26999 results about "Lithium 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 discloses an electric bicycle lithium battery data real-time monitoring and early warning system and method, and relates to the technical field of battery management, and the system comprises a multi-dimensional data collection module which is used for obtaining a multi-source heterogeneous data set of a lithium battery system; the collaborative feature extraction module is used for generating a comprehensive evaluation parameter set; the dynamic threshold generation module is used for constructing a self-adaptive early warning boundary model according to the comprehensive evaluation parameter set; the intelligent decision module is used for generating a hierarchical control instruction set based on a multi-objective optimization algorithm; and the cloud collaboration module is used for synchronizing the hierarchical control instruction set to the edge computing node and the cloud management platform, and triggering a multi-level linkage protection mechanism based on the game theory when the thermal runaway risk index is detected to exceed a first dynamic threshold value. According to the electric bicycle lithium battery data real-time monitoring and early warning system and method provided by the invention, the safety and reliability of a battery system are improved.

The invention relates to a lithium battery energy storage system fire-fighting ventilation and explosion venting safety assessment method based on multi-dimensional simulation. The method comprises the following steps: constructing an energy storage system digital twinborn model fusing structure parameters, material attributes and environmental parameters; generating a multi-mode failure scene set covering multiple temperature domains and aging states through mode recognition; simulating and quantifying dynamic interaction of a temperature field, a flow field and a stress field in the thermal runaway evolution process based on thermal-fluid-solid multi-physics field coupling; constructing a space-time associated dynamic safety evaluation matrix, and combining fuzzy comprehensive evaluation and Monte Carlo sampling to generate risk quantitative indexes; and iteratively correcting parameters of the fire-fighting ventilation and explosion venting system through a multi-objective optimization algorithm to form a graded safety assessment conclusion. According to the method, the technical bottlenecks of environmental parameter splitting and single failure scene in a traditional method are broken through, the thermal runaway suppression efficiency is improved, the combustible gas concentration control error is reduced, and collaborative optimization of explosion venting pressure fluctuation suppression and ventilation response is realized through a closed-loop evaluation mechanism.

The invention discloses a lithium battery health state prediction method and system based on multi-parameter fusion, and relates to the technical field of lithium batteries, and the method comprises the following steps: collecting multi-dimensional parameter data in a lithium battery operation process, preprocessing the multi-dimensional parameter data, and constructing a time sequence data set; extracting multi-source degradation characteristics from the time sequence data set, and generating degradation influence factors; building a hybrid model based on an LSTM network and a Transform encoder, inputting the degradation impact factors into the hybrid model for model training until the model converges, and obtaining a lithium battery health state prediction model; and performing health detection on a to-be-detected lithium battery based on the lithium battery health state prediction model, and outputting an SOH prediction value. Multi-source battery parameters can be fused to predict the health state of the lithium battery, and the prediction precision and the real-time performance are improved.

Systems, methods, and articles for a portable power case are disclosed. The portable power case is comprised of at least one battery and at least one PCB. The portable power case has at least one USB port and at least two access ports, at least two leads, or at least one access port and at least one lead. The portable power case is operable to supply power to an amplifier, a radio, a wearable battery, a mobile phone, and a tablet. The portable power case is operable to be charged using solar panels, vehicle batteries, AC adapters, non-rechargeable batteries, and generators. The portable power case provides for modularity that allows the user to disassemble and selectively remove the batteries installed within the portable power case housing.

The invention relates to the technical field of battery detection, and particularly provides a battery fault early warning and diagnosis method, which comprises the following steps: acquiring operation parameters of a battery pack in real time, processing the operation parameters based on a preset judgment condition, generating a corresponding preprocessing signal according to a noise environment state, and extracting spatial-temporal characteristics to perform weak signal enhancement processing, so as to obtain a battery fault early warning and diagnosis result. Generating an enhanced feature set; based on the enhanced feature set, performing space-time fusion calculation according to a preset weight relation to obtain a fault energy accumulation value; dynamically correcting a fault judgment threshold according to the health state of the battery and the real-time environment parameters; and when the fault energy accumulation value exceeds the fault judgment threshold after dynamic correction, outputting a graded early warning signal. By capturing weak fault features of the battery pack and combining spatial domain feature extraction of temperature difference and voltage distribution and a signal enhancement technology, the detection sensitivity of early hidden faults is improved, and in a lithium battery safety early warning scene, the fault detection time is shortened, the false alarm rate is reduced and the like.

A lithium battery includes: a housing in which an accommodation space is formed; a core disposed in the accommodation space; a negative cover disposed at a first end of the housing; a positive cover disposed at a second end of the housing; a positive current collecting disk disposed on a side of the positive cover close to the core; and a support member disposed between the positive cover and the positive current collecting disk and abutting against the positive current collecting disk.

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 battery health state assessment method and system, and particularly relates to the technical field of battery health state assessment. The method comprises the following steps: performing time sequence alignment and structured preprocessing on multi-source operation data of a target lithium battery in a plurality of historical work cycles to construct a structured data set; constructing a spatial-temporal characteristic residual error map based on residual error mapping analysis, and extracting a spatial heterogeneity index; in combination with a spatial heterogeneity index, generating regional degradation feature mapping; through high-dimensional feature embedding and evolution path clustering, a heterogeneous aging mode is identified, and a classification result is generated; evaluating the health state grade of the target lithium battery according to the regional degradation characteristic mapping and heterogeneous aging mode classification result; whether the battery has a local potential thermal runaway risk or not is judged based on the evaluation result, and a corresponding risk early warning signal and a safety disposal suggestion are generated, so that the nonlinear influence of the lithium battery aging heterogeneity can be accurately identified, and the health state evaluation precision and the safety risk early warning capability are effectively improved.

The invention relates to the technical field of ship power systems, and discloses a ship comprehensive power system based on composite energy storage, comprising an energy generation module which comprises a diesel generator, a gas turbine and a renewable energy sub-module and is used for generating electric energy; the energy storage management module comprises a lithium battery, a super capacitor and a flywheel energy storage system and is used for storing and releasing electric energy; the energy distribution and scheduling module is used for controlling energy distribution between the energy generation module and the energy storage management module; the load management module is used for adjusting the load power requirement according to the load priority; and the health management and fault monitoring module is used for monitoring the operation states of the energy storage management module and the system in real time. Through cooperative work of multiple energy storage units and combination with optimization control of the energy distribution and scheduling module, characteristics of different energy storage devices are fully exerted. Therefore, loss in the energy conversion process is remarkably reduced, distribution and utilization of electric energy are optimized, and the overall energy utilization efficiency of the system is improved by about 15%-20%.

The invention relates to the technical field of lithium battery management, in particular to a lithium battery pack dynamic balancing method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: predicting battery cell state parameters of each battery cell based on a preset long-short-term memory neural network model to obtain a capacity attenuation trend of each battery cell; building a health state evaluation index based on the capacity attenuation trend; based on the health state evaluation indexes, performing health grade classification on each battery cell by adopting a preset clustering algorithm; obtaining the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell, and determining the balance priority of each battery cell based on the health grade category division result, the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell; and on the basis of the equalization priority, a target dynamic equalization adjustment strategy is generated, and each battery cell is adjusted according to the target dynamic equalization adjustment strategy, so that the energy scheduling accuracy of the lithium battery pack is 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 present invention belongs to the field of lithium battery technology, and specifically discloses a method and system for predicting the life and fault of lithium batteries for new energy vehicles based on deep learning. The method includes the following steps: collecting and preprocessing the voltage, current, temperature, number of cycles and usage time data sets of lithium batteries, building a hybrid deep learning model based on convolutional neural network CNN and long short-term memory network LSTM, training the hybrid deep learning model, and obtaining an optimized hybrid deep learning model; collecting the voltage, current, temperature, number of cycles and usage time information of the lithium battery to be predicted, inputting the optimized hybrid deep learning model after preprocessing, and outputting the prediction results of the life and fault of the lithium battery. By adopting this technical solution, by building an efficient deep learning model, the spatiotemporal characteristics in the battery operation data are fully utilized to improve the accuracy, timeliness and reliability of the prediction, and at the same time enhance the adaptability of the model to different batteries and working conditions.

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 dynamic control method of a lithium battery module intelligent equalization management system, and relates to the field of lithium battery module equalization, and the method comprises the steps: constructing a hierarchical data fusion platform, and generating a battery state data set containing SOC, SOH and an inconsistency evaluation value; a credibility reasoning algorithm is adopted to calculate a single body imbalance credibility factor value to divide an equalization stage, and equalization current is dynamically adjusted; a charge and discharge curve health factor is extracted through an LAB health factor algorithm, SOH is updated online in combination with extended Kalman filtering, and an SOH attenuation rate is fed back to the battery model; in-module equalization is carried out through a bidirectional energy transfer circuit, inter-module equalization is carried out through a ring bus topology, and layered topology linkage control is formed. The method has the advantages that the consistency and the heat management efficiency of the battery pack are improved through the hybrid equalization strategy and the layered topology linkage control, the service life of the battery pack is prolonged, and the overall performance and the safety of the electric vehicle are improved.

The invention relates to the technical field of lithium battery safety monitoring, and discloses a lithium battery pack thermal runaway early warning system based on multi-mode sensing. The system comprises multi-source sensing data acquisition, thermal field feature tensor construction, thermal field reconstruction and thermal coupling association network generation. The multi-source sensing data acquisition module acquires multi-dimensional heterogeneous data from a temperature sensor, a voltage and current monitoring unit, a gas component detector and an acoustic emission sensor, and generates a standardized data bin through timestamp alignment and missing value compensation; the thermal field feature tensor construction module extracts features such as temperature gradient and electrochemical response from the data bin in a multi-scale manner, and constructs a tensor in combination with time continuity; the thermal field reconstruction module generates association diagrams according to the feature space-time distribution and fuses the association diagrams into a lithium battery pack three-dimensional thermal field reconstruction map; and the thermal coupling association network generation module extracts a feature vector cluster, calculates an entropy weight value, and generates a network according to a high-entropy node space adjacency relationship. According to the system, multi-dimensional data fusion is realized, and the thermal runaway evolution law can be comprehensively described.

The invention relates to the technical field of solid-state lithium batteries, in particular to a cellulose acetate modified PEO-based solid-state electrolyte membrane, a solid-state lithium battery and a preparation method thereof.The preparation method comprises the steps that lithium bis (trifluoromethanesulfonyl) imide, cellulose acetate and polyethylene oxide are dissolved in a solvent, and a mixed solution is obtained; and curing the mixed solution in a mold to obtain the cellulose acetate modified PEO-based solid electrolyte membrane. According to the invention, the cellulose acetate is introduced to modify the polyethylene oxide solid electrolyte, and the prepared cellulose acetate modified PEO-based solid electrolyte membrane can generate a stable solid electrolyte interface rich in Li2O and LiF on the surface of a lithium negative electrode in situ in the application of a solid lithium battery, so that the growth ability of lithium dendrites is effectively inhibited, and the performance of the lithium battery is improved. The interface impedance is reduced, and the operating temperature range of the solid-state lithium battery is widened.

The invention discloses a lithium battery life prediction method and system based on a graph neural network, and the method comprises the steps: obtaining the charge-discharge cycle data of a plurality of historical periods of a lithium battery, and extracting the capacity data of the battery according to the charge-discharge cycle data; segmenting the battery capacity data to obtain a plurality of sequence samples corresponding to the time sequence, and dividing the sequence samples into a training set and a test set according to preset time nodes to train a preset neural network model; self-adaptively adding white noise into the battery capacity data, and amplifying the non-correlation degree among all modes so as to decompose the modes of the battery capacity data; and constructing a graph neural network, combining the adjacent matrix, graph convolution and an attention mechanism to obtain a lithium battery life prediction model, and predicting the residual life of the lithium battery through the lithium battery life prediction model. The problems that the lithium battery life prediction precision is insufficient and the prediction precision improvement is limited in the prior art are solved.

The invention belongs to the technical field of SOC and SOH estimation, and particularly relates to a park lithium battery SOC and SOH joint estimation method and device, and the method comprises the steps: collecting the multi-dimensional feature data of a lithium battery in the charging and discharging process, extracting a health factor, and carrying out the preprocessing of the data; constructing a CNN-BiLSTM model, combining the CNN-BiLSTM model with a LightGBM model, constructing a joint estimation model, and training the joint estimation model; and inputting the health factor into a CNN-BiLSTM model to estimate the SOH, and inputting the multi-dimensional feature data and an estimated value of the SOH into a LightGBM model to estimate the SOC, thereby obtaining a final SOC and SOH joint estimated value. According to the method, the time sequence features of the SOH are modeled through the BiLSTM and the CNN, and SOC estimation is performed in combination with the LightGBM, so that SOC calculation can dynamically consider the change of the SOH, and accumulation of SOC estimation errors is reduced.

The invention relates to the technical field of lithium battery management, and discloses a cloud-based lithium battery management method and system, and the method comprises the steps: constructing a cloud collaborative management framework, and obtaining a dynamic adjustment factor and a degradation compensation coefficient; performing multi-dimensional state joint estimation, and predicting the health state and the residual life of the battery; a dynamic balance control strategy is set, and energy differences among the monomers are compensated; and closed-loop feedback and model iteration are carried out. The system comprises a distributed data acquisition unit, an edge computing unit, a cloud analysis platform, a communication gateway and a balance execution unit. According to the method, technologies such as cloud collaborative management, multi-dimensional data processing, dynamic balance control and model iterative optimization are utilized, accurate management of the lithium battery is realized, the battery state prediction accuracy can be improved, the service life of the battery can be prolonged, the system safety and stability can be enhanced, and the method is suitable for various lithium battery application scenes.

The invention discloses a lithium battery charge state estimation method based on a Bayes-TLCO optimization deep neural network, and belongs to the technical field of battery state monitoring. The method comprises the following steps: firstly, preprocessing a lithium battery charging and discharging data set; then, constructing a deep neural network model comprising a convolutional neural network, a long-short-term memory network and a multi-head attention mechanism, dynamically optimizing hyper-parameters of the model by using a Bayesian optimization-assisted termite life cycle optimization algorithm, introducing Bayesian optimization during iteration stagnation in a TLCO algorithm iteration process, and finally obtaining a termite life cycle optimization model; fitting historical data through a Gaussian process to construct a search empirical model, generating high-value sampling points, and accelerating model hyper-parameter convergence to a globally optimal solution; and finally, estimating the state of charge of the lithium battery. The method breaks through the limitation of a single algorithm, achieves the high-precision estimation of the state of charge of the lithium battery under a complex working condition, effectively improves the model training efficiency, is suitable for electric vehicles, energy storage systems and other scenes, and provides a key technical support for the intelligent upgrading of battery management.

The invention discloses a lithium iron phosphate battery pack SOE dynamic prediction and life evaluation method and system fused with a P2D model, and relates to the technical field of battery management, and the method comprises the steps: collecting operation data, building a multi-physical field coupled P2D model, carrying out the SOE dynamic prediction based on the P2D model, building a life evaluation model in combination with a performance attenuation mechanism, and optimizing an operation strategy. According to the method, on the basis of historical operation data and real-time state parameters, an SOE dynamic prediction result of the battery pack is generated by using an electrochemical-thermal-mechanical multi-physics field coupled P2D model; and analyzing the prediction result in combination with a long-term performance attenuation mechanism, evaluating the residual life of the battery pack, and outputting an evaluation result. The SOE prediction precision is improved, the performance change rule is comprehensively reflected by using the life evaluation model, and the operation efficiency is remarkably improved and the service life is prolonged through real-time monitoring and optimization. According to the invention, efficient and reliable battery management requirements under complex working conditions can be met.

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 an intelligent preparation and coating method for lithium battery electrode slurry in the technical field of information, which comprises the following steps: acquiring particle size distribution, specific surface area and chemical composition attribute parameters of a lithium battery electrode slurry raw material, and establishing a quantitative relation model among the slurry component proportion, rheological property and stability through an autonomous learning algorithm; adjusting the component proportion by adopting a dynamic optimization algorithm to obtain a slurry formula meeting a target attribute; feature extraction and classification are performed on the constructed data set, a decision tree algorithm is adopted to train and generate optimization rules and judgment logic of slurry preparation process parameters, a preparation process knowledge base is formed, and autonomous optimization of the process parameters is realized; aiming at the coating quality problem, slurry preparation process parameters are traced and analyzed, collaborative optimization of a slurry formula, a preparation process and coating parameters is realized through a human-computer interaction interface, and the production efficiency and the yield are improved while the electrode performance is ensured.

The invention provides a lithium battery equalization thermal management method. Real-time temperature data is obtained by constructing a thermal distribution monitoring network, the temperature difference between a center and an edge area is analyzed, a thermal load distribution coefficient is calculated, and a dynamic heat dissipation intensity adjustment model is established; driving the multi-channel cooling system to operate according to the heat dissipation demand priority, and adjusting the flow distribution proportion of a cooling medium; a dynamic nonlinear feedback correction mechanism is adopted, secondary adjustment is carried out for uneven temperature distribution, and an optimal heat dissipation parameter combination is determined in combination with historical data. Uniformity control over temperature distribution of the battery module is achieved, the problem of heat accumulation in a center area is effectively solved, the heat dissipation efficiency under the rapid charging working condition is improved, and the safety performance and the service life of the battery module are guaranteed.

The invention discloses a series lithium battery pack power supply equalization management method. The method comprises the following steps: acquiring an original data set of a series lithium battery pack; calculating a voltage change rate, a temperature sudden rise rate and an internal resistance fluctuation amplitude, and obtaining a dynamic feature set of battery operation; when the dynamic feature set exceeds a corresponding preset threshold value, obtaining a short-circuit risk assessment result; matching a pre-established battery pack topological structure diagram with the short-circuit risk assessment result, determining a target battery identifier needing to be isolated, generating an isolation instruction by adopting a relay control algorithm, and completing the isolation of the short-circuit battery; adjusting the charging current distribution of the updated battery pack through an equalization management algorithm to obtain equalized battery operation parameters; and if the voltage, temperature and internal resistance data of the residual single batteries are all in a normal range, determining that the system stability is recovered, and obtaining a final running state of the battery pack. The short-circuit risk of the lithium battery pack can be effectively prevented, and intelligent management and safety improvement of the battery pack are realized.

The invention provides a lithium battery health state prediction method and system based on fusion health indicators, and the method comprises the steps: obtaining lithium battery charging and discharging parameters and a data set, extracting indirect health indicators for reflecting the degradation characteristics of a battery, and verifying the reasonability of the indirect health indicators through a correlation analysis method; performing dimension reduction processing on the indirect health index by using a KPCA method to obtain first principal component data, constructing a lithium battery health state comprehensive estimation model, and further obtaining a battery health state; and decomposing an original battery capacity sequence into a local fluctuation sequence and a global degradation sequence, performing prediction by using a radial basis function neural network and Gaussian process regression, and performing superposition to obtain a battery residual life prediction value. Aiming at the problems of low accuracy, unstable model and the like in the prior art, the battery health state is measured and calculated by adopting a mode based on the fusion health indexes, the method has the advantages of high estimation precision, good application prospect and the like, and the prediction accuracy is effectively improved.

The invention relates to the technical field of SOC estimation, in particular to an SOC estimation method and system for a lithium battery pack. The method comprises the following steps: collecting basic parameters and voltage data of a lithium battery pack, generating steady-state voltage data by using singular value decomposition and time domain reconstruction, further performing multi-level feature mapping and performance reconstruction to obtain basic discharge data, calculating battery energy efficiency and deducing normal power consumption based on the data, and calculating the energy efficiency of the lithium battery pack. Discharging simulation is carried out to evaluate the temperature rise condition, discharging capacity attenuation is predicted in combination with working environment parameters, relative errors are calculated, finally, the charge state is corrected, electric quantity self-calibration is carried out, and intelligent state estimation of the lithium battery pack is achieved. According to the invention, a more efficient SOC estimation method is realized.

The invention relates to a lithium battery health degree detection method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the lithium battery health degree detection method comprises the steps: evaluating vehicle basic features according to vehicle source data, and obtaining a battery feature data table; taking the charging state of the battery as a research object, analyzing a charging segment, determining a sampling time threshold value, and judging data missing and interpolation filling according to the residual charge of the battery; calculating the battery capacity based on the charge charging time, and analyzing the battery capacity transversely and longitudinally to obtain an initial capacity value; extracting effective capacity data in all the initial capacity values according to a preset travel constraint condition; training a single-pack health degree evaluation matrix or a double-pack health degree evaluation matrix, predicting a charging fragment sequence, and constructing a battery health degree evaluation model; the single-pack health degree evaluation matrix or the double-pack health degree evaluation matrix is corrected, an optimal health degree evaluation weight matrix is obtained, and lithium battery health degree detection is completed in combination with a physical compensation mechanism; according to the method, upward fluctuation of the SOH is avoided, and the SOH estimation precision and the model adaptability are improved.

The invention relates to a lithium battery electrochemical impedance spectroscopy online measurement and estimation method and system based on a charging pile, and belongs to a battery detection technology. The system comprises a measurement module, an estimation module and a result output module. The measurement module comprises a signal generation sub-module, a waveform amplification sub-module and a response processing sub-module; a pulse control signal is inserted into the signal generation sub-module during charging, and is amplified into a measurement excitation signal by the waveform amplification sub-module to be applied to a battery; the response processing sub-module samples the response signal and calculates an impedance spectrum. The estimation module comprises a neural network sub-module and an impedance spectrum estimation sub-module; the neural network sub-module is used for training a physical information neural network by using impedance response to output Randles circuit model parameters to the estimation sub-module, and the change of the electrochemical impedance spectrum is predicted. And the result output module displays and stores the measured and estimated values of the impedance spectrum. According to the invention, the impedance values of the to-be-measured battery pack under multiple frequencies can be measured and estimated on line, and the estimation of the impedance spectrum is physically interpretable.