15230 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 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 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 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 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 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 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 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.

The invention relates to the technical field of lithium ion batteries, and provides a lithium cobalt oxide composite material which comprises a core body and a coating layer coating the core body, the core body comprises O2-phase lithium cobalt oxide, and the O2-phase lithium cobalt oxide is doped with an element M; the coating layer comprises an oxide containing an N element; the M element comprises at least one of Al, Mg, W, Ti, La, Zr, Ni, Mn and Y; n comprises at least one of Al, Mg, Y, W, Nb, La, Zr, Ti and P. The lithium cobalt oxide composite material provided by the invention is used as a positive electrode material of a lithium ion battery, and the cycling stability of the lithium ion battery under high potential is improved.

The invention provides a lithium-rich lithium ferrite material and a preparation method and application thereof, the lithium-rich lithium ferrite material comprises a core layer and a carbon coating layer, the core layer is doped lithium-rich lithium ferrite, the chemical formula of the doped lithium-rich lithium ferrite is Li < 5 > Fe < 1-x > Al < x > O < 4 >, and x is 0.003-0.030. The lithium-rich lithium ferrite material is high in structural stability and excellent in conductivity; as a lithium supplement additive of a positive electrode active material, the composite material can improve electrochemical performance such as high-temperature storage performance and normal-temperature cycle performance of a lithium ion battery.

The invention belongs to the technical field of lithium ion battery state prediction, and particularly relates to a Transform-based lithium battery health state estimation method. Comprising the following steps: carrying out abnormal value processing and feature extraction on a battery aging data set to obtain feature factors; calculating the relevancy between the characteristic factors and the original capacity sequence of the lithium ion battery, and obtaining the characteristic factors meeting a preset relevancy threshold value; decomposing the original capacity sequence based on a completely adaptive noise set empirical mode decomposition algorithm to obtain intrinsic mode functions of different frequencies; combining sin chaotic mapping, an adaptive dynamic weight factor and a reverse learning-Cauchy alternate variation strategy to improve a sparrow search algorithm, and optimizing the trained Transform model by using the improved sparrow search algorithm to obtain a lithium ion battery health state estimation model; and estimating the health state of the lithium ion battery. The method can accurately estimate the health state, and facilitates the user to optimize the use of the battery.

The invention provides an energy storage system power distribution method based on battery pack health state prediction, and the method comprises the steps: constructing a hybrid neural network model for predicting the health state of a lithium ion battery through combining a convolutional neural network and a long short-term memory network, and optimizing the parameters of the hybrid neural network model through employing a Bayesian algorithm. The battery pack health state prediction precision of the model is improved; in combination with the provided lithium ion battery prediction model, a battery pack charging / discharging priority ranking rule based on an entropy weight method is provided; a battery pack power optimization distribution strategy is established by taking the consistency of the state of charge of the battery pack as an objective function and taking the state of charge and the state of health of the battery pack as evaluation indexes, so that energy optimization distribution of the energy storage system is realized, the service life of an old battery is prolonged, overcharge and overdischarge of a single battery are avoided, and balance of the state of charge of the battery pack is realized.

The invention provides a lithium ion battery energy storage cabinet fire early warning system based on multi-source data fusion, and belongs to the technical field of fire early warning. According to the system, through multi-source sensor data fusion and deep learning analysis, the state of a battery in the energy storage cabinet is monitored in an all-around mode, the accuracy and reliability of thermal runaway early warning are improved, and operation safety of energy storage facilities is guaranteed; the system comprises a sensor module, a data acquisition module, an image acquisition module, an edge calculation module, a sound-light alarm module and a remote monitoring module. The lithium ion battery energy storage cabinet fire early warning system has the advantages that the deep learning and multi-source data fusion technology is adopted by the lithium ion battery energy storage cabinet fire early warning system; according to the invention, the arrangement scheme of the industrial camera is innovatively designed; according to the invention, a multi-scale-based early warning evaluation framework is innovatively designed; according to the invention, an edge computing architecture is adopted and an Internet of Things cloud platform is combined, so that an integrated solution from data acquisition, preprocessing and deep learning analysis to remote monitoring is realized.

The invention provides a special lithium ion battery system for a UPS (Uninterrupted Power Supply) based on intelligent management, and relates to the technical field of UPSs, the system comprises a battery state monitoring and predicting module, an intelligent charging management module and a fault diagnosis and fault-tolerant control module of a battery system, and the SOH and SOC of a battery are accurately predicted through a support vector machine, a random forest and a particle filter algorithm. A basis is provided for reasonable charging and discharging, the service life of the battery is prolonged, the intelligent charging management module dynamically adjusts charging parameters according to the SOC and the temperature of the battery through a fuzzy logic control algorithm, balance of rapid charging and battery protection is achieved, the intelligent charging management module adapts to different environments, and the fault diagnosis and fault-tolerant control module detects faults in time with the help of a support vector machine algorithm. A model prediction control algorithm optimizes control input when a fault occurs, it is ensured that the UPS system still operates normally under the battery fault, a data acquisition and transmission module and a system monitoring and display module monitor the battery state and provide historical data query analysis, and a user can conveniently master the battery condition and make a decision.

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 discloses a silicon negative electrode material as well as a preparation method and application thereof, and belongs to the technical field of lithium ion batteries. The silicon negative electrode material comprises a silicon material substrate and a coating layer coating the surface of the silicon material substrate, the coating layer comprises cyclized polyacrylonitrile and a fast ion conductor. According to the silicon negative electrode material provided by the invention, a uniform cyclized polyacrylonitrile and fast ion conductor coating layer is constructed on the surface of a silicon material matrix. The good electronic conductivity and elasticity of the cyclized polyacrylonitrile and the excellent ionic conductivity of the fast ion conductor are beneficial to buffering the volume expansion of the silicon carbon material, accelerating the lithium ion transmission kinetics and effectively improving the rate capability of the negative electrode material; the cyclized polyacrylonitrile has high chemical stability and strong compatibility with the silicon material and the sulfide solid electrolyte, and is used as a buffer layer between the silicon material and the sulfide solid electrolyte to avoid direct contact between the silicon carbon material and the sulfide solid electrolyte, inhibit interface side reaction and effectively improve the cycle stability of the battery.

The invention provides a lithium ion battery and electric equipment, the lithium ion battery comprises a positive pole piece and an electrolyte, active substances in the positive pole piece comprise a phosphate lithium positive pole material and nickel cobalt manganese lithium oxide, and the electrolyte comprises a positive pole film-forming agent, a negative pole film-forming agent and a lithium salt, the lithium salt comprises lithium hexafluorophosphate and lithium bis (fluorosulfonyl) imide, the lithium ion battery meets the following formula: # imgabs0 #, NL is the mass ratio of the lithium nickel cobalt manganese oxide to the lithium phosphate positive electrode material, Salt is the sum of the molar concentrations of the lithium hexafluorophosphate and the lithium bis (fluorosulfonyl) imide in the electrolyte by taking mol / L as the unit, and the sum of the molar concentrations of the lithium hexafluorophosphate and the lithium bis (fluorosulfonyl) imide in the electrolyte is the sum of the molar concentrations of the lithium hexafluorophosphate and the lithium bis (fluorosulfonyl) imide. Add is the mass ratio of the positive electrode film-forming agent to the negative electrode film-forming agent. When the ternary material is doped in the lithium manganese iron phosphate or the lithium iron phosphate, the proportion of the positive and negative electrode film-forming agents and the proportion of the heat-resistant high-lithium salt can be adjusted according to the mixing proportion of the lithium manganese iron phosphate and the lithium iron phosphate, so that the cycle performance of the battery is improved.

The invention discloses an electrolyte with a temperature switch function and a preparation method and application thereof, and relates to the technical field of lithium ion batteries. The electrolyte provided by the invention comprises the following components: lithium salt, a first organic solvent and a polymer, the lithium salt is at least one of lithium bis (trifluoromethylsulfonyl) imide, lithium bis (fluorosulfonyl) imide, lithium hexafluorophosphate, lithium tetrafluoroborate, lithium difluoro (oxalato) borate and lithium bis (oxalato) borate; the polymer is polymethacrylate, and the first organic solvent is [Cnmim] [TFSI]. According to the electrolyte, the reversible phase transformation process is realized, the electrode reaction can be closed at high temperature, and the lithium battery is prevented from being out of control due to overheating; and when the temperature is reduced to normal temperature, the battery can work normally. The phase transition temperature of the electrolyte is flexible and controllable, and the phase transition temperature can be regulated and controlled to a reasonable interval by optimizing the composition of the electrolyte and the dosage (concentration) of each component, that is, the phase transition temperature is higher than the normal working temperature of the battery and lower than the initial temperature of the heat release side reaction.

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 relates to a temperature control technology of an energy storage system, and discloses a temperature control system and method of a lithium ion battery energy storage system, which can realize accurate temperature control, reduce the power consumption of temperature control and reliably guarantee high-efficiency and long-life operation of the lithium ion battery energy storage system. The temperature control system comprises a battery management system, a heat management system and a battery temperature sampling unit, the battery temperature sampling unit is used for sampling the temperature of each battery cell in the energy storage system in real time; the battery management system is used for generating a temperature control strategy according to a preset temperature feature point in combination with the current temperature data of the battery cell and the current working state of the energy storage system, and issuing the temperature control strategy to the thermal management system; the temperature characteristic point is preset according to characteristic data of a battery cell used in the energy storage system; and the heat management system is used for controlling the temperature of the energy storage system by executing a temperature control strategy. The method is suitable for the energy storage system.

The invention relates to the technical field of lithium ion battery silicon-carbon coating, in particular to a silicon-carbon composite material, a silicon-carbon negative electrode material, a preparation method of the silicon-carbon negative electrode material and a battery. The preparation method of the silicon-carbon composite material comprises the following steps: S1, mixing porous carbon and a gaseous organic silicon source for reaction under the conditions of 450-550 DEG C and 100-200 kPa; s2, under the condition of 500-800 DEG C, carrying out carbon coating on the product obtained in the step S1 by using a carbon source to obtain a silicon-carbon composite material; the flow velocity of the gaseous organic silicon source is 12-30L / min. The silicon-carbon composite material provided by the invention has good conductivity, and the subsequently prepared silicon-carbon negative electrode material and battery material have good electrochemical performance.

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.

The invention discloses a method and a system for predicting a temperature field of a high-capacity energy storage lithium ion battery. The method comprises the following steps: acquiring temperature values of a few temperature measuring points of the lithium ion battery to be predicted; inputting the obtained temperature value into a preset physical information-neural network model for prediction to obtain a temperature field of the lithium ion battery cell to be predicted; wherein the preset physical information-neural network model comprises a basic neural network part and a physical information fusion part, and the physical information fusion part adds a battery heat transfer differential equation and a battery boundary condition as physical information constraints in a neural network training process; the loss function in the neural network comprises boundary loss, partial differential equation loss and observation data set loss. The system comprises a data acquisition module and a data processing module. According to the method, the neural network algorithm is combined with the lithium battery heat transfer model, and temperature field inversion of the high-capacity battery is realized.

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.