5119 results about "Battery system" patented technology

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

The invention 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 provides a battery life intelligent prediction method fusing a time convolution network and a Mama state space model. The method comprises the following steps: a TCN branch extracts local time sequence characteristics in a battery degradation process by utilizing multi-layer expansion causal convolution, and nonlinear degradation phenomena such as capacity regeneration and the like are effectively identified; the Mama branch is based on a selective state space mechanism, dynamically models a long-period dependency relationship of a battery aging track, and adaptively focuses a key degradation node through weight adjustment of context sensing. In order to enhance model robustness, a variational mode decomposition unit is integrated to carry out noise suppression and mode separation on an original capacity sequence, and the generalization ability of a cross-battery chemical system is improved in combination with normalized constraint of a hidden state matrix. The accuracy of life prediction is remarkably improved, and key features in a complex degradation mode are effectively captured; the method has excellent noise suppression capability and cross-model generalization, is suitable for multiple types of battery systems, and realizes real-time aging state evaluation.

The invention relates to an algorithm fusion and fault early warning system in a battery safety management platform, and aims to realize multi-dimensional risk identification and active response control in a battery operation process. The system comprises a multi-source data acquisition module, a fusion calculation engine, a fault early warning response module and an execution interface module. By collecting voltage, current, temperature, humidity, sound wave signals, electrochemical impedance spectroscopy, gas concentration and other multi-mode operation data, a fusion calculation engine completes time sequence correlation modeling, causal feature recognition and risk trend deduction on the basis of a constructed hierarchical algorithm fusion structure, and a risk response signal reflecting a safety state is generated; the fault early warning response module generates hierarchical control instructions corresponding to risk levels according to the signals, the hierarchical control instructions comprise virtual fusing, cooling scheduling, BMS refreshing and the like, the execution interface module issues the control instructions to a physical controller of the battery system, response behaviors are driven, and therefore intelligent, closed-loop and dynamic battery safety management is achieved.

The invention provides a dynamic reconfigurable battery energy storage system balance control method and device based on PWM duty ratio modulation. Belongs to the battery system control field. Voltage information of all single batteries in the energy storage system is collected in real time, total loop current, system operation modes and temperature distribution are monitored synchronously, a controller dynamically calculates charge state values of all the single batteries based on the information, a target object to be balanced is recognized, and meanwhile a global battery consistency evaluation index is established. The method dynamically adjusts the maximum allowable equalization current and generates an equalization topology. The modulation frequency, the duty ratio spectrum amplitude and the phase distribution are adjusted through PWM, and the maximum duty ratio is limited. All the operation instruction sets take effect synchronously at the starting moment of the next equalization period. Besides, the efficiency and working condition mapping table in the historical learning library is synchronously updated to form a closed-loop self-evolution optimization mechanism, so that the balance efficiency optimization of the system in a dynamic scene is realized, and the voltage overshoot is ensured to be within a set threshold value.

The invention discloses a power battery health state on-line monitoring and early warning method and system based on multi-data fusion. The method comprises the following steps: synchronously acquiring multi-dimensional heterogeneous data in a battery operation process through a multi-sensor data acquisition module; carrying out standardization processing and preprocessing on the multi-dimensional heterogeneous data, extracting a comprehensive feature vector and carrying out multi-sensor data fusion; a multi-sensor data fusion algorithm is adopted to carry out weight distribution on the fused comprehensive feature vector, a dynamic weight coefficient is calculated according to the sensitivity and reliability of each parameter to the health state of the battery, and a fused battery state feature descriptor is obtained; and performing mode recognition and state classification on the fused battery state feature descriptors by using a pre-established evaluation model to realize quantitative evaluation of the health degree of the battery. According to the invention, real-time monitoring, evaluation and early warning of the health state of the battery in the whole life cycle are realized, and a reliable basis is provided for safe operation and maintenance decision of a battery system.

The invention discloses a battery fault identification method based on probability labels and identification feature learning. The method is suitable for modeling and discrimination of various fault states in small sample and weak label scenes. The method comprises the following steps: firstly, acquiring key parameters such as voltage, current and temperature in an operation process of a battery system, and constructing standardized time sequence characteristic data; secondly, three types of pseudo labels are generated based on multi-source information such as alarm time difference, prediction residual error and mahalanobis distance, and a unified abnormal probability label is obtained through weighted fusion; constructing positive and negative sample pairs according to the difference between the tags, and introducing difficult samples with similar features but large tag difference to enhance the discrimination ability of the model; then constructing a twin neural network structure composed of shared parameter sub-networks, inputting positive and negative sample pairs for comparative learning, and extracting low-dimensional embedding features with clustering and distinguishability; and finally, through calculating a space distance between a new sample embedding vector and a known fault type, identification of a current fault type and evaluation of an abnormal degree are realized.

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 thermal management strategy management method and system based on an energy storage battery system, and relates to the technical field of battery thermal management, and the method comprises the steps: collecting temperature data, carrying out the preprocessing, generating and recording a measurement result and a convective heat transfer coefficient, taking the measurement result and the convective heat transfer coefficient as the input of a Kalman filter, and outputting an optimal estimation result of a battery. The temperature data refer to the environment temperature and the core temperature of the single battery, based on the optimal estimation result of the battery, establishing a fuzzy rule base for iteration and updating, outputting a dynamic temperature difference threshold value, constructing a three-dimensional thermodynamic potential function, calculating the cosine similarity of a cooling driving force vector and a cooling mode, and correcting the cosine similarity to obtain a cooling driving force vector. The mode switching list is generated, the cooling equipment decision mode is generated, starting judgment and optimization of the cooling mode are carried out, and the intelligent level, safety and energy efficiency ratio of thermal management of the energy storage battery system are remarkably improved.

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

The embodiment of the invention provides a battery system discharge control method, a battery system, a power utilization device and a battery management system. The possibility of over-discharge of a battery in the discharge process can be reduced. The battery system comprises a first battery and a second battery, the battery system comprises independently arranged energy areas, the first battery and the second battery are arranged in different energy areas, and the method comprises the following steps: acquiring a first state parameter and a second state parameter; determining first allowable discharge power of the first battery according to the first state parameter, and determining second allowable discharge power of the second battery according to the second state parameter; and determining target allowable discharge power of the battery system according to the first allowable discharge power and the second allowable discharge power, and controlling the battery system to discharge according to the target allowable discharge power.

The invention relates to a lithium ion battery life prediction method, and belongs to the field of battery life prediction and intelligent maintenance. The method comprises the steps that S1, a battery capacity degradation sequence is collected, and integrity is checked and normalized; s2, decomposing the sequence by using an improved complete set empirical mode decomposition algorithm, and dividing the sequence into a high-frequency component and a low-frequency component according to a zero-crossing rate; s3, modeling the high-frequency component: fusing multi-scale channel interactive attention, a time sequence convolutional network and a hybrid expert model, and extracting short-term fluctuation and capacity recovery features; s4, modeling a low-frequency component: introducing a two-way gating circulation unit network constrained by a double-index degradation model, and simulating a long-term trend; and S5, constructing a high-frequency migration module through tensor decomposition, improving cross-battery generalization, and fusing high and low frequency results to output a residual life prediction value. According to the method, a dual-channel framework combining signal decomposition, deep learning and physical modeling is combined, the prediction precision and adaptability under complex degradation are improved, and the method is suitable for various battery systems.

The invention relates to the field of lithium battery detection, and particularly discloses a lithium ion battery fault detection method, which comprises the following steps of: firstly, acquiring total current data of a battery pack in real time, and judging the current running state of a battery system according to the total current of the battery pack and the duration time; and when the battery system is in a standing state, calculating the voltage drop rate and the temperature rise rate of each single battery in a preset time window, and comparing the voltage drop rate and the temperature rise rate with corresponding thresholds to realize self-discharge detection and fault identification. And when the battery system is in a charging or discharging state, a deep learning algorithm is further introduced, temperature-pressure coupling time sequence fluctuation characteristics of each single battery are extracted, and temperature-pressure coupling time sequence fluctuation state consistency analysis is performed on each single battery, so that intelligent detection of faults of the single batteries in the charging and discharging process is realized. According to the method, the abnormal temperature and pressure fluctuation of the single battery in the charging and discharging process can be effectively captured, so that early warning and accurate positioning of a fault are realized.

The invention discloses an online EIS battery pack performance parameter estimation algorithm, and relates to the technical field of battery management, and the algorithm comprises the steps: generating a basic frequency conversion excitation signal through a digital signal generator, and loading the basic frequency conversion excitation signal to a working battery pack through a self-adaptive frequency sweeping strategy; acquiring a response signal, analyzing an impedance real part and an imaginary part of the battery pack at each frequency point through a digital orthogonal synchronous demodulation technology, and constructing an electrochemical impedance spectrum; extracting multi-dimensional health features from the electrochemical impedance spectrum, inputting the multi-dimensional health features into a fusion deep learning model, and estimating the state of charge and the state of health of the battery; and generating a system optimal control signal based on the estimated state of charge and state of health. According to the algorithm, portable, online and real-time detection of the performance parameters of the battery system in the working state is realized, and the application scene limitation of traditional EIS equipment is broken through; and through the optimized signal processing and intelligent algorithm, the detection stability and response speed are improved.

The invention relates to a sodium electric energy storage system dynamic partition power control method based on string type cluster level management, which comprises the following steps of: firstly, realizing physical isolation of battery clusters through a string type framework, configuring an independent power conversion module and a cluster level controller for each cluster, acquiring voltage, current, SOC (state of charge) and SOH (state of health) of each cluster in real time, and on the basis of the data, in a discharging or discharging process, determining the state of charge of each cluster; a dynamic partition strategy and automatic adjustment of power distribution are realized according to inter-cluster SOC, SOH, voltage range, temperature range, power limit and other differences, and an energy storage system is dynamically divided into a high-energy high-health area, a high-energy medium-health area, a medium-energy high-health area, a medium-energy medium-health area, a low-energy or sub-health area and a fault early warning area in a discharging and charging mode; and rapid balance of electric quantity among clusters is realized through the converter module, so that power distribution is ensured to meet power grid requirements, overcharge and overdischarge of batteries are avoided, and the service life of a battery system is prolonged.

Systems and methods for battery system management and monitoring apparatus(es) and equipment using a sensor cluster are described. The systems and methods can be used to automatically repair or otherwise address actual and predicted failure modes of the apparatus(es), which include electrical systems, power systems, energy storage systems, and other systems.

The present disclosure relates to a battery system supporting a plurality of operation modes according to the SoC. To this end, the battery system is characterized by comprising: one or more batteries supporting multiple operation modes for power storage or supply according to the state of charge (SoC); and a controller which determines an operation mode of the batteries among the plurality of operation modes according to the power supply state of a grid and the SoC of the batteries, wherein the batteries include a battery supporting an uninterrupted power supply (UPS) mode at an SoC at or below a first standard.

The invention relates to the technical field of battery system fault recognition, and provides a battery system fault recognition method and system based on a time sequence contrast learning encoder, and the method comprises the steps: employing a lightweight residual error convolution module and a time sequence Transform module which are connected in series and in parallel to extract a network, and obtaining a fault recognition result after the classification of extracted feature vectors; in the multi-layer time sequence feature extraction network training process, extracted feature vectors are mapped to another low-dimensional space through a contrast projection head to obtain contrast space features, and contrast loss is calculated through the contrast space features to enhance the separability of the feature vectors. According to the method, a light-weight convolution and Transform hybrid coding structure is fused, and a contrast projection head and a user-defined contrast loss function are introduced, so that the model can effectively distinguish normal and abnormal states in a battery system under the condition that a large number of labels are not needed, and the sensitivity and generalization ability of fault recognition are remarkably improved.

The invention provides an energy storage battery state simulation and prediction system and method based on digital twinning, and aims to solve the problems that an existing energy storage system is inaccurate in state modeling, insufficient in sensing granularity, unexplained in prediction, lagged in response and the like. According to the method, an equivalent thermal model, an electrochemical model and an aging mechanism model are adopted for modeling a battery, and a self-adaptive gating parameter fusion mechanism and data characteristics are introduced, so that cross-scale state mapping and characteristic reconstruction from a battery cell to a system level are realized; generating a baseline trajectory by using an electric-thermal-aging coupling relationship, realizing multi-time-domain closed-loop prediction by combining a data-driven residual correction mechanism, and identifying potential faults and risks in advance through a multi-time-domain anomaly judgment system; and constructing a three-dimensional topology model to dynamically display the state parameters, generating a graded alarm signal and providing operation suggestions. The system constructed by the invention has high precision, interpretability and prediction and visualization capabilities, and can significantly improve the intelligent operation and maintenance level of the energy storage battery system.

The invention relates to a fluorine-nitrogen co-doped carbon-coated positive electrode material and a preparation method thereof, and the cycle performance of the positive electrode material is effectively improved by arranging a fluorine-nitrogen co-doped carbon-coated layer on the surface of a positive electrode active material. When the positive electrode material is applied to a battery system, the cycle life of the battery can be prolonged.

The invention relates to the field of vehicle control, in particular to a battery cell safety thermal management system for an electric vehicle and an intelligent control method. According to the invention, the differential sensors are arranged in the key area of the battery system and redundancy design is adopted, so that all-directional and multi-dimensional monitoring of the temperature field of the battery is realized; a high-heat scene is pre-judged through an extreme working condition heat production prediction model, a grading early warning mechanism is combined, the heat risk grade, the battery damage stage and the user requirement of a cross-country scene are accurately matched, full-chain protection of prevention, control and emergency is achieved, the influence of excessive intervention on the cross-country performance is avoided, and system safety under the extreme working condition is ensured; the hysteresis of the prediction model is made up through a transient protection mechanism, the prediction lag of the heat production prediction model under the extreme working condition is supplemented through immediate response aiming at the sudden working condition, the thermal runaway risk caused by the time difference of a prediction-execution chain is avoided, and meanwhile, the performance is prevented from being sacrificed due to excessive protection through the strategy of combining short-time redundancy with dynamic falling back.

The invention discloses a lithium ion battery thermal runaway multistage early warning system and method and a storage medium, and belongs to the field of battery safety monitoring. The system comprises a data acquisition device, a feature extraction module, an early warning strategy module and an early warning model module. The method comprises the following steps: collecting multi-physical field parameters of heat, electricity, force and gas; screening key feature points by adopting a mutual information method; mining association rules of feature points and early warning levels based on an Apriori algorithm to generate a four-level early warning strategy; and constructing an early warning model integrating grade evaluation and remaining time prediction. According to the method, through multi-parameter coupling analysis and intelligent algorithm mining, the defects that the prior art depends on a single parameter and lacks grading capacity are overcome, early-stage, accurate and grading early warning and remaining time prediction of lithium ion battery thermal runaway are achieved, and the safety early warning precision and emergency disposal capacity of a battery system are remarkably improved.

An electrical line selection system comprising a circuit module configured to connect energy sources with multiple line outputs. The circuit module has a number of inputs configured to connect to energy sources, including rechargeable battery systems, and a number of outputs configured to connect with other electrical systems such as other battery systems, loads, charging sources, and various AC or DC power supplies. A switching circuit is contained by the circuit module and a controller manages the switching circuit based on desired connections and strategies for managing power supplies and current sinks.

The invention discloses an echelon utilization method for waste power batteries of new energy vehicles, which comprises the following steps: firstly, collecting and preliminarily classifying and screening retired batteries according to health states, and quickly eliminating potential safety hazards by using infrared thermal imaging and voltage consistency detection; comprehensive performance detection including complementary energy detection, discharge capacity test at different temperatures, direct current internal resistance measurement and charge and discharge temperature rise test is carried out; key parameters are extracted from the detection data, comprehensive performance analysis is carried out in combination with application scene requirements, and a comprehensive evaluation score is generated; selecting similar batteries to form a pre-combination unit according to the score sequence; a grouping result is optimized through a clustering algorithm in the three-dimensional performance coordinate system, and it is ensured that the deviation of core parameters of the same group of batteries is smaller than a set threshold value; the stability and the operation efficiency of the echelon utilization system are improved, the adaptive capacity of the echelon utilization system in different application scenes is enhanced, and a guarantee is provided for stable operation of the echelon utilization battery system.

The invention discloses a battery box and a battery pack for battery cell thermal runaway optimization management, and relates to the field of battery pack thermal runaway optimization, the battery box for battery cell thermal runaway optimization management comprises an outer box body; the battery cell placing space is formed in a cavity of the outer box body, the smoke exhaust channel is communicated with the battery cell placing space, an explosion-proof assembly is arranged on the outer box body, and the explosion-proof assembly comprises a first explosion-proof valve communicated with the smoke exhaust channel and a second explosion-proof valve which is communicated with the cavity of the outer box body and is different from the first explosion-proof valve in opening pressure; when the battery cell aluminum shell is damaged, high-temperature flue gas can be guided to be discharged in order, the influence of the high-temperature flue gas on a high-pressure part is reduced, and a safe pressure relief path is ensured, so that the safety redundancy and reliability of a battery system are greatly improved.

The invention discloses a two-phase electrolyte, a preparation method thereof and a liquid energy storage battery system, and belongs to the technical field of electrochemical energy storage. According to the invention, a liquid-liquid double-phase electrolyte is composed of a water-phase electrolyte and an organic-phase electrolyte, wherein the organic-phase electrolyte contains a coupling system of 1-(methyl-decyl)-3-methylimidazolium bromide, tris (methyl-decyl) methyl ammonium bromide and (10-octadecyl) alkyl trimethyl ammonium bromide in a molar ratio of (5-7): (2-4): 1. During charging, positive electrode bromide ions are subjected to an oxidation reaction to generate a bromine simple substance, and meanwhile, the bromine simple substance and a coupling system of the quaternary ammonium bromide compound and the bromine complexing agent provided by the invention form a stable polybromine complex product, so that the polybromine complex product is prevented from being migrated into a water-phase electrolyte, and the purpose of improving the stability of the water-phase electrolyte in the absence of a diaphragm is achieved. The self-discharge reaction of bromine is significantly inhibited at a high temperature exceeding the boiling point temperature of bromine, the coulombic efficiency and energy efficiency of the liquid energy storage battery are improved, the system cost is reduced, and the long-term cycle stability of the liquid energy storage battery is improved.

The invention provides a hydrogen supply control method of a fuel cell system, a controller, a system and a medium, and relates to the technical field of fuel cells. The method comprises the steps that in response to a starting instruction, the opening degree proportion of a first proportional valve is controlled to be adjusted to K11 from K10 through a first PID control parameter, K10 is the opening degree proportion of the first proportional valve which does not allow hydrogen to pass at all, and K11 is the initial opening degree proportion of the first proportional valve at the current environment temperature; obtaining a current actual pile entering pressure value and a hydrogen pile entering pressure set value of the electric pile; the second PID control parameter is used for controlling the opening proportion of the first proportional valve to be adjusted till the deviation between the actual reactor entering pressure value and the hydrogen reactor entering pressure set value is within the allowable error range, and the proportionality coefficient of the first PID control parameter is larger than that of the second PID control parameter. According to the method, the stability of hydrogen pressure is improved, so that the stability and the service life of the fuel cell system are improved.

Systems and methods for operating a vehicle power system are described. The vehicle power system includes an inverter and an electric machine. Switches and a diode are arranged in a way that allows a traction battery to be charged by either a lower voltage charger or a higher voltage charger. Additionally, the switches and diode allow the vehicle power system to heat the traction battery so that the traction battery may operate in a desired temperature range.

The invention relates to the technical field of battery systems, and discloses a multifunctional power distribution unit and method for a heavy truck type battery system, full-function architecture design is carried out, and the method comprises the following steps: designing a main loop; designing a negative electrode end detection control loop; designing a heating loop; a communication control loop is designed, a battery management system is integrated in a designed battery distribution unit, and a communication connector group is externally connected with a whole vehicle system for data interaction; designing a pre-charging loop; solidifying the box body structure and the device positions, and carrying out device layout in the box body along the box wall and the box bottom; corresponding devices are reduced according to a preset mode, the total negative relays of the pre-charging loop and the negative electrode end detection control loop are adjusted to be connected in parallel according to needs, the collecting wire harness and / or the trend of the copper bar are adjusted, and different new frameworks are derived. The platform PDU is obtained through design, the box body serves as a shared platform, the multi-framework derivation capacity is achieved, the diversity and universality of the power distribution unit are improved, and the development cost and period of cross-configuration are optimized.

The invention discloses a state monitoring and fault diagnosis method for a battery management system, and relates to the technical field of battery management, and the method comprises the steps: S1, collecting basic parameters of a battery in real time based on a sensor, and carrying out the preprocessing and integrating into a data set; s2, the charge state value, the health state value and the function state value of the battery are obtained through calculation, weighted summation is carried out on the obtained states, the battery state value is obtained through integration, and the battery state condition is fed back based on the battery state value; and S3, obtaining a state water average value of historical battery use based on the big data, comparing the obtained battery state value with the water average value, and identifying an abnormal value by using a concentration trend and a dispersion degree of the data. The fault source of the abnormal battery is accurately positioned through voltage, capacity and internal resistance tests, differential maintenance measures are adopted, blind replacement is avoided, the maintenance cost is reduced, the overall service life of the battery is prolonged, and management and maintenance of a battery system are facilitated.