4221 results about "Internal resistance" patented technology

The invention discloses a storage battery abnormity early warning system based on an intelligent battery gateway, and relates to the technical field of battery monitoring. The system comprises a sensor module used for collecting voltage, internal resistance, temperature, thermal runaway state and floating charge voltage parameters of a single battery in real time; the intelligent battery gateway comprises a data processing module and an early warning module, and the data processing module carries out fusion analysis on sensor data through an intelligent algorithm, calculates the battery remaining capacity SOC and the state of health SOH and executes an intelligent equalization strategy; and the early warning module generates a multi-stage early warning signal according to the parameter threshold and the anomaly detection model. According to the invention, through a multi-dimensional fusion sensing technology and a dual-drive intelligent algorithm, the aging sign of the internal microstructure of the battery is accurately captured, and high-precision estimation of the residual electric quantity and the health state of the storage battery is realized; and in the aspect of early warning performance, a multi-dimensional linkage detection model and an intelligent early warning mechanism are established, so that the accuracy and timeliness of fault early warning are effectively improved, and the early warning time is greatly advanced.

The invention provides an electric vehicle battery health state monitoring and service life estimation method and system, and the method comprises the steps: S1, obtaining a real-time data flow of a battery during the operation of a vehicle, extracting the original records of charging and discharging depth, temperature change, internal resistance parameters and capacity data from the real-time data flow, a dynamic monitoring sample is calculated through voltage, current and temperature values collected by a sensor; s3, calculating a capacity attenuation rule through the time sequence feature set, analyzing the decrease amplitude of the capacity after each charge-discharge cycle by adopting an exponential attenuation model, obtaining a long-term operation trend from historical data, and obtaining a dynamic curve of capacity attenuation; and S8, if the confidence interval of the final estimation probability distribution is greater than a preset threshold value, recollecting data with higher frequency for the dynamic monitoring sample, and performing iterative optimization on the life estimation model through the updated sample to obtain an adjusted life detection result.

The invention belongs to the technical field of battery monitoring, and discloses an electric vehicle power battery health condition real-time monitoring method and system, and the method comprises the steps: obtaining multi-parameter data such as voltage, current, temperature, internal resistance and the like through a battery management system, and carrying out the synchronous collection of the data through a preset sampling frequency, and obtaining a multi-dimensional time series data set; aiming at the multi-dimensional time sequence data set, adopting a feature extraction algorithm to separate dynamic change features of voltage, current, temperature and internal resistance to obtain a multi-parameter feature set; if the deviation of the battery health state output by the dynamic mapping relation model exceeds a preset threshold value, analyzing a fluctuation mode in the multi-parameter feature set through an anomaly detection algorithm, and judging whether a potential degradation risk exists or not; and according to the comprehensive representation of the battery health state, a time sequence prediction algorithm is adopted to analyze the future change trend of the multi-parameter feature set, and a short-term prediction value of the battery health state is generated.

An early warning method for battery health degree and residual life of a medium-and-large-sized electrochemical energy storage power station belongs to the field of battery health assessment, and comprises the following steps: firstly, establishing a battery health state grading system, determining threshold ranges of key parameters such as voltage, internal resistance and charge and discharge capacity, training historical data through various algorithms such as a support vector machine to construct a discrimination model, and calculating the battery health degree and residual life of the medium-and-large-sized electrochemical energy storage power station; and the health level is accurately evaluated. The health detection system monitors data in real time, an evaluation model is constructed through preprocessing and correlation analysis, and the health level is judged and a report is generated according to the evaluation model. And for a sub-health battery unit, determining an isolation priority according to fuzzy comprehensive evaluation, unloading and predicting a deterioration trend to formulate a maintenance plan. And during discharging, the battery is selected to discharge and power is adjusted according to the health level. According to the method, historical data are clustered and analyzed by using a machine learning algorithm to construct an evaluation model to predict a new data level, and an energy storage system operation strategy is dynamically adjusted based on the health level, so that economy and reliability are improved.

The invention relates to the technical field of battery management, and discloses an equalizing charging method, device and equipment for a power battery pack. The method comprises the following steps: performing parameter acquisition on a power battery pack, measuring terminal voltage values, temperature values and charge and discharge current values of each single battery, and calculating a state-of-charge value and an internal resistance change rate of each single battery; inputting into a multi-head self-attention network for feature extraction, and generating a state correlation feature matrix; constructing an Actor network and a Critic network based on the state correlation characteristic matrix, and outputting an equalization current value, a constant-current or constant-voltage equalization mode and equalization time through the Actor network; according to the equalization current value, the equalization mode and the equalization time, the DC-DC converter in the main equalization loop is controlled to generate constant current output, or the Buck-Boost converter in the secondary equalization loop is controlled to generate constant voltage output, the equalization efficiency of the power battery pack is guaranteed, meanwhile, energy loss is reduced, and temperature self-adaptive control over the equalization circuit is achieved.

The invention relates to the field of energy storage system management and battery performance evaluation, in particular to an energy storage system electrical performance evaluation method and system.The method comprises the steps that battery cluster data and environment data are collected in real time, the charging and discharging process of a battery is accurately simulated in combination with simulation data and a digital twin model, and the future running state of the battery is predicted. The reinforcement learning algorithm adaptively optimizes the charging and discharging strategy according to simulation data and feedback results, and adjusts the charging power, the discharging rate and the temperature control strategy in real time through model prediction control. The method effectively evaluates the charging and discharging efficiency, the internal resistance change, the temperature fluctuation and the remaining service life of the battery, and provides decision support for operation optimization and maintenance of the system. According to the invention, the energy storage system can significantly improve the charging and discharging efficiency, prolong the service life of the battery, and ensure the stable and efficient operation of the system under different load and environmental conditions.

The invention provides a deep learning-based battery life accurate prediction and evaluation method and system, and relates to the technical field of battery life prediction, and the method comprises the steps: obtaining battery operation data, carrying out the process division based on change features, employing a state observation model and a variational inference network to extract time sequence features, and obtaining dynamic features through phase-space reconstruction; establishing a deep feature enhancement network to obtain comprehensive features; features are extracted through capacity and internal resistance prediction branches, segmented recursive coupling channels are established for fusion, and a prediction result is output; determining the residual life in combination with a scrapping threshold value; according to the invention, the battery life prediction precision and reliability are improved.

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

The invention discloses a charge and discharge controllable system and method for a decommissioned battery, and relates to the technical field of intelligent charge control. By collecting the capacity fading rate, the internal resistance value, the cycle index and the environment temperature data of the decommissioned battery in real time, a health degree parameter is calculated by adopting a nonlinear coupling algorithm; and the future health degree evolution trend is predicted in combination with the LSTM neural network. And dynamically generating a grading label according to a preset scene threshold matrix, and matching the charging demand thermodynamic diagram with the battery grading label through a dynamic scheduling algorithm to realize intelligent distribution of charging and discharging power. And introducing a photovoltaic-battery-power grid cooperative power supply model, predicting and dynamically adjusting the power supply proportion based on the environment temperature and the photovoltaic output, and deploying to a target scene. And through a dynamic health degree evaluation and scene adaptive matching mechanism, the utilization rate of the retired battery is improved, the deployment cost of charging facilities is reduced, and the power supply reliability under multiple scenes is remarkably 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 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 relates to the technical field of storage batteries, in particular to a storage battery monitoring, detection, treatment and evaluation integrated system and a storage battery management method, and the system comprises a plurality of accompanying modules, a collection power distribution control module, a storage battery management capacity checking host and a remote platform; the internal resistance analysis test of the system is based on multi-frequency-point alternating current excitation (0.5 Hz-7. 5kHz), a complex impedance spectrum of not less than 20 frequency points can be constructed, and the internal polarization characteristics of the battery are analyzed; the system adopts a feed network type discharge capacity checking, so that discharge electric energy is fed back to a power grid, and the capacity detection precision is improved to 98%; according to the system, EIS data and an LSTM neural network are fused, and a battery state of health (SOH) prediction model is established; the system is based on a DSP digital power supply technology, and can realize 50A active equalization topology.

The invention relates to the technical field of power supply control, in particular to a power supply control system of a photovoltaic energy storage charging pile, which comprises a load power analysis module, an energy storage battery pack management module, a charging and discharging dynamic regulation and control module, an abnormal charging and discharging correction module and a power distribution optimization module. According to the invention, power demand analysis and battery management optimization are carried out. By dynamically analyzing the instantaneous power and fluctuation of the photovoltaic energy storage charging pile, the stability of the load can be more accurately judged, adjustment is carried out according to different fluctuation levels, finer energy allocation is achieved, and by monitoring and adjusting the internal resistance of the battery in real time, abnormal charging and discharging behaviors can be found and corrected in time. According to the invention, safe operation and reliability of energy output are ensured, the utilization rate of photovoltaic power generation can be improved through optimized power distribution, the power supply proportion of the power grid and photovoltaic power generation is dynamically adjusted according to the actual power supply condition, energy waste is effectively reduced, and the economical efficiency and environmental protection property of overall electric energy are improved.

The invention discloses a new energy automobile lithium battery thermal runaway monitoring system and method. The system comprises a vehicle-mounted lithium battery, a multi-source data acquisition module, a multi-source data fusion module, a hidden Markov model monitoring module, a state identification module, a data analysis processing module, an early warning module, an emergency processing module, a wireless communication module, a cloud platform and a battery management system. The multi-source data acquisition module is responsible for acquiring multi-dimensional data such as voltage, temperature and internal resistance of a lithium battery, and the data fusion module processes the data and then transmits the data to the hidden Markov model monitoring module for state monitoring and prediction. According to the system, a high-order hidden Markov model and a hidden semi-Markov model are adopted, state retention time modeling and a self-adaptive online learning mechanism are combined, and conversion of a battery from a normal state to an abnormal state and a thermal runaway state can be accurately recognized. The system remarkably improves the safety and stability of the lithium battery of the new energy automobile, and is suitable for power battery management of the new energy automobile.

The invention discloses a liquid cooling pipeline temperature control system based on liquid flow regulation, and the system comprises a data collection module which is used for collecting the temperature, internal resistance and current of a battery cell in real time, and carrying out the calculation to obtain the heat generated by the battery cell; the region division module is used for obtaining weight coefficients of a plurality of battery cells according to the average temperature of the battery module and the heat production of the battery cells, and performing region division according to the weight coefficients and the space coordinates of the battery cells by utilizing a clustering algorithm; the optimization control module is used for constructing a region optimization model according to the target temperature and solving flow set values of a plurality of regions based on constraint conditions; and the flow feedback module is used for calculating the pressure compensation flow according to the pipeline shapes of the plurality of areas and the current flow, calculating the aging compensation flow according to the internal resistance of the battery cell, and updating the flow set value again by using the pressure compensation flow and the aging compensation flow. The invention relates to the technical field of battery thermal management, and solves the technical problems of non-uniform temperature adjustment and inaccurate control of an existing liquid cooling temperature control system.

The invention relates to the technical field of lithium battery safety management, and particularly discloses a lithium battery thermal runaway early warning and protection method based on an adaptive neural network, which comprises the following steps: acquiring temperature, internal resistance and voltage data of a lithium battery in real time, extracting multi-dimensional features based on discrete wavelet transform, singular value decomposition and principal component analysis algorithms, and performing early warning and protection on the lithium battery thermal runaway. The influence of internal resistance and voltage on abnormal temperature fluctuation is quantified; constructing a feature vector through the comprehensive influence coefficient, inputting the feature vector into an adaptive fuzzy neural network prediction model, outputting a temperature anomaly fluctuation coefficient, and accurately predicting the thermal runaway risk of the lithium battery; according to the lithium battery thermal runaway prediction method, multi-level protection measures such as early warning response triggering, system automatic charging and discharging strategy adjusting, power output reducing and heat dissipation system starting are taken based on the prediction result, development of the thermal runaway risk is restrained, multi-dimensional data fusion and an intelligent algorithm are combined, and the lithium battery thermal runaway prediction accuracy and the risk control real-time performance are improved.

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 battery pack equalization charging and discharging intelligent control method and system based on adaptive learning. The method comprises the steps that the SOC change trend of battery monomers is predicted, an SOC-time curve is obtained after correction, and the monomers are clustered and recombined to obtain a target battery pack; monitoring a real-time operation state of the target battery pack, when a voltage difference value, a temperature difference value, an SOC difference value or an internal resistance difference value between at least two batteries exceeds a first preset value, judging that the target battery pack is in an unbalanced state, and determining a regulation and control priority according to an unbalance degree; and when the priority is greater than a second preset value, adaptively adjusting the energy of different battery monomers through energy transfer, and otherwise, adaptively adjusting the charging and discharging rates of different battery monomers. According to the method, the SOC change trend of the single body can be predicted in advance, the consistency of the battery pack is ensured through clustering and regrouping, the battery charging and discharging are balanced by adopting a grading strategy when the battery pack is unbalanced, and the operation energy efficiency and the safety of the battery pack are greatly improved.

The invention relates to the technical field of battery management systems, and particularly discloses a multi-dimensional parameter fused multi-battery-pack parallel dynamic balance control method and system, and the system comprises a multi-dimensional space-time parameter deep fusion module, an aging driving parameter weight self-adaption unit, a twin closed-loop strategy iteration system and a battery pack dynamic balance execution layer. Utilizing the state data output by multi-dimensional parameter fusion to drive aging weight adjustment; a weight strategy is verified and optimized through a twin system, and then feedback parameter fusion and weight adjustment are performed, so that the system continuously adapts to the battery state change. Multi-dimensional parameters such as voltage, current, temperature, internal resistance, SOC, SOH and the like are collected, time-space correlation characteristics are constructed in combination with timestamps and spatial position information, electrochemical, thermal and life states and time-space scene correlation of the battery are comprehensively covered through preprocessing and time-space attention mechanism neural network fusion, the one-sidedness of traditional single parameter evaluation is solved, and the evaluation accuracy is improved. Potential imbalance risks are identified in advance.

The invention discloses an energy storage system state evolution trend prediction method based on multi-source data fusion. The method comprises the steps of terminal voltage, current and temperature time sequence data acquisition, time sequence segmentation normalization, multi-physics field coupling feature construction, trend prediction model construction and training and energy storage system state evolution trend prediction. According to the method, the distinguishing capacity of the model for charging and discharging physical characteristics is improved, meanwhile, the voltage change rate, the multi-dimensional feature vector of the differential internal resistance and the thermal-electric coupling effect and the explicit encoding electric-thermal-resistance coupling relation are constructed, the transient response and the temperature hysteresis effect can be effectively captured, and then the model can be used for analyzing the charging and discharging physical characteristics. A degradation-aware cross-cycle feature extraction and gating mechanism is adopted, short-term fluctuation and long-term trend are adaptively balanced in multi-scale prediction, the prediction conflict problem is relieved, finally, physical constraints based on the electrochemical law and the internal resistance temperature characteristic are embedded in a loss function, it is ensured that the prediction result is accurate in numerical value and conforms to the physical law, and the prediction accuracy is improved. And generation of physically impossible solutions is avoided.

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

The invention relates to the technical field of battery fault detection, and particularly discloses a new energy automobile battery fault intelligent diagnosis method and system, and the method comprises the steps: collecting the three-dimensional time sequence data flow of the voltage, temperature and internal resistance of a battery pack through a distributed sensor array; the environment temperature, the number of charge and discharge cycles and the vehicle operation condition parameters are synchronously integrated as auxiliary diagnosis dimensions, and a multi-source heterogeneous data sensing system is constructed; the method comprises the following steps: preprocessing original data by adopting a dual-channel hybrid filtering architecture based on a multi-source heterogeneous data sensing system, and establishing an adaptive filtering parameter adjustment mechanism to generate multi-modal data; according to the method, multi-dimensional time sequence data and auxiliary diagnosis parameters of the battery pack are comprehensively collected through a constructed multi-source heterogeneous data sensing system, a rich and accurate data basis is provided for fault diagnosis, noise interference is reduced through a two-channel hybrid filtering architecture and a self-adaptive filtering parameter adjustment mechanism, and the fault diagnosis accuracy is improved. And a solid foundation is laid for subsequent feature extraction and model training.

The invention provides a positive electrode, a low-temperature electrolyte, a low-temperature sodium-ion battery and a preparation method and application of the low-temperature sodium-ion battery, and belongs to the technical field of battery materials. The low-temperature sodium ion battery comprises a positive electrode, a negative electrode and a low-temperature electrolyte, wherein the positive electrode comprises a positive electrode active material, a binder, sodium salt containing fluorine and sulfur and conductive carbon; the positive electrode is improved by adding sodium salt containing fluorine and sulfur, sulfur / fluorine rich in anion derivatives can be formed on a contact interface of the positive electrode and an electrolyte to form a firm SEI film, the internal resistance of the battery is further reduced, and the coulombic efficiency of the battery during charging and discharging at normal and low temperatures is improved. The low-temperature electrolyte comprises an ester solvent and an ether additive, by adding the ether solvent, the physicochemical property of the electrolyte and the composition of an SEI film on a contact interface of an electrode and the electrolyte are optimized, the viscosity of the electrolyte at low temperature is reduced, the ionic conductivity is improved, and formation of the SEI film rich in sodium fluoride on the surface of a negative electrode can be promoted; and thus, the problem of low coulombic efficiency of battery charge-discharge cycle at low temperature is solved.

The invention discloses a lithium battery state estimation method based on relaxation time scale modeling, and the method comprises the steps: 1, carrying out a sudden pulse charging test on a lithium battery, collecting voltage and current data, and calculating the open-circuit voltage, direct-current internal resistance and a time-domain impedance sequence of the lithium battery; and 2, converting the time domain impedance sequence of the lithium battery into a frequency domain impedance sequence after discrete Fourier transform, establishing a fitting equation set of a distribution function of the relaxation time scale DRT of the lithium battery and the frequency domain impedance sequence, and solving by adopting a regularized least square method. And 3, taking the solved DRT model distribution function as the input of a neural network, taking the state parameters of the lithium battery as the output of the neural network, and establishing a prediction model of the state parameters of the lithium battery. According to the method, EIS test required by traditional relaxation time scale modeling is avoided, the control complexity and design cost of test acquisition equipment are effectively reduced, and the method can be effectively applied to occasions such as battery life prediction, battery classification and screening, battery echelon utilization and the like.

The invention provides a multi-parameter collaborative optimization battery charging and discharging management system integrated with a thermal management technology, and aims to improve the charging and discharging efficiency and safety of a battery and prolong the service life of the battery. The system monitors key parameters such as the temperature, the voltage, the current and the internal resistance of the battery in real time through a high-precision sensor, and the parameters are comprehensively analyzed through the data fusion technology. A core optimization algorithm adopts a genetic algorithm or deep learning, a charging and discharging strategy is dynamically adjusted, and battery performance maximization is achieved. The thermal management module effectively controls the temperature of the battery through a cooling fin, a liquid cooling system or a phase change material, overheating and supercooling are prevented, and the safety and stability of the battery are improved. The user interface is friendly, the battery state is convenient to monitor and adjust, and the system has wide application prospects in the fields of electric automobiles, renewable energy storage and the like.

The invention discloses a health assessment method and system for a decommissioned battery, and relates to the technical field of battery state assessment, and the method comprises the steps: recognizing a decommissioned battery identifier, automatically generating an assessment task number, and setting the battery state as "to-be-detected"; a battery charging mode is set, battery state data are collected in real time, battery features are extracted, and a feature vector G is constructed; and inputting the feature vector G into a parallel attention neural network to carry out static SOH estimation to obtain a preliminary SOH estimated value, carrying out SOH correction by using joint extended Kalman filtering, and obtaining the current internal resistance. According to the invention, the method can achieve the efficient and precise evaluation of the health state of the decommissioned battery, remarkably improves the comprehensiveness and deep modeling capability of feature extraction, improves the reliability and dynamic adaptability of an SOH estimation result, effectively reduces the error fluctuation, and improves the generalization capability of an evaluation system for a complex decommissioned state.

The invention relates to the technical field of electrical variable measurement, in particular to a control system and a control method of a battery measuring instrument. The method comprises the following steps: acquiring single voltage, total voltage, current and internal resistance data of a battery pack to obtain initial electrical parameters; collecting the surface temperature of the battery pack to obtain an initial thermodynamic parameter; performing synchronous time sequence integration on the initial electrical parameters and the initial thermodynamic parameters, and unifying formats to obtain a battery running state monitoring data set; and performing dynamic threshold determination and trend analysis on each parameter based on the battery operation monitoring data set to obtain an abnormal state identification result. By constructing a complete intelligent monitoring system covering data acquisition, processing, abnormity identification, state evaluation, communication packaging and visualization, high-precision real-time monitoring, fault pre-judgment and remote communication of the running state of the battery are realized, and the synchronism, the accuracy and the adaptability of the system and the intelligence and the practicability of field application are remarkably improved.

The invention provides a self-maintenance system based on single storage battery operation state difference monitoring, which comprises a distributed monitoring module array, each monitoring unit comprises a multi-parameter fusion sensor group, and the distributed monitoring module array is used for acquiring the voltage fluctuation frequency spectrum, temperature gradient distribution, internal resistance change track and electrolyte ion migration rate of a single battery in real time; and the dynamic difference evaluation module is used for processing the multi-dimensional time sequence data by adopting a time sequence convolutional neural network and calculating the operating state difference degree among the monomers. Through the distributed monitoring module array and the multi-parameter fusion sensor group, multi-dimensional data such as the voltage, the temperature, the internal resistance and the electrolyte ion migration rate of the single batteries are collected in real time, and the operation state difference between the single batteries can be accurately quantified in combination with the time sequence convolutional neural network and the dynamic difference evaluation algorithm. Abnormal monomers are effectively identified, and a reliable basis is provided for a subsequent maintenance strategy.

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 provides a lithium battery comprehensive management system based on the Internet of Things and a block chain technology, and relates to the technical field of lithium battery comprehensive management. Comprising a data acquisition module, a data transmission module, an edge cloud cooperative processing module, an intelligent contract management module, a data security protection module, a cross-domain fusion module, a fault prediction module, an optimization scheduling module and a knowledge updating module. The system overcomes the limitation of the conventional sensor configuration, and can capture the operation information of the lithium battery in all directions. Basic electrical parameters of the battery can be monitored in real time, environmental factors are taken into consideration, a rich and accurate data basis is provided for accurately mastering the real state of the battery, management decision errors caused by one-sided data are effectively avoided, and the reliability and scientificity of battery management are greatly improved.