses a lithium ion battery state-of-charge estimation method based on F-FOMIAEKF multi-working-condition wide temperature, and the method comprises the steps: calculating an SOC reference value according to a capacity value at a first working condition wide temperature; after the first working condition wide temperature experiment, constructing a data set containing W groups of data points, and deducing an OCV-SOC relational expression by using a polynomial fitting method; establishing a lithium ion battery fractional order second-order equivalent circuit model and a state-space equation; identifying parameters to be identified in the model; and under different working condition wide temperatures, state variables and observation variables in a state-space equation of the fractional-order second-order equivalent circuit model are estimated by adopting an SOC estimation algorithm according to the identified parameters, and an SOC estimation value is obtained. On one hand, a fractional order model is introduced; on the other hand, a filter updating process in the whole estimation process is optimized by introducing a multi-information theory, and in addition, an estimation algorithm is optimized by updating statistical characteristics of process noise and measurement noise in real time by introducing an adaptive theory, so that the accuracy and the stability of SOC estimation are improved.

The invention relates to the technical field of lithium ion batteries, in particular to a lithium ion battery safe optimization rapid charging control method, which comprises the following steps: S1, constructing a lithium ion battery electric heating aging coupling model, determining a model control equation, and establishing an offline charging environment required by reinforcement learning according to the model; s2, initializing a strategy network and two value networks, defining a reinforcement learning state space and an action space, and setting a charging reward function and a cost function; s3, state space variables are updated according to the battery model in the step S1, and the current state, the charging action, the reward value, the cost value and the state at the next moment are added to an experience pool, rapid charging with electric heating perception and health awareness can be achieved, the expression form of a multi-target optimization problem is reconstructed to enhance the safety of a charging strategy, and the method has the advantages of being high in practicability and the like. And meanwhile, an offline training and online deployment mode is adopted, so that the calculation complexity of online charging is reduced, and the method is more efficient in actual charging application.

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

The invention relates to a lithium ion battery health state estimation method based on semi-supervised learning, and the method comprises the steps: obtaining health factors in an original IC curve, and carrying out the preprocessing; constructing a data set based on the preprocessed health factors; wherein the data set comprises a marked sample data set and an unmarked sample data set; using the data set to train a semi-supervised Transform model, and obtaining a battery SOH prediction model; and performing lithium ion battery SOH estimation by using the battery SOH prediction model. According to the method, the effective information of the marked sample is fully mined by utilizing the advantages of the self-attention mechanism in the feature extraction of the time sequence data and the long-range dependence modeling method, the marking and utilization of the unmarked sample are realized through self iteration, and the precise prediction of the SOH of the lithium ion battery is realized.

The invention discloses a lithium ion battery health state estimation method and system, a medium and equipment, and the method comprises the steps: enabling each client of a battery mechanism participating in collaborative modeling to extract a health factor based on the original data of local battery monitoring, constructing and training a deep convolution generative adversarial network, and obtaining local synthetic data; each client uploads the local synthetic data to a central server, and the central server aggregates the local synthetic data of each client to form global synthetic data and distributes the global synthetic data to each client; each client carries out federated transfer learning training based on local data and the global synthesis data, uploads a local model to a central server after local training is finished, carries out dynamic weighted aggregation to form a global model and issues the global model, and the local training and federated aggregation are continuously and iteratively executed until the whole training process is finished; and each client carries out personalized fine tuning on the global model based on local data to realize estimation of the health state of the battery.

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

The invention provides a lithium battery SOC estimation method based on a P2D-LNN fusion model, belongs to the technical field of lithium battery management, and solves the problems that an existing lithium battery SOC estimation method is high in calculation cost and poor in universality and interpretability, and the method comprises the steps that lithium ion battery data are collected, and a lithium ion battery test database is established; constructing a P2D model, and capturing electrochemical dynamics inside the lithium battery in the lithium ion battery test database; performing parameter identification on the lithium ion battery test database, and correcting P2D model parameters; actual current data under the working condition are input into the corrected P2D model, and voltage, temperature, css, cs and bulk data generated through simulation are output; optimizing the LNN model by using a BPTT algorithm, establishing a CNN-LNN model, and performing time sequence prediction on the state of the lithium ion battery; and integrating the P2D model and the CNN-LNN model through a UKF algorithm, establishing an online SOC prediction model, and completing accurate estimation of the SOC of the lithium battery.

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 relates to the technical field of cylindrical lithium ion battery production, and discloses a method for detecting insufficient welding of a cylindrical lithium ion battery, and the method comprises the following steps: S1, appearance inspection; s2, initial internal resistance benchmark test; s3, applying a short-time large-current pulse to the battery by using a pulse generator, simulating an actual load condition, and recording an instantaneous voltage drop and current response curve of the voltage of the battery end during the pulse period; s4, performing infrared thermal imaging identification and data processing; s5, learning the picture and establishing a model; s6, comparing the test picture and the data with the model; s7, dynamic internal resistance calculation; s8, cold solder joint query and sorting processing; and S9, retesting the electrical performance. According to the invention, the technologies of dynamic internal resistance measurement, nondestructive X-ray or ultrasonic detection, rate discharge test and the like are adopted, so that efficient, accurate and nondestructive detection of the cold solder joint of the cylindrical lithium ion battery is realized, and the production efficiency and the detection precision are remarkably improved.

The invention relates to a surface-modified lithium-rich manganese-based positive electrode material, a preparation method and a lithium ion battery, and the preparation method comprises the following steps: uniformly mixing a lithium-rich manganese-based precursor, a lithium source and a metal oxide required by garnet type solid electrolyte to obtain a mixture; and performing gradient temperature calcination on the mixture in an oxidizing atmosphere to obtain the surface-modified lithium-rich manganese-based positive electrode material, the surface-modified lithium-rich manganese-based positive electrode material comprises a lithium-rich manganese-based material, a disordered spinel self-protection layer coated on the surface of the lithium-rich manganese-based material, and a garnet type solid electrolyte coated on the surface of the disordered spinel self-protection layer, the gradient temperature calcination comprises the following steps: calcining at the nucleation temperature of the lithium-rich manganese-based material, and then heating to the nucleation temperature of the garnet type solid electrolyte for calcining. The lithium-rich manganese-based positive electrode material can solve the problems of low first coulombic efficiency and poor rate capability of the lithium-rich manganese-based positive electrode material in related technologies.

The invention discloses a time sequence modeling method for lithium ion battery performance evaluation, and aims to solve the technical challenges of a traditional method in the aspects of multi-scale feature extraction and fusion. The method specifically comprises the steps of obtaining battery capacity data and performing preprocessing; constructing an MTH3 model by combining a time convolutional network and a multi-head attention mechanism on the basis of a Mama model, and performing multi-time scale feature extraction by taking the preprocessed data as input; constructing a NetTDF model based on a U-shaped network, fusing the features extracted by the MTH3 model, and modeling a global relationship; and dividing a training set and a test set, and inputting the training set and the test set into the overall model to obtain a prediction result of the lithium ion battery health state. According to the method provided by the invention, the advantages of each model are fully exerted, an efficient prediction model for the capacity of the lithium ion battery, which can accurately capture local features and effectively model a global relationship, is provided, and reliable support is provided for performance evaluation of the lithium ion battery.