10638 results about "Charge and discharge" patented technology

The invention discloses a self-generating and self-using distributed photovoltaic power station power anti-countercurrent control method and system, and relates to the technical field of photovoltaic anti-countercurrent control, and the method comprises the steps: obtaining the real-time load power of a grid-connected point of a power grid and the output power of a photovoltaic inverter, and calculating the net load power deviation; when the net load power deviation is smaller than a preset countercurrent risk threshold value, it is judged that a countercurrent risk exists, and an anti-countercurrent adjusting instruction is generated; in response to the anti-countercurrent regulation instruction, dynamically correcting the maximum output power limit value of the photovoltaic inverter, and synchronously activating the charge and discharge compensation mode of the energy storage system; an anti-countercurrent control coefficient is calculated, and the power reduction proportion of the photovoltaic inverter and the compensation power of the energy storage system are synchronously adjusted based on the anti-countercurrent control coefficient, so that the power flow of the grid-connected point of the power grid is kept to be zero or forward flow; according to the method, dynamic and accurate cooperation of photovoltaic reduction and energy storage compensation power in time and magnitude is realized, and the efficiency of a self-generating and self-using distributed photovoltaic power station is improved.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

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

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

The invention relates to the field of power battery energy management, in particular to a power battery pack charging and discharging efficiency dynamic optimization system and method, and the system comprises a central control unit, a multi-dimensional sensing module, a dynamic charging and discharging control module, a working condition-heat dissipation cooperation module, a parallel cell equalization module and an aging adaptation optimization module. The multi-dimensional sensing module is used for collecting basic parameters, polarization characteristics, parallel state, aging degree and environmental parameters of a battery pack, the dynamic charging and discharging control module is used for collecting data, the working condition-heat dissipation coordination module adopts a three-stage heat dissipation framework, and the parallel battery cell balancing module is used for balancing the battery pack through a dynamic current distribution and voltage active balancing double-coordination mechanism. The aging adaptation optimization module is used for grading and differentially optimizing charge and discharge parameters and heat dissipation strategies based on the aging degree of the battery cell, so that the problems of low charge and discharge efficiency, poor wide temperature range adaptability, weak parallel consistency, fast aging attenuation and multi-factor coupling deterioration of the power battery pack are solved.

The invention discloses a lithium battery temperature state estimation method based on a physical information neural network, and relates to the technical field of lithium ion battery temperature state estimation, and the method comprises the following steps: obtaining multi-working-condition lithium battery charging and discharging data, and carrying out sliding window denoising and abnormal data elimination preprocessing; a battery thermal model containing total irreversible heat, reversible heat and heat dissipation is constructed by combining heat production and heat dissipation mechanisms, and a temperature change thermodynamic equation is obtained; establishing a physical information neural network, based on a residual network, embedding a time-varying internal resistance module and a time sequence feature extraction module based on the Arrhenius law, constructing a multi-component total loss function, and optimizing parameters through adaptive weight adjustment and an Adam algorithm; temperature state estimation is realized in training and prediction stages, the model is optimized and parameters are stored in the training stage, and a temperature result is output and verified in the prediction stage. The method gives consideration to both physical consistency and data fitting precision, and can support thermal management of the battery.

The invention provides a lithium battery health state estimation method based on a long short-term memory network, and relates to the technical field of battery health state estimation. The method comprises the following steps: firstly, carrying out a charge-discharge cycle test on a high-health-degree sample battery, collecting operation and working condition data, extracting indirect and composite health factors including voltage differential, charging time, temperature change rate and the like, and constructing a health factor set; key health factors strongly related to the health state of the battery are screened out by adopting a dynamic double-threshold correlation analysis method, and a time sequence sample set is formed; a multi-output prediction model combining a double-layer gating circulation unit, an attention mechanism and output uncertainty estimation is constructed, and a sparrow search algorithm is introduced to carry out adaptive global optimization on model hyper-parameters. And predicting the health state of the target lithium battery by using the optimized model, and analyzing the deviation between a predicted value and a true value to complete the judgment of the final health state of the target lithium battery.

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 invention provides a battery health state evaluation method, device and equipment and a medium, and the method comprises the steps: collecting multi-source parameters of a power battery in a charge-discharge cycle process, the multi-source parameters comprising voltage, current, temperature and electrochemical impedance spectroscopy; performing feature engineering processing on the multi-source parameters based on a battery aging mechanism, and extracting key feature parameters; the key characteristic parameters are input into a multi-physics field coupling model to simulate thermoelectric coupling behaviors of the battery under a dynamic load, lithium ion concentration distribution and temperature distribution in the battery are obtained, and the multi-physics field coupling model is used for representing a coupling relation between an electrochemical field and a thermal field of the battery; and fusing the key characteristic parameters with data output by the multi-physics field coupling model, and determining a health state evaluation result of the power battery. According to the invention, the accuracy of power battery health state evaluation is improved.

The present invention relates to the technical field of power distribution networks. Disclosed are a multi-energy storage power distribution network voltage regulation method and apparatus, and a device. The method comprises: establishing a voltage optimization model for a power distribution network, converting an energy storage constraint in the voltage optimization model of the power distribution network on the basis of physical information of an energy storage system, establishing a security model of the energy storage system, forming a training guide model of the energy storage system on the basis of a Markov decision model, training each energy storage system by using a TD3 algorithm, sharing network parameters obtained through training between different energy storage systems, and adjusting the voltage of the power distribution network. By means of optimizing an operation strategy of an energy storage system, the present invention realizes efficient and sustainable operation of the energy storage system. A charging and discharging strategy of the energy storage system is optimized by means of a DRL algorithm having shared parameters, improving voltage stability of the power distribution network, reducing energy loss, improving the overall efficiency of the system, enhancing the collaboration performance of multiple energy storage systems, and optimizing control of the energy storage system in an active power distribution network.

The invention provides a lithium battery internal short circuit fault early warning and positioning method and system based on deep learning. The early warning and positioning method comprises the following steps: step S10, multi-dimensional data semantic acquisition and working condition self-adaptive preprocessing; step S20, collaborative extraction and alignment of cross-scale spatio-temporal features; step S30, performing dynamic threshold adaptive internal short circuit early warning and feedback optimization; s40, fault accurate positioning and verification of topology perception are carried out; and step S50, performing embedded collaborative optimization and self-diagnosis deployment. According to the invention, early weak signals of an internal short circuit fault can be found in time, and the efficiency and timeliness of battery safety monitoring are improved; the sensitivity of early warning is improved; the robustness in high-temperature, low-temperature and rapid charging and discharging environments is enhanced; and the positions of the single battery and the electrode with the fault can be accurately identified.

The invention discloses a lithium ion battery life prediction method and system based on health state detection, and relates to the technical field of batteries, and the method comprises the steps: collecting a plurality of real-time operation data of a lithium ion battery in a charge-discharge cycle process, and obtaining a battery state data set; traversing the battery state data set to extract a health characteristic parameter set for health assessment, and obtaining a health state value of the battery; performing cycle use prediction according to the health state value, obtaining a battery residual cycle prediction frequency, performing conversion according to the use duration in combination with the battery use frequency, and generating battery residual use prediction time; and calculating a battery life decline rate and combining with the battery residual use prediction time to perform battery life prediction, generating a residual use life estimation value to perform health state evaluation on the battery, and generating a battery life report. The technical problems of inaccurate battery health assessment and insufficient life prediction precision in the prior art are solved, and the technical effects of improving the battery health assessment accuracy and the life prediction reliability are achieved.

The invention discloses an intelligent energy storage system multi-target hierarchical scheduling method and system oriented to source network load storage cooperation, and belongs to the technical field of energy storage system optimization control. The method comprises three levels of day-ahead layer multi-objective game optimization, intra-day layer rolling correction optimization and real-time layer adaptive droop control. The day-ahead layer establishes three objective functions of economy, environmental protection and smoothness, and solves and outputs a day-ahead charging and discharging power plan by using a Nash negotiation algorithm. And the intra-day layer obtains ultra-short-term prediction data of the source load, performs rolling correction on the day-ahead plan by adopting a model prediction control method, and outputs a corrected real-time power instruction. The real-time layer collects power grid frequency deviation and a battery health state value, calculates an adaptive droop coefficient according to the health state value, and superposes and outputs primary frequency modulation response power and a real-time power instruction. According to the invention, source network load storage collaborative optimization is realized through multi-time scale hierarchical scheduling, and the service life of an energy storage system is prolonged through adaptive droop control based on health state perception.

The invention relates to the technical field of micro-grid dispatching, and particularly provides an intelligent dispatching method and system for a photovoltaic-storage-diesel micro-grid, and the method comprises the steps: obtaining the operation data of an energy storage battery, the operation data of a diesel generator, and the global operation data of the micro-grid; evaluating the power response capability of the energy storage battery according to the operation data of the energy storage battery, and evaluating the quick response capability of the diesel generator according to the operation data of the diesel generator; analyzing the stability risk level of the micro-grid according to the global operation data, the power response capability and the quick response capability of the micro-grid; adjusting scheduling optimization target emphasis according to the stability risk level; emphatically acquiring an energy storage battery charging and discharging instruction, a diesel generator starting and stopping instruction and an output instruction according to the adjusted scheduling optimization target, and executing all the acquired instructions; according to the method, the problem that the system stability is sacrificed due to blind pursuit of cost minimization in a traditional strategy can be avoided.

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

The present invention relates to the technical field of batteries, and provides a battery cover plate, a battery and a battery pack, the battery cover plate comprises: a cover plate body provided with a through hole; the sealing plate is arranged at the through hole, and the sealing plate is in sealing connection with the through hole; the boss is arranged on the outer side of the sealing plate in the first direction, the boss extends out of the through hole in the first direction, a plurality of convex ribs with closed outlines are integrally formed on the end face of the side, away from the cover plate body, of the boss, and the first direction is the thickness direction of the cover plate body; the surface area of the outer surface of the boss is increased through the existence of the convex ribs, meanwhile, the heat dissipation area of the battery cover plate is greatly increased, heat generated by a system can be rapidly conducted and dissipated in the charging and discharging process of the battery, the working temperature of the battery is effectively reduced, the battery can stably adapt to the high-rate rapid charging technical scheme, and in addition, the service life of the battery cover plate is prolonged. Through the design, the surface of the boss can be coated with the structural adhesive, and the bonding area of the structural adhesive and the boss is remarkably increased.

The invention provides a large-scale ultrafast charging facility vehicle network interaction-oriented adaptive regulation and control system, and belongs to the technical field of charging pile load scheduling. During working, a multi-source data acquisition and vehicle network state sensing module acquires and fuses multiple types of data, and a vehicle network interaction strategy optimization module generates a strategy according to the data; the power dynamic distribution module distributes power according to a strategy, and the energy storage coordinated control module optimizes energy storage charging and discharging. The real-time response and feedback module quickly responds to a power grid instruction and corrects a strategy, and the security and protocol adaptation module guarantees security and docking. During use, after the terminal is started, fusion data is automatically collected, a regulation and control strategy is generated and executed, instructions are responded and corrected in real time, and safety and cross-platform adaptation are guaranteed in the whole process.

The invention discloses a wind and light storage station power reporting and tracking method based on energy storage rapid power response. The method comprises the following steps: acquiring wind and light power probability prediction data, power grid real-time operation state data, station operation state data and power grid examination allowable power deviation data in a current scheduling period; generating a dynamic power reporting interval of the next scheduling period based on the wind-solar power probability prediction data, the power grid real-time operation state data, the station operation state data and the power grid examination allowable power deviation data; and based on the dynamic power reporting interval and the real-time operation data of the wind and light storage station, a flexible control strategy is adopted to control the charging and discharging behaviors of an energy storage system in the station. By generating the dynamic power reporting interval with self-adaptive change and adopting the flexible control strategy, the operation condition of the energy storage system can be obviously optimized, the fluctuation of the wind and light power can be effectively stabilized, and the economy and reliability of the operation of the station can be improved on the premise of ensuring that the wind and light storage station tracks the power grid assessment requirement.

The invention discloses an energy storage battery health feature extraction and state evaluation method based on transfer learning. The method comprises the following steps: S1, constructing a source domain health feature library and pre-training a model; according to the method, dependence on complete cyclic data is broken through, high precision and robustness are still achieved under the conditions of data sparsity and working condition difference, and the method is suitable for intelligent operation and maintenance and predictive maintenance of an energy storage power station. And meanwhile, common incomplete and partial charge and discharge data fragments under actual working conditions can be directly utilized for feature extraction and state evaluation, dependence on complete charge and discharge cycles is avoided, and the application scene of the data driving method is greatly widened.

The invention belongs to the technical field of power dispatching control, discloses an intelligent dispatching accident plan optimization method based on deep learning, and aims to solve the problems that traditional power accident dispatching depends on artificial experience, the plan generation efficiency is low, and the current situation of wide application of distributed photovoltaic and energy storage is difficult to adapt. According to the core technical path, on the basis of massive historical dispatching accident data, accident processing key features are automatically mined through a deep learning model, distributed photovoltaic output and energy storage charging and discharging features are fused, and a multi-source feature learning framework considering power flow balance in the accident state is constructed; and generating a dynamically optimized structured plan. By applying the method, the generation efficiency and accuracy of the accident plan can be remarkably improved, the accident handling speed is increased, the power failure duration and the economic loss are effectively reduced, meanwhile, the power flow stability of the power system after distributed energy access is guaranteed, and finally the overall reliability and the safe operation level of the power system are improved.

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 a dynamic thermal management and risk pre-judgment system for a lithium battery energy storage system, and relates to the technical field of lithium battery energy storage, and the system comprises a thermal field monitoring module which generates a real-time three-dimensional thermal field distribution map of a battery pack; the dynamic regulation and control module is used for self-adaptively regulating the heat dissipation power and the refrigerant flow in combination with the charging and discharging states of the battery and the environmental parameters; the risk assessment module is used for constructing a thermal runaway risk grade assessment model and generating a risk probability value; the early warning response module triggers a multi-stage early warning mechanism according to the risk probability value, and is linked with the dynamic regulation and control module to execute an emergency heat dissipation strategy to generate a risk disposal scheme; and the data archiving module is used for storing data. According to the invention, the whole-process intelligent design of monitoring, regulation and control, evaluation, early warning and archiving is provided, the thermal management precision, the risk prevention and control capability and the operation economy of the lithium battery energy storage system are comprehensively improved, and a key technical guarantee is provided for large-scale energy storage application.

The invention relates to the field of batteries, in particular to a preparation method and system of a silicon-carbon material, and the preparation method comprises the following steps: pretreating porous carbon, conveying the pretreated porous carbon to a fluidized bed reactor, sequentially introducing a silicon source gas and a carrier gas for silicon deposition treatment, heating, introducing a carbon source gas, adjusting the flow of the carrier gas for carbon coating treatment, and discharging and cooling to obtain a finished product, meanwhile, reaction tail gas is recycled. The preparation system comprises a gas inlet unit, a fluidized bed reactor unit, a tail gas circulation unit, a feeding preheating unit and a discharging unit. The fluidized bed reactor comprises an outer shell, an up-down stirring device, a heat exchange assembly and an air distribution assembly, and efficient reaction and temperature control can be achieved. The silicon-carbon material prepared by the method has excellent electrochemical performance and is suitable for a lithium ion battery negative electrode material. The unique porous structure and the uniform carbon coating layer effectively improve the conductivity and the structural stability of the material, and meanwhile, the volume expansion effect of silicon in the charging and discharging process is reduced.

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

The invention provides a coordination control method and device of a photovoltaic energy storage inverter for automobile charging based on artificial intelligence, and belongs to the technical field of automobile photovoltaic energy storage coordination control, and the method comprises the steps: monitoring the output power of a photovoltaic array, the state of charge (SOC) of an energy storage system, the state of a power grid and the power demand of a load in real time; a reinforcement learning algorithm is introduced, weather forecast and historical load data are combined, photovoltaic output and load demands in a future time period are predicted, and a power distribution instruction is generated; deciding a current operation mode and generating a mode switching instruction; seamless switching between grid connection and grid disconnection is controlled, and after switching is completed, the charging and discharging proportion of the lithium battery and the super capacitor is coordinated; integrating the energy storage real-time state and the power distribution instruction, and optimizing and adjusting the control parameters of the inverter in real time; continuously monitoring a running state and a switching process, and starting a standby mode when a fault is detected; and the data is uploaded to a monitoring platform. The system operation is efficiently optimized, the service life is prolonged, and stability is guaranteed.

The invention belongs to the technical field of energy storage system battery management and state monitoring, and discloses an energy storage system battery monomer consistency detection method and system considering charging and discharging time sequence characteristics. According to the method, historical operation data of each monomer is acquired and preprocessed, an equivalent circuit model and OCV-SOC mapping are established, and the SOC and ohmic internal resistance time sequence is estimated by adopting filtering recursion; screening key influence factors based on correlation analysis and constructing a consistency detection database; a charging and discharging process is extracted according to a single voltage rising edge / falling edge, and a complete charging and discharging cycle time sequence sample is generated; and clustering the samples, calculating the distance to a clustering center, judging an outlier according to an outlier threshold value, positioning inconsistent monomers, and performing adaptive optimization on the clustering category number and the threshold value to output a final result and an alarm level. The method is suitable for a large-scale energy storage operation scene, the detection accuracy, robustness and traceability can be improved, and the safety risk is reduced.

The invention relates to the technical field of intelligent automobiles, in particular to a battery performance test method and system for an intelligent automobile, and the method comprises the steps: obtaining the operation data of each single battery of the intelligent automobile under the current driving working condition and the working condition statistical data of the automobile, and constructing an input feature set, standardizing the input feature set, and inputting the standardized input feature set into a battery performance evaluation model to obtain key performance indexes of the single battery; dynamically adjusting a battery charging and discharging control strategy according to the key performance index and a real-time load demand under the current driving condition, verifying the accuracy of the key performance index by comparing the voltage fluctuation amplitude and the temperature change rate of the battery before and after adjustment, comparing with a preset safety threshold value, and if the threshold value range is exceeded, determining that the battery is in a charging state. If yes, generating corresponding performance early warning information; the method can adapt to complex and changeable driving working conditions of the intelligent automobile, and the precision and the real-time performance of battery performance evaluation are remarkably improved.

The invention discloses a photovoltaic cluster flexible grid-connected regulation and control method and system, and the method comprises the steps: collecting the state data of a power distribution network node and a photovoltaic cluster, carrying out the data cleaning and timestamp alignment preprocessing, and obtaining a system state comprehensive data flow; constructing a multi-target optimization model containing power grid friendliness and photovoltaic power generation benefits based on the system state comprehensive data flow, and solving by utilizing a reinforcement learning algorithm to obtain a control strategy of a photovoltaic inverter and an energy storage unit; according to the control strategy, active power output and reactive power output of the photovoltaic inverter and charging and discharging power of the energy storage unit are controlled in a coordinated mode, and flexible grid connection of the photovoltaic cluster is achieved; and based on system response data, optimizing a control strategy by adopting an optimal decision tree algorithm, updating the reinforcement learning model, and establishing an optimized experience knowledge base. According to the invention, efficient flexible grid-connected regulation and control of the photovoltaic cluster are realized, and the problems of voltage out-of-limit, reverse power transmission and the like when large-scale photovoltaic access to the power distribution network are solved.

The invention relates to the technical field of energy storage system scheduling, in particular to an energy storage system scheduling method and device, computer equipment and a medium, and the method comprises the steps: obtaining battery operation parameters and prediction data of an energy storage system, inputting the battery operation parameters into a pre-established battery digital twinborn life model, and calculating a life loss factor, and according to the life loss factor and the prediction data, determining a scheduling optimization strategy which enables the objective function to be optimal, and according to the scheduling optimization strategy, controlling the energy storage system to execute charging and discharging operation. Therefore, according to the method, the digital twin model and the battery operation parameters are fused, the multi-objective optimization function is constructed, and the scheduling optimization strategy is dynamically adjusted, so that the operation state of the energy storage battery can be accurately sensed in real time, and the dynamic optimization balance of the scheduling strategy between the economic benefit and the battery life loss is realized; therefore, the scheduling accuracy and the operation stability of the energy storage system are 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.