1675 results about "Battery capacity" patented technology

The invention discloses a vehicle idling start and stop control system and method. The control system judges whether a vehicle has idling start and stop conditions by monitoring the water temperatureof an engine, the capacity of a storage battery, a safety belt and other information, judges whether the vehicle has conditions of stopping the engine by monitoring the rotating speed of the engine, the speed of the vehicle, the brake air pressure and other conditions, and judges whether the vehicle has conditions of starting the engine by monitoring the gear of a gearbox, the speed of the vehicle, a hand brake and other information. When the relevant conditions are met, start and stop work signals are sent to the engine though a CAN bus. In order to guarantee the reliability of frequent startand stop of the engine, the system strengthens flywheel gear rings of a starter and the engine. In order to ensure that the battery does not lose power, a storage battery capacity sensor is added tothe system on the basis of increasing the capacity of the storage battery, and the idling start and stop are allowed when the state of charge (SOC) of the storage battery satisfies a safe starting threshold. The idling start and stop control system has advantages of low cost and high reliability, and is especially suitable for commercial vehicles.

The invention discloses a storage battery capacity attenuation trend prediction method, and belongs to the technical field of storage battery prediction. By collecting voltage, current and temperature data of each monomer in real time and combining historical capacity attenuation and internal resistance growth data, the method identifies a voltage and capacity difference value, evaluates cyclic stress non-uniform distribution, and determines a current sharing proportion and a load unbalance degree. Identifying an abnormal mode of new battery overload and aged battery deep discharge, constructing a mixing abnormal working condition identification mode, if the unbalance degree exceeds the standard, adaptively adjusting the charging and discharging time and the current switching frequency, establishing a load balance control framework, predicting the capacity attenuation rate and the residual cycle index of each monomer, and determining the capacity matching degree and the life matching degree; finally, a comprehensive residual life estimation value and a credibility interval are generated through fusion; the performance balance of the mixed battery pack is remarkably improved, the overall service life is prolonged, and the method is suitable for real-time monitoring of a battery management system.

The invention discloses a lithium battery residual life prediction method and system and terminal equipment, and relates to the technical field of lithium battery health management. The method comprises the following steps: receiving a battery capacity attenuation sequence as an original input sequence, detecting and filtering abnormal data by adopting a 3 sigma criterion, and carrying out noise suppression processing on the battery capacity attenuation sequence through a Dropout mask; and carrying out normalization processing on the preprocessed battery capacity attenuation sequence, dividing the battery capacity attenuation sequence into a training set, a verification set and a test set through a sliding window algorithm, and generating a time sequence characteristic matrix and a corresponding residual service life label. According to the method, a neural network structure fusing trend prior perception and dynamic attention regulation is constructed, a multi-scale capacity modeling strategy is introduced to separate a degradation trend, fluctuation disturbance and high-frequency noise, and compared with a traditional time sequence neural network or a single attention model, pseudo fluctuation characteristics caused by capacity regeneration can be more effectively recognized, and the method is more efficient and more reliable. And the judgment accuracy of the model in a complex degradation scene is improved.

The invention provides an energy storage power station optimization operation mode decision-making method and system, and relates to the technical field of energy storage power station optimizing.A hybrid prediction model is constructed to realize high-precision decomposition prediction of power load, and meanwhile, the internal resistance of a battery is estimated in real time by adopting a recursive least square method; and the battery capacity and internal resistance parameters are dynamically corrected in combination with a temperature compensation mechanism. Through health state multi-index fusion evaluation, self-adaptive distribution of charging and discharging power is achieved, and compared with the prior art, the problem that a traditional static model cannot adapt to complex environment changes is solved. The battery capacity fading risk can be predicted in advance by introducing a double-compensation mechanism of an environmental influence index and an electric power influence index. According to the scheme, the response speed and economical efficiency of energy storage in a high fluctuation load scene are remarkably improved, and a reliable dynamic optimization decision support system is provided.

The invention relates to the technical field of energy storage systems, and provides an energy storage system capacity configuration optimization method based on battery capacity attenuation trajectory prediction. The method comprises the following steps: acquiring operation state data of a battery module in the energy storage system, establishing a historical operation database and a battery capacity attenuation trajectory prediction model, calculating an attenuation trajectory of battery capacity along with time through a temperature accelerated aging factor and a cyclic aging factor, and obtaining a capacity attenuation prediction curve in a future time period; and establishing a capacity configuration optimization function taking net present value maximization as a target by combining a load demand curve and an economic index of the energy storage system, dynamically adjusting charge and discharge depth limitation and a power distribution proportion, dynamically adjusting charge and discharge power of each battery module according to a real-time capacity state, and realizing optimized operation of the energy storage system. According to the invention, accurate prediction of battery capacity attenuation and optimization of full life cycle capacity configuration are realized, and the economic benefit and operation reliability of the energy storage system are improved.

The invention relates to the technical field of battery management, in particular to a battery replacement cabinet battery real-time monitoring and scheduling method, the health state of a battery is analyzed and evaluated according to a preset algorithm model, and the health state of the battery is evaluated by comparing a comprehensive health score, an internal resistance change rate and a capacity fading rate with corresponding threshold values; and determining a corresponding processing mode based on the evaluation result, including generating and sending abnormal alarm information to the operation and maintenance terminal, or executing an adjustable charging mode according to the state of residual electric quantity of the battery, or executing a protective charging mode according to the deterioration degree of the battery, the health state of the battery is actively evaluated to accurately identify an abnormal battery, for example, the electric quantity may be not low but potential safety hazards exist, in advance, so that targeted charging scheduling can be realized. According to the invention, real-time monitoring and active protection of the health and safety state of the battery are realized, so that the operation efficiency is improved on the premise of ensuring the safety.

The invention discloses a storage battery capacity prediction method based on multi-source asynchronous perception and time hybrid modeling, and the method comprises the steps: collecting electrochemical parameters of a storage battery pack, carrying out the preprocessing, obtaining a time sequence sample, and extracting a capacity time sequence; constructing a multi-channel convolution encoder family module, and performing independent feature extraction on the electrochemical parameters by taking a time sequence sample as input to obtain spatial feature mapping; constructing a time hybrid modeling module, and obtaining capacity time sequence feature mapping by taking the capacity time sequence as input; constructing a cross-parameter feature fusion layer, and inputting spatial feature mapping and capacity time sequence feature mapping to obtain health state features; inputting the health state characteristics into a regression prediction head, and outputting a prediction value of the storage battery pack; and constructing a main loss function to carry out model training. The method realizes an integrated prediction mechanism for the transformer substation storage battery capacity degradation process, can effectively adapt to the complex operation environment of a transformer substation, and has relatively high engineering feasibility and popularization and application values.

The invention discloses a battery capacity prediction and state evaluation method and system based on multi-model collaborative learning, and belongs to the technical field of battery management. The method comprises the following steps: constructing a database containing multiple lithium ion battery long-term cycle data, and classifying according to a capacity attenuation trend; cleaning and preprocessing short-term cycle data of the to-be-tested battery; matching the to-be-tested data with the long-term attenuation trend in the database by using a clustering algorithm, and determining an optimal matching trend; distributing weights for the data in the matching trend by adopting a correlation algorithm, and generating initial capacity attenuation prediction; performing sequence correction on the preliminary prediction in combination with meta-learning and a related model, and generating a smooth future attenuation trend conforming to a physical law; and outputting a capacity prediction and health state evaluation result, and evaluating the prediction precision through a root-mean-square error and an average absolute percentage error. The method significantly improves the precision and generalization ability of long-term capacity prediction, and is suitable for various scenes such as electric vehicles, energy storage systems, consumer electronics and the like.

The invention discloses a power battery health degree online evaluation method and system based on a charging curve, and the method comprises the steps: dynamically dividing a characteristic interval of a constant-current charging stage and a constant-voltage charging stage through monitoring the voltage and current data in a charging process in real time; extracting time domain and frequency domain characteristic parameters in the characteristic interval; inputting the time domain and frequency domain characteristic parameters into a health degree evaluation model subjected to transfer learning optimization, and generating a fusion health degree index at least comprising a battery capacity attenuation coefficient and an internal resistance change vector; and outputting a final health degree evaluation result and residual service life prediction through an adaptive weighting algorithm based on the fused health degree index in combination with historical cycle data and operating environment parameters of the battery. According to the embodiment of the invention, high-precision and non-intrusive online evaluation and life prediction of the health degree of the battery can be realized, and the real-time performance, accuracy and engineering applicability of evaluation are improved.

The invention discloses a battery capacity loss real-time prediction method based on Arrhenius-LSTM, and the method comprises the steps: S1, collecting the current, voltage, state of charge and temperature data of a vehicle-mounted battery management system, and generating an original time series data set; s2, performing time alignment and capacity calculation on the original time sequence data set to generate a capacity loss sequence; s3, an Arrhenius Arrhenius model is constructed based on the capacity loss sequence, and a physical baseline prediction sequence is generated through temperature related parameter estimation; s4, calculating a difference value between the capacity loss sequence and the physical baseline prediction sequence, and generating a residual sequence; s5, performing feature extraction and time sequence window construction on the residual error sequence to generate an LSTM training data set; s6, inputting the LSTM training data set into the long short-term memory network for training, and generating a residual prediction model; and S7, adding the physical baseline prediction sequence and the output of the residual prediction model to generate a capacity loss prediction result. And the online monitoring and early warning functions of the health state of the battery are realized.

The invention is suitable for the technical field of battery safety monitoring, and provides a sodium ion battery energy storage system health state dynamic prediction method based on simulation digital twinning, and the method comprises the steps: constructing a digital twinning body comprising a physical layer, a virtual layer and a data interaction layer; performing health state estimation based on the physical layer and the virtual layer, and outputting a health state estimation value; comparing the health state estimated value with the measured value, calculating a residual error, and updating key aging parameters in the simulation model; inputting the updated key aging parameters into a pre-trained time sequence neural network model, carrying out sequence learning, and outputting a capacity attenuation trend and a residual life prediction result of the battery; generating a full life cycle optimization strategy based on the prediction result, and generating an optimal charge-discharge power table by simulating the influence of different charge-discharge strategies on the life; the problems of life attenuation and high operation and maintenance cost caused by insufficient prediction precision in the prior art are effectively solved.

The invention discloses a battery and a power utilization device, and belongs to the technical field of secondary batteries. The battery comprises a positive plate, the positive plate comprises a positive current collector and a positive active material layer, the positive active material layer comprises a positive active material and a lithium supplement agent, and the lithium supplement agent comprises an iron-containing lithium supplement agent; the ratio (set as a) of the characteristic peak area of Fe < 3 + > to the peak area of O1s in the hollow electric state in the XPS spectrum of the positive plate, the ratio (set as b) of the negative electrode discharge capacity to the positive electrode discharge capacity of the battery and the discharge curve end potential (set as cV) of the negative plate are adjusted, so that a * b * c is controlled within a suitable range, the gram capacity of the positive plate can be effectively improved, and the battery performance is improved. And meanwhile, the cycle performance of the battery is ensured.

The invention provides a lithium ion battery health state assessment method based on physical information and data driving, and belongs to the technical field of lithium ion battery health state assessment, and the method comprises the steps: constructing a physical model for describing a lithium ion battery capacity attenuation rule according to empirical model constraints and physical rule constraints; a physical information neural network model is built, the physical information neural network model comprises an attention-based cross-modal fusion estimation network and a loss function embedded into a physical model, and optimization strategies such as adaptive weight equalization, time-frequency domain equalization and boundary hard constraint are introduced; training the physical information neural network model by using the battery aging data; and estimating the health state of the lithium ion battery by using the trained physical information neural network model. According to the method, under the condition that only a small amount of training data is used, the estimation precision and stability of the battery health state under the scenes of interpolation and extrapolation are remarkably improved, and the problem of small-sample battery SOH prediction is solved.

The utility model provides a battery capable of quantitatively repairing the capacity of a lithium ion battery. The battery capable of quantitatively repairing the capacity of the lithium ion battery comprises a shell, and a battery cell, an energy supplementing system and a battery management system which are arranged in the shell, the energy complementing system is connected with the battery management system through a wire harness; the battery cell comprises a cathode, a third electrode and an anode; the third electrode is used for supplementing lithium for the cathode and the anode; the third electrode is a porous electrode or a mesh electrode; the number of the negative electrodes and the positive electrodes is greater than or equal to 1; a layer of third electrode is arranged between each pair of negative electrode and positive electrode; the battery management system is connected with the negative electrode, the third electrode and the positive electrode through wire harnesses. The third electrode serving as a lithium supplementing electrode is arranged between the positive electrode and the negative electrode, the size of the third electrode is equivalent to that of the positive electrode, the third electrode is of a porous structure or a net structure, lithium ion transmission between the positive electrode and the negative electrode is not blocked, and the third electrode can stably exist in the electrolyte when not working (dormancy).

The invention discloses a battery capacity degradation prediction method based on parameter identification and neural network joint modeling. The method comprises the following steps: firstly, carrying out data preprocessing on voltage and current data of a lithium ion battery to extract more local information; then defining key parameters to be identified; carrying out parameter identification on key parameters in the physical model by adopting a genetic algorithm; generating a high-precision prediction voltage sequence for filtering instantaneous working condition disturbance by using a physical model; and finally, based on a PKA network, time sequence features are extracted, and high-precision capacity degradation trajectory prediction is realized. And evaluating MSE and RMSE indexes on the test set, and supporting uncertainty estimation and visualization. According to the method, the physical model is introduced to predict the voltage to serve as a feature enhancement signal, physical constraint and mechanism interpretation are provided for capacity degradation modeling, and therefore prediction precision, robustness and interpretability are improved, and the method can still operate when a capacity label is lacked in the early stage.

The present invention discloses a voltage-configurable rechargeable battery device and charging terminal and charging station, wherein the voltage-configurable rechargeable battery device comprises a rechargeable battery, a battery voltage management circuit, and a battery capacity acquisition circuit; the rechargeable battery is electrically connected to a charging terminal; the battery voltage management circuit comprises a boost / buck management circuit; the battery capacity acquisition circuit is connected to the battery voltage management circuit; the rechargeable battery can realize discharge curve following according to the current capacity via the battery voltage management circuit. The present invention can ensure basically consistence of the actual power level and the displayed power level, and improve users' experience.

The invention relates to the field of lithium ion battery health state evaluation, and discloses a lithium ion battery life in-situ rapid prediction method and device, and the method comprises the steps: carrying out a battery capacity calibration test, and obtaining a first calibration capacity Q1 of a battery; installing the battery in a mechanical system of the measuring device, and setting an initial pre-tightening force; standing the mechanical system of the measuring device for a preset standing time, and then testing the capacity calibration of the battery again to obtain a second calibration capacity Q2 of the battery; when the value of Q1 / Q2 is higher than a preset ratio, carrying out charge-discharge circulation on the battery, and in the charge-discharge circulation process of the battery, collecting the pressure change of the battery in the longitudinal direction, namely a pressure change curve of expansion force; and establishing an expansive force change fitting formula in the circulation process according to the pressure change curve of the expansive force, and predicting the service life of the battery according to the relationship between the expansive force and the calibrated capacity of the battery. By using the scheme of the invention, early warning of the advance can be realized, the algorithm is simple, and the applicability is high.

The invention relates to the technical field of lithium ion batteries, in particular to a chromium-molybdenum double-doped lithium iron phosphate material as well as a preparation method and application thereof. The preparation method is used for solving the problems of low capacity, low electronic conductivity and poor cycling stability of the existing lithium iron phosphate material. According to the preparation method, chromium-molybdenum is doped into lithium iron phosphate, and the bond energy of a Cr-O bond and a Mo-O bond is stronger than that of a Fe-O bond, so that the dissolution loss of Fe < 2 + > in long-term circulation is reduced, and the battery capacity and the intrinsic electron conductivity of the material are improved; lithium iron phosphate is coated with nitrogen-doped carbon nanotubes, and nitrogen is doped in graphite crystal lattices of the carbon nanotubes, so that additional free electrons are provided, the intrinsic conductivity is improved, the resistance is reduced, the rate capability is improved, and the cycle life is prolonged; the polyaniline coating layer forms a barrier layer on the surface of the lithium iron phosphate particles, so that the cycle performance and the battery capacity are improved; the three components cooperate to improve the capacity, conductivity, rate capability and cycling stability of the material.

The invention relates to the technical field of energy storage battery health state evaluation, and provides a low-voltage station area battery capacity calibration and health factor extraction method, which comprises the following steps: acquiring battery end voltage, current, temperature and SOC data in real time; a complete charge-discharge cycle is automatically divided according to a preset rule, and the equivalent discharge capacity is calculated through an ampere-hour integral; constructing an exponential type working condition correction factor based on the circulating temperature and the root-mean-square current to correct the capacity, and obtaining a dynamic calibration capacity in a sliding window by adopting exponential weight or weighted least square fitting; meanwhile, multi-dimensional health factors are extracted from voltage, current, temperature and SOC signals, and comprehensive health characterization is formed through adaptive weighted fusion in a data driving mode. According to the method, online and dynamic capacity estimation and multi-dimensional health assessment of the energy storage battery in the transformer area under the complex and unsteady working condition are achieved, the capacity calibration precision and the life prediction reliability are remarkably improved, and intelligent operation and maintenance and risk early warning are facilitated.

The invention relates to the field of battery maintenance, and discloses a machine room battery capacity monitoring method and system, and the method comprises the steps: obtaining and preprocessing battery operation and environment data in a machine room, and extracting static and dynamic features from the preprocessed battery operation data; modeling by associating the static characteristics with environmental data to obtain a static health index of the battery, and monitoring a steady-state basic health level; associating the dynamic characteristics with the environmental data under event driving to obtain a battery dynamic response index, and monitoring transient performance response capability; the static and dynamic indexes are fused, historical data are combined, a capacity prediction model is used for analysis, and a current capacity estimation value and an attenuation trend prediction sequence are output; finally, early warning is judged according to the prediction result, and an early warning prompt is generated and output, the whole process aims at comprehensively evaluating the battery state, accurately predicting the capacity and early warning from the static and dynamic aspects by integrating battery operation and environment data, so that stable operation of a machine room is guaranteed, the fault risk is reduced, and a reliable basis is provided for operation and maintenance decision making.

The invention provides a new energy vehicle battery remaining life prediction and reverse logistics process dynamic optimization method and system based on intelligent network connection. The method comprises the following steps that vehicle operation information and battery state information are obtained in real time through a vehicle-mounted terminal; constructing a lithium ion battery residual life prediction model based on BiLSTM, and optimizing parameters in the lithium ion battery residual life prediction model by using an improved whale optimization algorithm; according to the capacity data and the residual life of the lithium ion battery, the battery scrapping number and time distribution of each area are obtained, the logistics quantity among the nodes in the reverse logistics network is distributed through an improved particle swarm optimization algorithm, the optimal recycling and scrapping path is obtained, and on the premise that the recycling requirement and the transportation constraint are met, the recycling efficiency of the lithium ion battery is improved. And dual optimization of regional overall logistics cost and carbon emission cost is realized. According to the invention, the logistics quantity configuration scheme among the recovery nodes can be dynamically optimized, and the overall operation efficiency of the recovery network is improved.

The invention belongs to the technical field of preparation of a solid electrolyte in a high-specific-capacity battery, and particularly discloses a preparation method and application of a composite solid electrolyte of a high-specific-capacity battery, and the method comprises the following steps: adding natural ore powder into a zinc salt electrolyte or a lithium salt electrolyte, stirring and replacing, and then adding a lithium salt electrolyte or a zinc salt electrolyte; centrifugally washing and drying to obtain ore powder; weighing ore powder and mixing with a polymer to obtain mixed powder; and pressing the mixed powder by using a tablet press to obtain the composite solid electrolyte. According to the preparation method and the application of the high-specific-volume battery composite solid electrolyte, the composite solid electrolyte prepared by the method can be used for an aqueous zinc-iodine battery, so that side reaction caused by free water is effectively reduced; the dissolution of active iodine and the shuttling of polyiodide can be inhibited, the capacity fading of the battery is relieved, and the cycling stability is improved; meanwhile, the composite solid electrolyte can also be used for a lithium ion battery, and the cycling stability and safety can be improved.

The invention relates to the technical field of batteries, in particular to a battery capacity prediction method and system and a computer program product. The invention provides a battery capacity prediction method, which comprises the following steps of: obtaining a constant voltage stage time characteristic sequence of a battery, and inputting the constant voltage stage time characteristic sequence into a bidirectional long short-term memory network to obtain an initial prediction sequence; generating a capacity degradation sequence including battery capacity corresponding to the battery based on the capacity degradation model; constructing a mapping relation between the initial prediction sequence and the capacity degradation sequence through a dynamic time warping strategy; determining a target prediction residual error through a particle filtering model and based on the initial prediction sequence and the capacity degradation sequence which have the mapping relation; and correcting the initial prediction sequence through the target prediction residual error to obtain a capacity prediction value. The problem of reference correction error caused by time migration in a traditional battery capacity prediction method is solved, and the prediction precision and long-term stability in a capacity jump scene are improved.

The invention provides a lithium battery residual life detection method and apparatus, an electronic device and a storage medium. The method comprises the steps of performing correlation analysis on a voltage battery capacity curve of each charging interval and a battery health state to obtain a correlation coefficient; when the absolute value of the correlation coefficient is greater than a preset value, constructing a battery voltage characteristic sample set through the battery voltage and the battery capacity of the charging interval, and taking impedance spectrum data corresponding to each charging interval in the battery voltage capacity characteristic sample set as an impedance spectrum characteristic sample set; inputting the battery voltage characteristic sample set into a deep learning model to obtain a predicted impedance spectrum, and training the deep learning model based on the predicted impedance spectrum and the impedance spectrum characteristic sample set; and inputting the charging data of a to-be-predicted lithium battery into the trained deep learning model to obtain a target predicted impedance spectrum, and determining the predicted residual life of the lithium battery based on the equivalent circuit model and the target predicted impedance spectrum. The detection efficiency is improved by detecting the battery voltage characteristics of the lithium battery.

The invention discloses an electrode health diagnosis method for a silicon-carbon negative electrode battery with a self-adaptive voltage range, and relates to the technical field of battery control, and the method comprises the steps: obtaining a quasi-static open-circuit voltage discharge curve according to a low-rate discharge test of the silicon-carbon negative electrode battery in an aging process, and measuring the total battery capacity; analyzing and extracting the silicon content in the battery; reconstructing a silicon-carbon composite electrode curve according to the silicon content, and performing inversion calculation of a voltage reconstruction model to obtain electrode parameters; the method comprises the following steps: acquiring charging fragment data of a silicon-carbon negative electrode battery in a circulation process, extracting charging time sequence characteristics based on equal interval voltage alignment and relaxation voltage characteristics after full charge according to the charging fragment data, and constructing a corresponding binary mask array; and constructing an estimation model, and performing supervised learning to obtain a trained estimation model for performing diagnosis of the electrode health parameters according to the charging fragment data, thereby effectively improving adaptability and accuracy during electrode health diagnosis.

The invention relates to the technical field of energy storage battery capacity, and discloses a liquid metal battery capacity prediction method, system and device based on a Stacking model and a medium, and the method comprises the steps: selecting a gradient boosting decision tree, a random forest and support vector regression as a base learner, and linear regression as a meta learner; constructing a stacking model through Stacking ensemble learning, and training the model by adopting a cross validation method to prevent overfitting; carrying out a liquid metal battery aging experiment to obtain historical capacity data; and inputting historical capacity data in a battery circulation process into the trained stack model, and predicting future capacity change. The method gives full play to the advantages of the selected basic model, effectively fuses the sensitivities of different models to the aging characteristics of the liquid metal battery, and comprehensively improves the accuracy of capacity prediction of the liquid metal battery through the comprehensive capture of the aging characteristics.

The invention relates to the technical field of energy storage battery management, and provides an energy storage battery management system based on Bayesian fusion and model prediction control, and the system comprises a data collection and preprocessing module which collects the voltage, current and temperature data of an energy storage battery pack in real time; the electrochemical model prediction module is used for updating parameters by adopting a recursive least square method based on a second-order RC equivalent circuit model to obtain a capacity prediction value and prediction uncertainty; the data driving model prediction module is used for obtaining a capacity prediction value and prediction uncertainty through Monte Carlo dropout reasoning; the Bayesian fusion module is used for calculating an evidence weight based on the prediction uncertainty and carrying out Bayesian fusion on the capacity prediction value; and the model prediction control module is used for solving an optimal equalization strategy based on the fusion capacity prediction value and generating a control instruction to realize intelligent equalization scheduling of the energy storage battery pack. According to the invention, the accuracy of energy storage battery capacity prediction is improved, and adaptive battery management under complex working conditions is realized.

The invention provides a working condition lithium battery SOH estimation method based on improved cordyceps sinensis correction-visual Transform, belongs to the technical field of battery health state estimation, and aims to solve the problems that the existing lithium battery capacity estimation depends on experimental data, the experimental data and working condition data have huge difference, and a real SOH label is lacked. The method comprises the following steps: acquiring lithium battery charging cycle and SOC change data, and selecting data sampling points; calculating a battery capacity estimation result, and carrying out statistical analysis on the battery capacity estimation result to obtain a distribution condition and statistical characteristics of a capacity estimation value; correcting the battery capacity estimation result after statistical analysis; and establishing an SOH estimation model, taking the corrected lithium battery capacity estimation result and the corresponding voltage, current and temperature data as input, and outputting a final lithium battery SOH estimation result.

The invention discloses a high-voltage lithium ion battery capacity compensation type electrolyte with a slow release effect, and relates to the technical field of electrolyte preparation. The electrolyte comprises a lithium salt, an organic solvent, a cosolvent and a lithium supplement agent, the cosolvent is a compound containing a nitrogen-containing heteroaromatic ring, and the nitrogen-containing heteroaromatic ring is modified with a boron group and / or a strong electron withdrawing group. According to the cosolvent, the solubility of the lithium supplement agent in an electrolyte solvent can be remarkably improved, meanwhile, the cosolvent and the lithium supplement agent can jointly participate in CEI forming an organic-inorganic hybrid component, and higher Young modulus, ion diffusion rate and interface stability are achieved, so that the interface side reaction between the electrolyte and an electrode can be effectively inhibited, and the service life of the electrolyte is prolonged. And the interface impedance is reduced, so that the capacity attenuation caused by the fracture of the electrode material structure is slowed down, and the cycling stability of the battery under high voltage is comprehensively improved. According to the invention, the problems of poor capacity compensation performance and poor interface stability under high voltage of the existing electrolyte are solved.

The invention belongs to the technical field of battery capacity grading detection, and particularly relates to a solid-state battery capacity grading method and system based on sampling resistor temperature compensation, and the method comprises the steps: collecting the local temperature of a sampling resistor and the voltages at the two ends of the sampling resistor in a capacity grading detection loop in real time; the actual resistance value of the sampling resistor is dynamically corrected through a resistance value-temperature model integrating first-order and second-order temperature coefficients and a self-heating power effect, so that high-precision real current is calculated, and the accurate capacity of the solid-state battery is determined according to the real current. The system correspondingly comprises a temperature sensor, a temperature compensation processing module and other hardware. By directly monitoring and compensating the temperature drift of the sampling resistor, the problem of capacity calculation error caused by self-heating of the resistor under large current is effectively solved, and the precision and reliability of solid-state battery capacity grading detection are remarkably improved.