383 results about "Faulty cell" patented technology

The invention discloses a long-term failure prediction and fault early warning method in a storage battery pack, and relates to the technical field of data prediction, and the method comprises the following steps: S1, obtaining key characteristic parameters of the storage battery pack in real time; s2, constructing a health state evaluation model of the storage battery pack; s3, analyzing short-term dynamic change and long-term degradation trend of the battery; s4, a fault early warning model is constructed, and the model sends out early warning information when the battery is abnormal; s5, identifying and deducing a fault mode; and S6, making an optimization strategy. According to the long-term failure prediction and fault early warning method for the storage battery pack, the accuracy and timeliness of fault prediction of the storage battery are greatly improved through dynamic modeling of the health state. Compared with the prior art, the method has the advantages that the abnormal change of the battery can be quickly detected in a short time, the residual life of the battery is accurately predicted through long-term decline analysis, and the problems of misinformation and missing report in a traditional method are remarkably reduced.

The invention relates to the technical field of power grid energy storage systems, in particular to a battery intelligent fault diagnosis method and system suitable for a power grid energy storage system. According to the method, multi-source monitoring data including battery electrical parameters, power grid features, environmental conditions and historical operation records are obtained, unified time step alignment and feature extraction processing are carried out on the data, and statistical features, time sequence features and frequency domain features are obtained. A double-layer diagnosis model structure is adopted, a random forest model is used for diagnosing main fault modes such as over-charging and over-discharging, and a convolutional neural network model is used for diagnosing complex fault modes such as short circuit and temperature out-of-control. The system has a dynamic updating mechanism, and online optimization is realized by calculating incremental updating values of model parameters; meanwhile, a model evaluation mechanism is established, and when an evaluation index is lower than a preset standard, a retraining process is triggered. According to the invention, high-precision and real-time identification and early warning of the battery fault are realized, and the operation reliability and safety of the energy storage system are significantly improved.

The invention relates to the technical field of battery health management, in particular to a battery fault unsupervised detection method based on diffusion Transform and confidence coefficient calibration, which comprises the following steps: acquiring multi-modal time sequence data in a battery operation process, and performing preprocessing, including data cleaning, normalization processing, alignment and sampling; a diffusion Transform self-supervised learning framework is constructed, and the framework comprises a diffusion process based on a cosine scheduling strategy, multi-scale Transform architecture coding and a cross-modal self-adaptive fusion mechanism. Through the framework, potential space representation is optimized, and the battery state confidence coefficient is calculated; the optimal detection threshold value is dynamically determined by adopting a Bayesian optimization framework, whether the battery state is normal or faulty is judged according to the comparison result of the battery state confidence coefficient and the optimal detection threshold value, dependence on a fault sample label is completely eliminated, and a high-performance fault detection model can be trained only by utilizing normal sample data.

The invention relates to a battery fault diagnosis method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring operation data of a target battery in an operation process; determining a target operation characteristic of the target battery in at least one dimension according to the operation data; and based on a pre-trained fault classification model, according to the target operation characteristics of the target battery in at least one dimension, determining a target fault type of the target battery. By adopting the method, the accuracy, robustness and interpretability of battery fault early warning can be improved.

The invention discloses a power battery fault early warning method, solves the problem of difficulty in timely early warning of faults caused by consistency difference of a power battery pack, and belongs to the field of power battery management. Comprising the following steps: preprocessing collected operation data to obtain target data; determining the length of a sliding window based on the evaluation function, segmenting the target data according to a time sequence by using the length of the sliding window, and extracting features for representing battery consistency and fluctuation anomaly characteristics in each sliding window to form a feature matrix; dynamically adjusting a fault detection threshold according to the deviation between the median and the absolute median of the target data in the sliding window; calculating a fluctuation anomaly score through a local outlier factor algorithm; if the fluctuation abnormity score exceeds a fault detection threshold value, positioning a target single battery corresponding to the fluctuation abnormity score, determining a fault type according to a characteristic matrix of the target single battery, and outputting graded early warning prompt information; according to the invention, high-precision detection and early warning of power battery faults are realized.

A system for facilitating battery fault prediction is configurable to: (i) access a set of raw data comprising battery sensor data associated with a physical battery and operation data associated with operation of the physical battery; (ii) obtain an estimated battery state for the physical battery by utilizing the battery sensor data as input to a battery digital twin; (iii) obtain a battery fault prediction by utilizing (a) the estimated battery state obtained via the battery digital twin and (b) operation input based on the operation data as input to a battery fault prediction model, and wherein the battery fault prediction comprises one or more likelihood metrics indicating a likelihood of one or more battery faults occurring within one or more predetermined time periods; and (iv) cause presentation of an alert via a battery fault notification system.

The invention relates to the field of battery fault detection, in particular to a battery system health state evaluation method and system based on multi-dimensional feature fusion, and the method comprises the steps: S1, obtaining original operation data of a battery system, and carrying out the preprocessing of the original operation data to obtain a standardized data matrix; s2, based on the standardized data matrix, extracting health state features of a plurality of preset dimensions to form an original feature set; s3, constructing an incidence relation model among the features in the original feature set, and performing nonlinear fusion processing based on the incidence relation model to generate a low-dimensional fusion feature vector; s4, performing principal component analysis based on the low-dimensional fusion feature vector to extract a principal component, calculating a T2 statistical magnitude and an SPE statistical magnitude, and constructing a comprehensive health index; and S5, when the comprehensive health index exceeds a preset threshold value, analyzing the contribution degree of each feature in the original feature vector to the comprehensive health index, and positioning the fault single battery according to the contribution degree. The problems of insensitive fault symptoms, inaccurate fault positioning and high false report and missing report rate are solved.

The invention discloses a power supply intelligent management system of an AGV trolley, and relates to the technical field of intelligent management, during operation of the system, data monitoring is performed through various sensors, measuring equipment and a battery management system BMS, and internal resistance data, battery capacity attenuation conditions and battery charging and discharging conditions of a battery of the AGV trolley are acquired in real time; the battery health index is comprehensively calculated through the battery internal resistance coefficient, the battery capacity attenuation coefficient and the battery charging and discharging efficiency coefficient, the output power of the battery is dynamically adjusted, the charging and discharging process of the battery is automatically adjusted according to data collected in real time and a load evaluation result, and intelligent scheduling is achieved according to needs. By analyzing the health data, the charging and discharging behaviors and the working environment of the battery, the battery fault is predicted in time, automatic fault detection and early warning are supported, system shutdown or performance reduction caused by the battery fault is avoided, an interactive interface between a user and the system is provided, and remote monitoring and control are supported at the same time.

The invention discloses an energy storage battery fault monitoring management method and system based on big data, and the method comprises the steps: calculating the change rate of a battery monomer between each window and an adjacent window, and recognizing an energy output inconsistent time period; identifying adjacent monomer power compensation behaviors in the energy output inconsistent time period; the load allocation rate, the deviation ratio and the heat consumption change amplitude of the compensated single battery in the compensation process are obtained; predicting the damage probability of the single battery in a plurality of periods in the future, and generating a fault probability curve; constructing a masked fault identification model, and identifying the fault state of the single battery; evaluating the fault level of the single battery, and constructing a fault level index table; and evaluating the validity of the fault early warning and maintenance strategy, and updating the fault early warning strategy and the health maintenance strategy. Through the technical method provided by the invention, accurate monitoring and intelligent management of the energy storage battery pack can be realized, the accuracy and timeliness of battery fault early warning are remarkably improved, and efficient and safe operation of an energy storage system is ensured.

The invention relates to a self-adaptive cooperative control system and method for a sodium-ion battery energy storage system, and relates to the technical field of energy storage system control. An EMS-BMS-PCS cooperative control module is customized, a three-level architecture dynamically adjusts a voltage threshold value, and the SOC is corrected; the multi-stage active safety protection module comprises battery cell monitoring, fault battery cell ejection, modular battery replacement and three-stage fusing protection; the power grid response module PCS performs high-frequency sampling and supports grid-connected and off-grid switching and AGC / AVC service; the intelligent collaborative optimization engine integrates data to generate a charging and discharging strategy, and early warning is performed 14 days in advance for predictive maintenance; the layered distributed control module is used for realizing battery cell-to-system level management; and the double-ring network communication redundancy module ensures real-time transmission of instructions. According to the invention, the DoD and capacity utilization rate of the sodium-ion battery is improved, and the system economy is enhanced; heat spreading is blocked through multi-stage protection, the maintenance time is shortened, and the safety is improved; millisecond-level response meets the high-order demand of a power grid, predictive maintenance reduces the operation and maintenance cost, and the method is suitable for multiple energy storage scenes.

The invention discloses an active safety monitoring method for an energy storage power station, which relates to the field of safety detection of the energy storage power station, and comprises the following steps: setting multi-level acquisition frequency, acquiring energy storage safety data such as cell temperature, module internal resistance, hydrogen concentration and the like in stages according to time intervals from large to small, and establishing a dynamic model based on an Arrhenius equation to predict theoretical internal resistance; the residual error rate of the actually measured internal resistance and the theoretical internal resistance is calculated to evaluate internal resistance abnormity, a risk index is generated by combining the hydrogen diffusion coefficient, the concentration change rate and internal resistance abnormity weighting, and then dynamic early warning measures are executed in a graded mode according to the risk threshold value corresponding to the cell temperature interval. According to the method, through a multi-parameter fusion analysis and dynamic response mechanism, early abnormality of the battery can be accurately captured, monitoring precision can be flexibly adjusted, risk levels can be quantified, differentiated disposal strategies can be matched, timeliness, comprehensiveness and pertinence of safety monitoring of the energy storage power station can be improved, the misjudgment rate can be effectively reduced, and the safety of the energy storage power station can be improved. And a scientific basis is provided for battery fault early warning and risk prevention and control.

The invention discloses a battery fault diagnosis method and device, and belongs to the technical field of battery management. The method comprises the following steps: synchronously acquiring multi-dimensional signals in a battery operation process through a multi-modal sensor array; performing encoding processing on the multi-dimensional signal through a pulse encoder to obtain a standardized pulse time sequence; based on a federated transfer learning framework, training the quantum pulse neural network in combination with a physical constraint loss function to obtain a trained battery fault diagnosis model; inputting the standardized pulse time sequence into the battery fault diagnosis model, and calculating a battery fault category and a thermal runaway probability through quantum inference; and triggering a corresponding security policy according to the thermal runaway probability and a preset threshold. By adopting the technical means of multi-mode signal fusion, quantum calculation acceleration and distributed learning, accurate and rapid diagnosis of battery faults is realized, and the method has the advantages of high precision, low delay and low power consumption.

The invention relates to a battery internal short circuit early warning method and system based on a physical information neural network, and belongs to the technical field of battery fault detection, and the method comprises the steps: constructing a first-stage PINN model through a linearized Butler-Volmer equation, and carrying out the training through a multi-source signal of a battery; taking the output of the first-stage PINN model as prior input, and constructing a second-stage PINN model by using a nonlinear electrochemical-thermal-mechanical coupling equation, and training the second-stage PINN model; using the trained first-stage PINN model and the trained second-stage PINN model to output prediction results of the voltage and the expansive force of the target battery; comparing the difference between the prediction result of the model and the measured value of the sensor to generate a voltage residual error and an expansive force residual error; and realizing identification and alarm of the short circuit risk in the target battery based on the voltage residual error and the expansive force residual error. According to the method, a multi-fidelity PINN hierarchical modeling mechanism is introduced, so that high-precision characterization and prediction of the internal state of the battery can be realized by using experimental data and mechanism model information at the same time under different precision and complexity hierarchies.

The invention discloses a battery health state evolution path prediction method based on subgraph representation learning, and aims to overcome the defects in the prior art, obtain the conversion relation between different fault key safety states and support battery fault early warning. The method comprises the following steps: firstly, collecting battery characteristic data, cleaning, serializing and segmenting, and performing efficient compression by using an auto-encoder; secondly, extracting a key state by adopting a data flow clustering technology, regarding segments as small micro-clusters, and integrating charging sequences to form large micro-clusters which are used as key state nodes of an evolution process; then, a state transition diagram is constructed based on the time sequence transition relation of the battery between the micro-clusters, nodes represent key states, and edges represent state transition; then, for any to-be-predicted node pair, dynamically extracting a closed sub-graph, and designing a structure identification vector containing four-dimensional topological characteristics for node marking; then, constructing an enhanced sub-graph by injecting a negative sample edge, and carrying out representation learning by adopting a multi-head graph attention network; and finally, performing link prediction by using the trained model, screening high-probability connecting edges, and splicing the high-probability connecting edges according to a time sequence to form a directed evolution path. According to the method, through subgraph extraction and composite topology marking, negative sample enhanced representation learning and an evolution path splicing mechanism, the prediction precision is remarkably improved, accurate description of the evolution trajectory of the full life cycle health state of the battery is realized, and a visual and quantifiable technical support is provided for fault early warning.

The invention discloses a transformer substation storage battery pack abnormal state identification method based on multi-modal characteristics. The method comprises the following steps of obtaining current, temperature, humidity and vibration frequency of a storage battery in historical data; formalizing a fault prediction task and constructing a fault detection model, and importing preprocessed data into a feature extraction module to obtain local features; importing the local features into a time sequence feature extraction module to obtain time sequence features; time sequence features are imported into a global feature extraction module; global features are obtained; importing the local features, the time sequence features and the global features to a feature fusion module to obtain multi-modal features; and importing the multi-modal features to a prediction module to obtain a predicted fault type. According to the method, the local features, the time sequence features and the global features are extracted in sequence by using a plurality of detection indexes of the transformer substation storage battery pack, and the features are subjected to multi-modal data fusion, so that the capture capability of the model on data exception is improved, and the recognition accuracy of the abnormal state of the transformer substation storage battery pack is improved.

The invention discloses a battery fault monitoring and processing system and method based on deep learning, and relates to the technical field of battery monitoring, and the method comprises the steps: collecting the multi-source data of a battery in a battery replacement cabinet in real time, and constructing the analysis characteristics of the battery according to the preprocessed multi-source data; inputting a battery health analysis model for analysis, outputting a health state score, inputting the multi-source data of the battery smaller than a preset threshold value and battery analysis characteristics into a CNN-LSTM hybrid model for real-time fault diagnosis, performing classification identification, and determining a fault category; and acquiring a state vector of the fault battery, updating a state action value function through a reinforcement learning algorithm, and selecting an optimal response action according to strategy distribution to realize adaptive processing of the fault battery. Accurate quantification of battery changes is realized through battery analysis characteristics, and the accuracy of health state scores is improved; a state action value function is updated through the constructed state vector in combination with reinforcement learning, key changes are captured, and the prediction and classification precision of a time sequence model is improved.

The invention provides a fault identification method applied to a battery management system and a server, and the method comprises the steps: constructing a battery fault feature correlation map, determining a reference fault feature set and a fault type set, carrying out the evolution trajectory extraction of an operation feature sequence collected in the real-time operation process of a battery, and generating a feature evolution trajectory set; inputting the feature evolution trajectory set into a preset multi-level fault reasoning model, and performing hierarchical progressive reasoning on a matching relationship between the feature evolution trajectory set and a battery fault feature association map to obtain multi-level fault probability distribution; and fault propagation path backtracking is carried out based on the multi-level fault probability distribution and the battery fault feature association map, a candidate fault path set is generated, confidence evaluation and sorting are carried out on the candidate fault path set, and a target fault path in the first sorting and corresponding fault positioning information are output. According to the invention, the accuracy, reliability and comprehensiveness of battery fault identification can be effectively improved.

The invention discloses a state monitoring method and system for an outdoor emergency power supply. Firstly, original data such as battery internal resistance, charging efficiency, temperature fluctuation and load abrupt change are collected to form an initial data set; and then noise suppression and fusion processing are carried out on the data set to obtain a smoothed battery parameter sequence. And constructing a dynamic trend vector by analyzing the differential change of the sequence, judging a potential anomaly level according to the slope of the dynamic trend vector, and outputting an anomaly probability score. And identifying an inter-parameter interaction cluster based on clustering analysis, and performing causal reasoning on a dominant cluster to determine a fault dominant factor. And finally, in combination with the real-time state, the environmental parameters and historical defect information, constructing a modeling matrix, and executing multi-dimensional clustering to identify specific fault types such as bearing wear, insulation aging and the like. If the fault is caused by internal resistance abnormity, an interference source is further positioned, and an accurate fault position is output. According to the invention, the battery fault monitoring accuracy and positioning capability in a complex environment can be effectively improved.

The invention provides a photovoltaic energy storage management system, which relates to the field of photovoltaic technology and comprises a state monitoring module, a parameter estimation module, a safety management module, a charging and discharging control module, an equalization module and a heat management module. The state monitoring module is used for detecting performance data of a battery in the system; the parameter estimation module is used for calculating according to the performance data so as to determine state parameters of the battery; the safety management module is used for determining battery faults; the charge-discharge control module is used for controlling the charge-discharge state according to the environment data and the performance data of the battery; the equalization module is used for equalizing the charge states of the batteries; the thermal management module is used for adjusting the temperature of the battery in the system. By detecting the performance data of the battery and analyzing and determining the state parameters of the battery, the problem of the battery is found and processed in time, the situation that the health degree of the battery is reduced due to faults is avoided, it is guaranteed that a photovoltaic system can operate safely and reliably, and the durability and operation efficiency of the photovoltaic energy storage system are improved.

The invention relates to a battery fault detection method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring target battery parameters of a to-be-detected battery pack in a historical time period and actual battery voltage of each single battery in the to-be-detected battery pack; inputting the target battery parameters into the voltage prediction model to obtain a predicted average battery voltage; and determining a fault detection result of each single battery according to a difference value between the predicted average battery voltage and the actual battery voltage of each single battery. On one hand, more dimensional features can be obtained, so that the extracted battery features are more accurate, and the battery fault detection result is improved; and on the other hand, the fault detection result of the battery can be determined more concisely and efficiently.

The invention provides a polling dynamic updating method combined with battery degradation prediction, and relates to the technical field of battery management, and the method comprises the steps: calling the historical operation data of a battery pack, carrying out the battery degradation risk prediction, and building a basic degradation prediction score; predicting and analyzing each battery in the battery pack, and extracting a characteristic space deviation index; performing data analysis, and establishing a data confidence index; performing deterioration prediction backtracking on each battery in the battery pack, and establishing a historical consistency index; establishing a battery confidence coefficient, and constructing an inspection priority; carrying out degradation conduction analysis, and constructing an additional priority; and after compensating the routing inspection priority, configuring a routing inspection task according to a compensation result and battery distribution. The technical problems that in battery inspection in the prior art, the inspection task usually depends on a static health score or a regular inspection period and cannot be flexibly adjusted according to the actual condition of the battery, so that the inspection efficiency is low, and the battery fault risk is increased are solved.

The invention belongs to the technical field of battery fault diagnosis, and provides a battery fault diagnosis method and system based on an energy storage battery cluster SOC. The battery fault diagnosis method comprises the following steps: estimating a battery cluster SOC on line; dynamically updating a safety early warning threshold value of the battery module based on the identification parameters of the equivalent circuit model of the battery module, the current current and temperature of the battery module and the online estimated SOC of the battery cluster; judging whether the battery module is a normal battery module or not based on the SOC deviation of each parallel branch of the battery module, the deviation of the battery module relative to the time differential and a corresponding threshold value; if the battery module is a fault battery module or a potential fault battery module, fault diagnosis of the next level is carried out, whether the battery module is a normal battery or not is judged according to the voltage deviation and the SOC deviation of the single batteries, the deviation of the single batteries about time differential and corresponding threshold values, and matching operation is executed according to the fault level of the battery module.

The invention discloses a model construction method for multi-system energy storage battery multi-scene state monitoring, and relates to the technical field of battery state monitoring, and the method comprises the steps: collecting the multi-source data of a battery; preprocessing the battery real-time operation data and the battery historical fault data to obtain a battery data set; establishing a battery failure database; carrying out deep learning by using the real-time operation data of the battery, and constructing a battery fault diagnosis initial model; optimizing the battery fault diagnosis initial model to generate a battery fault diagnosis optimization model; operation data of the battery are received in real time, and online monitoring and fault diagnosis are carried out. The technical problem of low battery reliability caused by inaccurate multi-scene state monitoring and poor fault diagnosis effect of the multi-system energy storage battery in the prior art is solved, and accurate monitoring and effective fault diagnosis and early warning of the multi-scene state of the multi-system energy storage battery are achieved. And the safety and the reliability of the operation of the battery system are improved.

The invention provides a battery short-circuit fault diagnosis method and system based on an impedance spectroscopy, and relates to the technical field of battery management, and the method comprises the steps: obtaining the impedance spectroscopy data of a to-be-diagnosed battery in an optimal sampling frequency band, and carrying out the preprocessing; inputting the preprocessed impedance spectrum data into a trained fault diagnosis model, judging whether a short-circuit fault occurs or not, and obtaining a fault diagnosis result; wherein the optimal sampling frequency band is constructed according to the frequency characteristic importance degree by using a fault diagnosis model and quantifying the influence of impedance data under each sampling frequency on a fault diagnosis result; according to the method, the fault data set of the fault diagnosis model is screened based on the frequency characteristic importance, the information content of the fault data set is improved, and accurate diagnosis of the battery short-circuit fault is realized under the condition that only small-scale battery fault data exists.

The invention belongs to the technical field of electric ships and fault detection. The invention discloses a digital twin electric ship power battery fault detection method based on artificial intelligence. The method is characterized by comprising the following steps: step 1, a data acquisition layer captures a battery operation state and environmental parameters in real time by deploying multiple types of sensors; 2, the digital twinborn layer realizes dynamic mapping of a physical battery system by constructing a high-fidelity virtual model; establishing a multi-physical field coupling model based on electrochemical characteristics, thermodynamic behaviors and structural parameters of the power battery; 3, the fault prediction layer extracts the harmonic component of the voltage curve, the spatial distribution mode of the temperature gradient and other multi-dimensional features through time-frequency analysis, and captures the gradual change trend of the internal resistance of the battery in combination with a sliding window technology; and 4, the application layer converts a prediction result into an operable engineering decision, and constructs a closed-loop operation and maintenance system. According to the method, efficient battery fault detection can be realized.

The invention relates to a battery fault identification method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring impedance spectrum data; the impedance spectrum data is obtained after preset excitation of a plurality of test frequency points is applied to the to-be-tested battery in a working state; converting the impedance spectrum data from a frequency domain to a time domain to obtain time domain impedance spectrum data; performing feature enhancement on the time domain impedance spectrum data to obtain enhanced impedance spectrum data; inputting the enhanced impedance spectrum data into a pre-trained fault recognition model to obtain a battery fault recognition result; the battery fault identification result is used for representing whether the to-be-detected battery has a fault or not. By adopting the method, whether the battery has obstacles can be accurately identified.

The invention relates to the technical field of battery fault diagnosis, and discloses a battery short-circuit fault intelligent diagnosis method, system and device and a medium, and the method comprises the steps: obtaining a battery unit voltage data set with a timestamp, and generating a diagnosis sequence containing a detection node; based on the detection node set, the monitoring terminal is controlled to collect voltage data, capture current fluctuation signals and extract feature vectors, abnormal mark information is determined, and the diagnosis process is controlled according to the state of the detection node set and preset conditions; and after the diagnosis is completed, sending the feature vector to an analysis server to obtain a fault positioning identifier, storing abnormal mark information, analyzing the difference between a mark information log and a historical log library to determine a fault evolution path, and generating and filing a diagnosis report when a preset path model is matched. The system comprises an acquisition unit, a sequence construction unit and a diagnosis execution unit. The equipment comprises a processor and a storage device, and a medium stores corresponding programs. According to the invention, the accuracy, efficiency and intelligent level of fault diagnosis are improved.

The invention discloses a battery fault diagnosis method based on a graph structure and an LSTM, and belongs to the field of battery fault diagnosis, and the method comprises the steps: converting battery blocks of a battery module and a connection relation thereof into a graph model based on a battery pack space connection information representation mode of the graph structure, so as to effectively capture the space dependence between the battery blocks; meanwhile, by combining the graph neural network and the long-short-term memory network, the spatial-temporal characteristics of the battery state can be accurately extracted, the processing capacity of time sequence data is enhanced, and therefore the accuracy and comprehensiveness of fault diagnosis are improved; besides, aiming at the problem of lack of a confidence evaluation mechanism in the prior art, an accurate confidence evaluation module is designed, and the reliability and transparency of the decision are effectively improved by calculating the confidence score of the diagnosis result.

The invention relates to a battery pack fault detection method and system and an electric vehicle. The battery pack fault detection method and system and the electric vehicle are simple in structure and ingenious in design, and the safety performance of a battery pack can be effectively improved. The state of the battery pack can be comprehensively known by monitoring the total voltage, the monomer voltage, the temperature and the current in real time; by screening out the battery cells with abnormal temperature, voltage or current and then diagnosing and analyzing whether the abnormal battery cells are subjected to micro short circuit, the abnormal battery cells can be quickly identified, unnecessary inspection on normal battery cells is reduced, and the detection efficiency is improved; battery faults can be comprehensively detected through a physical model, machine learning, time sequence analysis, ICA testing and EIS testing, and the accuracy and reliability of detection are further improved; according to the method, the safety and reliability of the battery pack are improved, the intelligent level of the battery management system is enhanced, and better use experience is provided for a user of the electric vehicle.

The invention relates to a battery fault chain blocking method and system based on dynamic knowledge graph driving. The method comprises the following steps: simulating a fault process of a battery and outputting a basic physical data set; performing time sequence deformation correction on the basic physical data set to obtain an enhanced multi-source time sequence data set; constructing an initial knowledge graph by taking the fault type as a node and the propagation conditional probability as an edge weight, and updating the initial knowledge graph by using the enhanced multi-source time sequence data set to obtain a dynamic knowledge graph; simulating a three-dimensional heat spreading path in a digital twin environment according to the current fault parameter and the dynamic knowledge graph, solving an optimal control parameter and generating a physical execution instruction set; and encrypting and pushing the physical execution instruction set to a corresponding edge device for execution. According to the method, the fault propagation probability is modeled in real time by using the dynamic knowledge graph, and the intervention strategy is optimized by combining the digital twin environment preview, so that the bottlenecks of diagnosis dissection, response hysteresis and data dependence of the traditional method are broken through.