3628 results about "Battery management systems" patented technology

The present invention relates to an intelligent, context-aware battery management system embedded within a mobile device that dynamically allocates power resources based on real-time user behavior, system state, and environmental context. It incorporates a smart recognition engine that analyzes sensor-derived telemetry data to compute behavioral deviation scores, enabling the system to anticipate abnormal or emergency-prone conditions. A continuous activity classification module contextualizes user motion and geolocation to inform power policy decisions. Upon detecting significant behavioral anomalies or critically low battery conditions, an emergency mode subsystem is triggered, restricting device operations to essential functionalities while preserving energy for critical communication and navigation tasks. The system also establishes a secure, lightweight emergency communication tunnel for relaying essential metadata, including GPS and behavioral indicators, to predefined response servers.

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 relates to the technical field of equalization control of a battery management system, and discloses a lithium battery hierarchical equalization control method based on a hierarchical model predictive control algorithm, comprising the following steps: constructing a hierarchical control architecture which comprises a state monitoring layer, an equalization decision layer and an execution control layer, each layer realizes cooperative control through closed-loop data interaction; the state monitoring layer collects multi-dimensional state parameters of the lithium battery system, pre-processes the collected data and then transmits the data to the equalization decision-making layer, the equalization decision-making layer constructs a multi-target optimization model based on a hierarchical MPC algorithm, and the model takes SOC consistency, equalization energy consumption minimization and cycle life maximization of the lithium battery system as optimization targets. The abnormal state of the sensor or the balancing module can be identified in time through a fault diagnosis mechanism, and when a fault occurs, a standby model is automatically switched or a balancing task is shared through an adjacent module, so that the system is ensured not to generate abrupt reduction of balancing performance due to the fault of a single component.

The invention relates to the technical field of battery electric quantity estimation, in particular to a lithium ion battery electric quantity accurate estimation method and system based on a BMS. The method comprises the following steps: collecting BMS original data, carrying out voltage current interval slicing, constructing a charging and discharging period node map, detecting node interruption joints, comparing slope differences to identify charging and discharging turning points, dividing independent charging and discharging periods according to the turning points, and determining current change time sequence data of each period. The actual charging and discharging electric quantity is calculated through the time sequence data, the charging and discharging difference is compared, the historical input and discharging electric quantity is further inquired, the fuzzy electric quantity range of the battery is deduced, and finally the real-time remaining available electric quantity is accurately calculated according to the fuzzy electric quantity range and the actual charging and discharging electric quantity difference and is uploaded to the BMS system. According to the invention, real-time monitoring and management of the electric quantity estimation result are realized, and the intelligent level of the battery management system is improved.

The invention discloses an intelligent charging and discharging management method and system for a lithium battery, and the method comprises the steps: collecting multi-dimensional parameter data of the lithium battery, and generating a data set with a timestamp; and analyzing the data features based on the battery type classification model, and determining a battery type identifier. And utilizing the recurrent neural network to model the relevance between the capacity attenuation and the health state, and predicting the residual capacity and the health score. If the capacity or health score is lower than the threshold value, extracting the environment temperature and the load demand to generate a temperature-load feature vector; and matching the candidate strategy set from the pre-established index database through the hash table. And distributing weights according to health scores and load demands, sorting strategy efficiency and life influences by adopting a linear regression model, and screening an optimal strategy. And if the strategy calculation complexity exceeds the equipment capability, iteratively optimizing parameters by utilizing a genetic algorithm, simplifying a strategy instruction, generating a charging current, a voltage curve and a discharging rate control instruction, and executing the charging current, the voltage curve and the discharging rate control instruction in real time by a battery management system. The method prolongs the service life of the battery and improves the charge-discharge efficiency.

The invention relates to the technical field of BMS and the like, and provides a battery management system based on adaptive digital twinning, a physical layer of the battery management system comprises a battery pack, a sensor network and an edge computing node, and is responsible for data acquisition and preprocessing; the digital twin layer comprises a self-adaptive multi-scale model and a real-time data engine, battery behaviors are dynamically simulated by coupling electrochemical, thermal and aging models, a future state trajectory prediction result is output, and the real-time data engine fuses sensor data, historical data and simulation data to drive model updating; the intelligent decision-making layer comprises a reinforcement learning controller and a fault prediction module which are deployed in a local server, the reinforcement learning controller dynamically optimizes a charging and discharging strategy according to a prediction result of the digital twinborn layer and issues and executes the charging and discharging strategy, and the fault prediction module analyzes multi-source time sequence data based on an LSTM network so as to early warn thermal runaway and short circuit risks in advance. According to the invention, long-term accurate mapping and adaptive adjustment between the battery physical entity and the digital model can be realized.

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.

Online electrochemical impedance spectroscopy (EIS) circuitry and methods. For mobile installations, such as in a hybrid or electric vehicle, battery management units or battery management systems having EIS circuits can provide additional data regarding the state, health and potential failure of batteries. New topologies for the EIS circuitry are proposed, allowing voltage controlled and current controlled EIS to be performed.

The invention relates to the technical field of artificial intelligence and new energy management, in particular to a battery pack intelligent health management method, device and equipment based on a dynamic space-time diagram neural network and a computer storage medium. According to the battery pack intelligent health management method based on the dynamic time-space diagram neural network, multi-source and multi-mode data in the operation process of the battery pack are fully fused, a dynamically evolved diagram structure is constructed to describe a complex coupling relation and a degradation propagation path between battery cells, a GRU mechanism is introduced to model state evolution characteristics in a time dimension, and the battery pack intelligent health management method based on the dynamic time-space diagram neural network is obtained. The method adapts to different battery working conditions through a'pre-training-fine tuning 'migration strategy, and is finally deployed in a battery management system (BMS) to realize online health monitoring and intelligent fault early warning. The method effectively improves the precision, stability and generalization ability of health assessment of the battery pack, and overcomes the technical bottlenecks that a traditional method cannot dynamically sense the state evolution of the battery cell and is difficult to adapt to a complex operation environment.

The present application relates to a digital twin device and a digital twin-based battery temperature monitoring method. The digital twin-based battery temperature monitoring method, according to one embodiment of the present invention, may comprise the steps of: receiving real-time state information of a battery unit from a battery management system (BMS); carrying out a temperature distribution analysis of the inside of the battery unit by applying the real-time state information to a digital twin corresponding to the battery unit; and transmitting, to the BMS, a virtual temperature value of a virtual point of measurement, which has been requested for by the BMS.

A battery management system includes a battery pack and a controller in communication with the battery pack and a packing actuator of the refuse vehicle. The controller is configured to receive a state of charge of the battery pack, determine if the state of charge satisfies a lower threshold, in response to determining that the state of charge satisfies the lower threshold, prevent operation of the packing actuator, determine if the state of charge satisfies an upper threshold, and in response to determining that the state of charge satisfies the upper threshold, initiate operation of the packing actuator.

The invention discloses a battery change cabinet battery abnormity detection method and system based on a thermal image, and relates to the related field of battery safety monitoring, and the method comprises the steps: collecting a surface thermal image sequence in a battery compartment through an infrared thermal imager arranged in a battery change cabinet, and enabling the surface thermal image sequence to have a time sequence and a charging stage label; extracting temperature field features of a space-time dimension from the thermal image sequence, and identifying an abnormal state and an abnormal region of the battery based on the temperature field features; and acquiring real-time battery management system (BMS) data of the battery, performing correlation verification on the abnormal state or the abnormal area and the BMS data, and determining an abnormal level and positioning a fault battery unit according to a correlation verification result. According to the invention, the technical problems of inaccurate and untimely detection and inaccurate fault positioning in the existing battery abnormality detection of the battery changing cabinet are solved, and the technical effects of improving the accuracy, timeliness and positioning accuracy of battery abnormality detection are achieved.

The invention relates to the technical field of energy storage battery control, in particular to a high-reliability low-attenuation energy storage battery management system based on intelligent control, and the system comprises a data collection module which is used for collecting battery temperature, environment temperature, SOC (state of charge), SOH (state of health) and external mechanical pressure information; the model construction module is used for constructing a battery attenuation risk dynamic coupling evaluation model and generating a battery safety and attenuation risk dynamic evaluation diagram; the self-adaptive adjustment module adopts a cross-dimension multi-stage cooperative control strategy algorithm, temperature compensation current control and SOH grading current limiting linkage are combined, and SOC dynamic charging optimization and mechanical pressure early warning are coupled; and the evaluation report module adjusts control parameters through a dynamic optimization algorithm, generates a battery health state evaluation report, synchronously uploads the battery health state evaluation report to a cloud database, and establishes a full-life-cycle battery attenuation risk file. Therefore, the problems that in the prior art, a risk assessment model is large in limitation, a control strategy lacks collaboration, and attenuation risk prevention and control lags are solved.

The invention discloses a double-closed-loop cooperative control method of a direct-drive electric chassis and a power system, relates to the technical field of power control of electric special vehicles, and provides a rotating speed and torque double-closed-loop cooperative control architecture, and realizes double-core real-time cooperative operation and dynamic optimization of motor output characteristics through a 32-bit processor. A direct-drive transmission structure is combined, a traditional speed reduction device is omitted, a flat wire motor directly drives a rear axle through a transmission shaft, and a four-in-one controller is matched, so that the system loss is reduced by 33%, and the power density is improved by 15%; on the basis of real-time temperature / electric quantity data of a lithium iron phosphate battery management system, control parameters are adaptively adjusted, and the bottleneck of performance attenuation in a low-temperature environment of-35 DEG C is broken through; and a high-voltage interlocking and insulation monitoring module is integrated, and an emergency hydraulic system is automatically switched when an electrical fault occurs, so that multi-stage safety guarantee is formed. According to the technology, the energy recovery efficiency is remarkably improved by 20%, and the wind resistance stability and the extreme environment reliability are effectively enhanced.

The invention relates to the technical field of data processing, in particular to a lithium battery management system safety early warning method and system. The method comprises the steps that lithium battery operation original data are acquired and preprocessed to obtain monitoring data, and the monitoring data comprise voltage data, current data and temperature data; constructing a local observation time window for data at each moment, and constructing a trend fluctuation factor corresponding to each moment; combining the trend fluctuation factor to construct a temperature and pressure coupling factor; analyzing the relevance between the current and the temperature change, constructing a temperature flow coupling factor, and taking the product of the temperature flow coupling factor and a temperature-pressure coupling factor as a collaborative anomaly index; and obtaining an abnormal score of the current data by using an LOF algorithm, taking a product of the collaborative abnormal index and the abnormal score as an actual abnormal degree of the current data, and judging the state of the lithium battery based on the actual abnormal degree. The method has the effect of improving the monitoring accuracy and timeliness of the running state of the lithium battery.

The invention provides a lithium ion battery core temperature prediction method based on a finite element model, and the method comprises the following steps: 1, designing an electrochemical test experiment, and constructing a one-dimensional electrochemical model containing a lithium ion battery electrochemical heat production mechanism; 2, designing a lithium ion battery temperature test experiment, obtaining electrochemical parameters, heat transfer characteristic parameters, internal thermophysical parameters and environmental parameters of the battery under different working conditions, and obtaining a temperature change curve of the center position and the surface of the battery; and step 3, based on the heat balance equation, establishing a three-dimensional heat transfer model having the same geometric characteristics as the battery used in the experiment. According to the experimental result in the step 2, the three-dimensional heat transfer model is subjected to non-uniform region division, each region corresponds to one one-dimensional electrochemical model, and the heat production rate per unit volume is calculated by the one-dimensional electrochemical model in the step 1; introducing the heat production rate of each area into a three-dimensional heat transfer model to calculate the temperature distribution of the battery, and designing an experiment to correct a one-dimensional electrochemical model; 4, modifying the operation conditions and heat dissipation conditions of the battery, and calculating and analyzing the temperature difference delta T between the center position and the surface position of the battery under different operation conditions and heat dissipation conditions by using the three-dimensional heat transfer model in the step 3; and 5, measuring the surface temperature of the battery, and calculating the temperature of the center of the battery according to the working condition of the battery in the step 4 and the delta T corresponding to the environment temperature, thereby realizing monitoring of the temperature of the center position of the battery. According to the method, a high-precision algorithm model is constructed and verified by utilizing data accumulated in an earlier-stage experiment, the central point temperature which is difficult to directly measure in the battery is predicted only through real-time and easily-acquired battery surface temperature information, operation condition parameters and heat dissipation conditions, key thermal state information is provided for a battery management system, and the battery management efficiency is improved. The method is used for real-time safety monitoring and thermal management optimization.

The invention relates to the technical field of battery management systems, and discloses an intelligent battery management and safety control system and an electric flame gas stove. The system comprises a sensing engine for collecting battery multi-physics field data through a heterogeneous sensor array; the calculation engine is used for performing spatial-temporal feature extraction on the data to generate dynamic security boundary parameters; the analysis engine is used for analyzing the external power request and fusing the security boundary to generate a power distribution constraint set; the strategy generation engine is used for synchronously solving an optimal power output track and a thermal management control instruction in the constraint set; and the execution engine is used for executing power driving and heat dissipation adjustment. According to the system, accurate sensing of the internal state of the battery and early warning of the safety risk are achieved through the dynamic safety boundary, efficient and stable power output of the battery within the safety boundary is ensured through cooperative control of power and heat management, and the safety and energy efficiency of the whole system are 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 provides a safe communication method and system for a V2G charging pile and an energy storage system and a storage medium. The system comprises an energy management system EMS, a charging pile EVSE, a vehicle battery management system BMS and an energy storage battery management system BMS. A session key is established through certificate bidirectional authentication and ECDH negotiation, encryption and anti-replay are realized based on AES / SM4-GCM and additional authentication data, and digital signature verification and REST / TLS transmission are performed on a cross-domain instruction; and when the cloud is unreachable, the EVSE locally executes cooperative control and virtual droop voltage stabilization, or the EMS adopts model prediction control to issue an optimal charging and discharging track. According to the protocol, the confidentiality, integrity and availability of V2G communication and control and the stability of the micro-grid are improved.

The invention provides an SOC estimation method and device for charging and discharging multiple joint correction, a medium, a program product and a terminal, and the method comprises the steps: obtaining the related parameters of a battery after a charging and discharging instruction is detected, and preliminarily calculating an SOC value through an ampere-hour integral method; in the charging and discharging process, various strategies such as IC curve correction, segmented static OCV correction and discharging dynamic correction are combined, and the SOC is dynamically corrected in real time. In each correction step, the ampere-hour integral result is calibrated step by step according to various data such as the battery operation condition, the voltage and the temperature, and the real-time precision and the stability of SOC estimation are effectively improved. The accuracy and continuity of SOC estimation are improved, battery aging, temperature change and complex working conditions can be adapted, the estimation error is remarkably reduced, and the safety and reliability of the battery management system are improved. Compared with an existing method, the problems that SOC estimation is highly dependent on the standing condition, the application range of a single correction strategy is limited, and the requirement for high precision in a dynamic scene is difficult to meet are solved.

The embodiment of the utility model discloses a confluence cabinet and an energy storage system, the confluence cabinet comprises a cabinet body and a cabinet door, the cabinet body is provided with a first isolation chamber and a second isolation chamber, the cabinet door is rotatably connected with the cabinet body and is configured to seal the first isolation chamber and the second isolation chamber, and the first isolation chamber and the second isolation chamber are arranged along a preset first direction. The first isolation chamber is internally provided with a first power distribution module, the second isolation chamber is internally provided with an uninterruptible power supply, the uninterruptible power supply is close to the bottom of the cabinet body, and the uninterruptible power supply is configured as a standby power supply of the battery management system, so that the standby power supply can be provided for the battery management system, and meanwhile, the uninterruptible power supply is arranged at the lower part of the confluence cabinet; the problem that the maintainability is poor due to the fact that the uninterruptible power supply is arranged at the top of the confluence cabinet is solved, maintenance of the uninterruptible power supply is facilitated, meanwhile, the problem that the energy storage cabinet breaks down due to liquid leakage of the uninterruptible power supply can be avoided, and the reliability and safety of the energy storage system are improved.

The invention provides a battery car dynamic charging optimization management system based on battery health state evaluation, and belongs to the technical field of battery car charging management. The system comprises a data acquisition module which is connected with an environment sensing device and a battery management system and acquires environment and battery parameters in real time; the data storage module is used for storing an initial training data set, a charging and discharging data set and a battery aging feature library; the model building module is used for building a battery health state evaluation model through a migration strategy and building a response model through a modeling strategy; the health state evaluation module is used for extracting associated features with a critical damage threshold value as a core through a mapping algorithm based on the battery parameters, the environmental parameters and an aging feature library; and the charging strategy generation module comprises a strategy generation unit, a reference parameter generation unit and a dynamic correction unit. The system effectively improves the adaptability of battery car charging, guarantees the efficient and stable operation of the battery, and remarkably improves the safety and prolongs the service life of the battery.

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

The invention provides an energy coordination control system and scheduling method for multi-energy complementary new energy storage, and belongs to the technical field of new energy storage and energy technology management. The data acquisition and processing module is connected with a current and voltage sensor, a power transmitter, a meteorological station sensor and a battery management system sensor, and is used for acquiring voltage, current, power, state of charge and environmental data of photovoltaic power, wind power, energy storage and load through the sensors; and sliding window filtering and wavelet de-noising processing are carried out through the embedded processor. The method has the beneficial effects that the uncertainty of wind and light output is effectively processed by establishing a layered distributed optimization architecture and adopting a method of combining multi-objective optimization and robust optimization, and the consumption rate of renewable energy sources can be remarkably improved, the net load fluctuation can be reduced and the energy consumption can be reduced on the premise of meeting various system operation constraints. Therefore, dependence on conventional fossil energy is reduced, and the economic operation level and the environmental protection benefit of the whole energy system are improved.

The invention relates to the technical field of industrial control systems, and discloses an industrial and commercial energy storage battery management system and method.The industrial and commercial energy storage battery management method comprises the steps that a battery main loop switching device is controlled to superpose micro-disturbance signals, a voltage response sequence is collected in real time, the approximate entropy value of the voltage response sequence is calculated, the battery operation mode is determined according to entropy value threshold value comparison, and a corresponding control strategy is executed; according to the method, the electrochemical stability in the battery is directly perceived through order degree analysis of the voltage response sequence, model-free state recognition is achieved, meanwhile, a resource self-adaptive mechanism that macroscopic monitoring triggers microscopic calculation is constructed, the decision reliability is improved through electrochemical and thermodynamic signal cross validation under the complex working condition, and the method is suitable for large-scale popularization and application. And a dynamic control system with both real-time performance and safety is formed.

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 lithium battery pack health state estimation method based on data driving and equivalent circuit model fusion comprises the following steps: acquiring actual operation data of a lithium battery pack; judging positive and negative current identification charging sections, and screening effective charging sections by using SOC change and duration; extracting health factors such as capacity change rate, voltage change rate, cumulative mileage, SOC statistical characteristics and temperature statistical characteristics, and screening data driving health factors with strong correlation by adopting a Pearson's correlation coefficient; a second-order RC equivalent circuit model is constructed, model parameters are dynamically identified through FFRLS, and equivalent circuit health factors are obtained; fusing the data-driven health factors and the equivalent circuit health factors into a comprehensive feature set; and constructing a GRU-Attention model, capturing a long-term and short-term dependency relationship of data by adopting the GRU, enhancing feature expression through Attention, and outputting the predicted health state of the lithium battery pack. The method achieves the high-precision estimation of the health state of the lithium battery pack, and is suitable for the intelligent health prediction of a new energy automobile battery management system and energy storage equipment.

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.

The invention provides an energy storage battery management system and a control method, belongs to the field of energy storage battery management, and is used for solving the problems of low battery state estimation precision, insufficient fault diagnosis sensitivity and poor full life cycle adaptability in related technologies. By combining a neural network-Kalman filtering cascaded state estimation algorithm and a twin structure feature coding fault diagnosis algorithm and matching with an edge-cloud collaborative model self-evolution mechanism, accurate estimation of a battery state and timely diagnosis and positioning of a fault are realized, and the full-working-condition adaptability and operation reliability of a system are improved.

The invention discloses an energy storage system and a parallel operation control method thereof. The system comprises a battery cluster, a battery management system and an energy storage converter. The battery management system is respectively connected with the battery clusters and the energy storage converter, and the plurality of battery clusters are connected with the energy storage converter through the coupling circuit. Wherein a pre-charging equalization loop is arranged between the battery cluster and the energy storage converter, and the battery management system is used for controlling connection or disconnection of a circuit. The battery management system is also provided with a threshold module which is integrated with a plurality of parallel operation thresholds and is used for judging the switching-on or switching-off time of the contactor. By constructing a coupling system containing a pre-charging equalization loop, the problems of impact current and device damage caused by large inter-cluster voltage difference in the parallel operation process of a traditional multi-battery energy storage system are solved; and meanwhile, through a strategy of setting multiple parallel operation threshold values, targeted current balancing can be carried out according to different dynamic working conditions and electric energy parameter differences, the impact current is prevented from being too high, components are protected, and the operation stability of the system is improved.