280896 results about "Electrical battery" patented technology

The invention discloses a battery pack thermal runaway risk identification and early warning system and method, and relates to the related field of battery risk early warning, and the method comprises the steps: collecting an operation parameter data set of a battery pack in real time through a multi-mode sensor network, carrying out the hybrid anomaly detection of the battery pack, obtaining a real-time anomaly detection result, and carrying out the thermal state prediction of the battery pack, and constructing a thermal state evolution trend chart to perform thermal runaway risk assessment, determining a thermal runaway risk level, triggering a multi-level early warning signal according to the thermal runaway risk level, and generating a thermal runaway suppression strategy. The technical problems that in existing battery thermal runaway early warning, due to monitoring limitation of a single sensor, the false alarm rate is high, response lags behind, and battery aging cannot be dynamically adapted are solved, and the technical effects that the thermal runaway early symptom detection rate is increased through multi-mode sensing, and the early warning time is shortened are achieved.

A wearable device including at least one sensor configured to sense a physiological parameter of a user. The wearable device including a base housing and a removable housing attachable to the base housing. The base housing and the removable housing portions each including a battery and an electronic subsystem in communication with each other. The battery of the removable housing portion charges the battery of the base housing portion when the removable housing portion is attached to the base housing portion. A second embodiment includes two or more fitness trackers each having an enclosure. The enclosures of the two or more fitness trackers having complimentary shapes that form a unified enclosure when the enclosures are placed adjacently. In a third embodiment, a wearable device includes a first screen display and a second screen display. The second screen display is transparent in at least one operational mode of the wearable device.

The invention provides a battery assembly process defect real-time detection and classification method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: carrying out the standardization preprocessing of collected battery surface images and three-dimensional point cloud data; a distributed edge computing architecture is adopted for feature extraction, the distributed edge computing architecture comprises a defect propagation prediction module based on a graph neural network, inter-process defect features are constructed into a dynamic graph structure, a defect evolution rule is learned through the graph network, and feature extraction parameters are dynamically adjusted; inputting the extracted features into a deep learning detection model, carrying out feature adaptive mapping and alignment through a multi-modal spatial-temporal feature coding layer, constructing a process association graph through a dynamic reasoning layer based on causal reasoning, and calculating a propagation influence degree; carrying out hierarchical precision detection on different defect evolution probability regions by a prediction perception detection layer with a self-adaptive threshold value, and outputting a defect detection result; and classifying defect detection results based on defect classification standards.

The invention relates to the technical field of battery management, and discloses a battery energy balanced distribution and optimization method, device and equipment and a storage medium. The method comprises the following steps: performing multi-source parameter acquisition and feature decoupling on each single battery in a target battery pack to obtain a single battery state data set, and performing multi-band excitation scanning and impedance spectrum fitting to obtain internal features of the battery and decompose an attenuation trend to generate equivalent circuit model parameters; performing state mapping and capacity calculation based on the model parameters to obtain a performance evaluation result of the battery pack, and constructing an energy transfer network and identifying unbalanced propagation characteristics through performance difference analysis and consistency calculation; and temperature coupling calculation and compensation matrix reconstruction are carried out based on propagation characteristics, multi-path parallel balance control is realized, and an optimization result of the battery pack is obtained through voltage deviation calculation and polarization compensation. According to the method, the multi-dimensional characteristic parameters and the dynamic change characteristics are combined, and accurate control over battery pack equalization is achieved.

The invention relates to the technical field of battery monitoring, in particular to an electric vehicle battery state real-time monitoring system based on deep learning. Comprising a battery data acquisition module, a feature extraction module, a battery state prediction module, a fault monitoring module and a safety regulation and control module. The battery data acquisition module acquires battery parameters in real time, the feature extraction module extracts key features through deep learning, and the battery state prediction module predicts battery capacity, health state and performance degradation based on a neural network and a differential equation. The fault monitoring module adopts integrated learning to detect abnormity and output an alarm signal, and the safety regulation and control module optimizes a charging and discharging strategy through reinforcement learning and fuzzy control to guarantee safe and stable operation of the battery. According to the invention, accurate prediction and intelligent monitoring of the battery state are realized through the deep learning technology, comprehensive state evaluation and fault early warning are provided, and the accuracy and stability of battery state monitoring are effectively improved, so that the operation safety of the electric vehicle is improved, and the service life of the battery is prolonged.

The invention relates to a battery life prediction and health assessment system based on BMS expansion parameter analysis, and the system fuses deformation quantity and electrochemical parameters, and remarkably improves the modeling precision and life prediction capability of a battery health state. The acquisition module obtains battery expansion characteristics and operation parameters through a sensor or a modeling mode, and constructs a multi-dimensional time sequence data set; the processing module performs normalization and feature reconstruction on the data to form a health track sample for learning; the prediction module outputs residual life and degradation trend scores based on a deep regression model, and introduces an expansion feature sensitivity adjustment mechanism; the evaluation module evaluates the health level of the battery according to the prediction result and a threshold rule and generates a report; the regulation and control module dynamically adjusts BMS control parameters according to an evaluation result, and optimizes energy efficiency and a safety strategy; the method breaks through the dependence of a traditional BMS system on electrochemical characteristics, realizes more comprehensive and accurate intelligent sensing and life management of the battery health state, and is suitable for various BMS architecture scenes.

The invention relates to the technical field of battery temperature control early warning, in particular to an energy storage battery temperature control early warning method and system. The method comprises the following steps: acquiring an internal structure diagram of the energy storage battery; performing internal space layout analysis and inter-unit physical connection analysis on the internal structure diagram of the energy storage battery to construct a three-dimensional battery topological structure model; acquiring real-time temperature monitoring parameters of the energy storage battery; dynamic temperature distribution visual rendering is carried out on the three-dimensional battery topological structure model according to real-time temperature monitoring parameters of the energy storage battery, so that thermal state portraits of a plurality of unit areas are constructed; performing multi-period battery operation simulation on the thermal state portraits of the plurality of unit areas, and performing sliding time window temperature change situation prediction to obtain a temperature change prediction situation of each area; and carrying out continuous abnormal temperature rise trend detection on the temperature change prediction situation of each area so as to generate an abnormal temperature rise trend characteristic. According to the invention, safe and efficient pool temperature control early warning is realized.

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 relates to the field of battery fault prediction, in particular to a lithium ion battery fault prediction method and system based on a BMS. The method comprises the following steps: extracting multi-dimensional operation monitoring parameters of a battery through a BMS (Battery Management System), carrying out multi-state evolution perception and label mapping processing, and constructing a global multi-state perception map of the battery; short-term abnormal sudden change detection is carried out according to the multi-dimensional operation monitoring parameters of the battery, and normal characteristic deviation trend analysis is carried out, so that an abnormal fluctuation deviation evolution trajectory is constructed; and performing deep topological correlation learning on the global multi-state sensing map of the battery based on the abnormal fluctuation deviation evolution trajectory, performing heterogeneous node global sensing, performing abnormal behavior causal relationship mining on heterogeneous deviation nodes in the battery, and performing multi-causal fission simulation to generate a battery behavior deterioration chain under an abnormal trend. According to the method, accurate and efficient fault prediction is realized, transfer learning is carried out, and the perspectiveness of subsequent BMS fault prediction is improved.

A method for conditioning and maintaining power with predictive load compensation using an uninterruptible power supply system is disclosed. The method includes measuring an electrical input from a primary power supply using analog sensors and transmitting the input through an impedance to introduce a controlled delay. During the delay, input parameters and downstream system effects are determined based on telemetry sensor data. Digital identities are generated for the electrical input and the downstream load and compared to a stored digital identity representing historical conditions. Using the comparison, a system simulation is performed with analog twinning to predict downstream system responses. The predicted response is used to adjust a power converter parameter, modifying the electrical input to an intermediate output that compensates for voltage fluctuations and harmonic distortions. A secondary power supply with a bidirectional high-discharge battery supplements or stores energy as needed, ensuring stable power delivery to the downstream load.

An apparatus is dedicated to controlling the voltage of a battery comprising at least two modules connected in series and each comprising at least one electrochemical cell, and each coupled to voltage balancing means. The apparatus comprises measurement means for determining first voltages across the terminals of each of the modules, calculation means for measuring a second voltage across the terminals of the battery and for determining a mean voltage per module representative of the second voltage divided by the number of modules, and processor means for comparing each measured first voltage with the mean voltage per module and for delivering to the balancing means signals representative of the result of the comparison whenever the first voltage of a module is greater than the mean voltage, such that the balancing means reduce the voltage across the terminals of the module.

The invention discloses a data driving-based power battery safety risk assessment method and system, particularly relates to the technical field of battery safety monitoring, and is used for solving the problems of false alarm and missing detection caused by confusion of electrochemical relaxation effect and real fault signal characteristics in the existing standing stage fault detection method. Through multi-source data acquisition based on voltage, temperature and state of charge in a standing stage, a current slope extreme point is identified through charge and discharge mode weighted correction, a time-delay mapping relation with a voltage curve geometrical morphology abrupt change point is established, abnormal inflection points are screened, and fractal dimension attenuation characteristics are quantified; according to the method, abnormal distribution density and material damage indexes are fused to generate a dynamic threshold value, composite criterion analysis is carried out by combining frequency domain energy offset characteristics extracted by time-frequency transformation and heat conduction time-lag phase difference, relaxation interference signals are effectively stripped, hidden danger characteristics are locked, full-dimension safety state evaluation from microdefect evolution to macroscopic parameter abnormity is realized, and the safety state evaluation accuracy is improved. And the reliability of power battery fault detection under complex working conditions is improved.

An autonomous robotic vehicle with dynamic path planning capabilities, including: a dynamic path planning module configured to generate and adapt navigation paths in real time based on environmental changes and mission objectives; a sensor fusion module that integrates data from multiple sensors, including LiDAR, radar, cameras, ultrasonic sensors and GPS, to create a comprehensive environmental model; an obstacle avoidance module capable of detecting and predicting both stationary and moving obstacles and modifying the navigation path to avoid collisions; an artificial intelligence-based decision-making module for analyzing environmental data and learning from past navigation scenarios for improved performance; a decentralized control module that enables the independent operation of the vehicle in areas with interrupted or no connection to a central server; an energy management module configured to optimize battery usage by dynamically adjusting operating parameters based on real-time data and mission requirements; a cooperation module that enables communication and coordination between multiple autonomous robot vehicles for fleet management or the synchronized execution of tasks; a fail-safe module to ensure uninterrupted operation and data security in the event of communication failures or hardware malfunctions.

The invention relates to the technical field of battery management, and discloses a multi-dimensional parameter evaluation system and method based on cell thermal runaway risk detection, a thermal-electric coupling data set is constructed by collecting multi-dimensional parameter data of a cell in different charging and discharging states, and electrochemical impedance spectroscopy data in different aging states are obtained; constructing a refined multi-dimensional electrochemical characteristic space; training a cell health degree evaluation model based on the feature space, extracting a cell health degree index, and establishing a mapping relationship between the cell health degree index and a thermal runaway risk level; in combination with the real-time working condition parameters of the battery cell, a working condition self-adaptive battery cell health degree index is obtained by dynamically adjusting the weight coefficient of each feature in the health degree evaluation model, the thermal runaway risk level is predicted according to the working condition self-adaptive battery cell health degree index, and an early warning mechanism is triggered; according to the invention, accurate, real-time and dynamic evaluation of the thermal runaway risk of the battery cell can be realized, the accuracy and reliability of early warning of the thermal runaway of the battery cell are remarkably improved, and powerful technical support is provided for safety management of the battery.

The invention relates to the field of battery state evaluation, in particular to a new energy automobile battery health state evaluation method and system. The method comprises the following steps: acquiring a vehicle battery multi-mode state monitoring parameter and a historical vehicle endurance mileage log; performing battery state dynamic evolution on the vehicle battery multi-mode state monitoring parameters to obtain battery multi-mode state evolution characteristics; carrying out multi-time-point maximum endurance calculation on the historical vehicle endurance mileage log, carrying out time sequence trend evolution, and constructing a vehicle endurance evolution trend chart; performing nonlinear attenuation trend analysis on the vehicle endurance evolution trend graph, and performing battery aging mechanism evolution analysis so as to construct a battery state aging evolution knowledge graph; according to the battery state aging evolution knowledge graph, environment change influence analysis is carried out on the vehicle endurance evolution trend graph, dynamic vehicle state modeling is carried out, and a dynamic vehicle twinborn model is constructed. According to the invention, efficient and accurate battery health state evaluation is realized.

The invention relates to a battery monomer, a battery device and a power utilization device, the battery monomer comprises an electrode assembly and an electrolyte, and the electrode assembly comprises a positive pole piece and a negative pole piece; the positive pole piece comprises a positive pole current collecting part and a positive pole film layer; the positive pole film layer comprises lithium-containing phosphate with an olivine structure; the negative pole piece comprises a negative pole current collecting part and a negative pole film layer, and the negative pole film layer comprises a carbon-based material; the electrolyte comprises a carboxylic ester solvent, a first additive and fluorine-containing lithium sulfimide, and the mass content of the carboxylic ester solvent is 8%-60%; the total mass content of the first additive is 2.5%-10%, the first additive comprises 1, 3-propane sultone with the mass content larger than or equal to 0, an ethylene carbonate derivative with the mass content larger than or equal to 0 and vinylene carbonate with the mass content larger than 0, and the ethylene carbonate derivative comprises a compound shown in the formula A; the mass content of the fluorine-containing lithium sulfimide is 4%-8%. According to the invention, the rapid charging performance and the cycle performance of the battery monomer can be improved. # imgabs0 # formula A

The invention discloses a hybrid energy storage system optimization scheduling method based on AI intelligent regulation and control, and relates to the technical field of hybrid energy storage, and the method comprises the following steps: collecting real-time data, and carrying out the cross verification of data consistency through a multi-source data fusion technology; and identifying data abnormity caused by sensor faults, communication delay and environmental interference by combining adaptive threshold detection with a statistical analysis method, and eliminating abnormal data. According to the method, energy storage scheduling is optimized through data cleaning, time sequence prediction and reinforcement learning, and the intelligent management and dynamic adaptive capacity is improved. Multi-source data fusion and anomaly detection are adopted to ensure data accuracy, energy consumption is predicted by means of LSTM and Transform, and an energy storage strategy is optimized in advance. By combining reinforcement learning and system dynamic adjustment charging and discharging, the photovoltaic consumption rate is improved, the electricity purchasing cost is reduced, the SOC is intelligently controlled to be 40%-80%, and the service life of the battery is prolonged. Meanwhile, the system stability is improved through anomaly detection and correction, and the maintenance cost is reduced.

The invention discloses a day-ahead and intra-day economic dispatching method for a wind and light storage system, which relates to the technical field of wind and light storage and comprises the following steps of: establishing a dynamic aging model based on historical operation data of a battery, fitting actual attenuation characteristics of the battery through machine learning, acquiring the health state of the battery in different operation states and the change rule of the battery along with time, and calculating the economic dispatching result. Therefore, the basis of the dynamic adjustment strategy is formed. Energy storage charging and discharging are optimized through health factors, the system monitors parameters such as SOH and SOC in real time, tasks are dynamically adjusted, battery aging is reduced, stability is improved, the SOC strategy is optimized in combination with deep reinforcement learning, the service life is prolonged, and the maintenance cost is reduced. Rolling optimization scheduling and experience playback are adopted, a system can dynamically adjust a strategy, the wind and light absorption rate is improved, power grid balance is optimized, thermal power dependence is reduced, scheduling cost is reduced, extreme weather adaptability is enhanced, and green and low-carbon development of electric power is promoted.

The invention discloses a battery charging monitoring and adjusting system based on multiple areas, and relates to the technical field of battery management. According to the system, electrochemical impedance spectroscopy, infrared thermal imaging and electric heating parameters are synchronously collected, a physical connection and heat conduction topological relation of a battery pack is constructed in combination with a graph neural network, and monomer aging distribution and a cross-regional heat diffusion path are accurately recognized. Based on multi-region data aggregation, battery health protection, temperature equalization and a power grid demand target are fused, and charging current constraint and equalization priority parameters are dynamically generated. And through a fuzzy control algorithm, an active energy transfer mode and a passive risk isolation mode are intelligently switched, the energy distribution efficiency is optimized, fault spreading is blocked, the risk level of the system is evaluated in real time, a hierarchical regulation and control instruction is triggered, and the weight is dynamically adjusted and optimized through a feedback mechanism. The problems of extensive monitoring, strategy stiffness and safety response lag of a multi-area battery system are solved, the charging safety and economy in a complex scene are remarkably improved, and the service life of the system is remarkably prolonged.

The invention discloses a charging chip real-time power scheduling control method, and relates to the technical field of new energy automobile charging pile power scheduling, and the method comprises the following steps: S1, obtaining the operation parameters of charging equipment in real time, including the voltage fluctuation value of an input power grid, the output current of a charging interface, and the temperature of a charging chip; according to the method, the power grid voltage fluctuation, battery SOC / temperature and chip working condition multi-source data are collected in real time, a multi-target optimization model of dynamic weight distribution is combined, the service life loss of the battery is minimized, the charging efficiency and the power grid load are balanced at the same time, the user charging mode and the battery state difference are taken into consideration by adopting a priority scheduling strategy, and the charging efficiency is improved. Millisecond-level power fine control is achieved through cooperative adjustment of the PWM duty ratio and the switching frequency, personalized charging requirements are met, the overall energy efficiency is improved, a dual safety defense line is constructed through a graded load reduction protection mechanism and a fuzzy control algorithm, and a power distribution scheme is intelligently reconstructed when a power grid is abnormal or a battery is overheated.

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 is operable to supply power to a transceiver. The portable power case is operable to be charged using a DC power source (e.g., solar panel, wind turbine, water turbine). A plurality of portable power cases, DC power sources, and transceivers are operable to form a mesh network.

A pulse oximetry system includes a wrist portion configured for placement on a wrist of a subject, the wrist portion having a first component and a second component configured to removably secure to one another. The wrist portion can include emitter(s) and detector(s) operably positioned by the wrist portion. In some implementations, the pulse oximetry system further includes a ring member configured to secure around the subject's finger and operably position emitter(s) and detector(s) and a cable connected to the wrist portion in electrical communication with the emitter(s) and the detector(s) of the ring member and configured to transmit the signal(s) from the detector(s) to the wrist portion. The system includes a battery and hardware processor(s) configured to receive and process signal(s) outputted by the detector(s) to determine physiological parameter(s) of the subject.

The invention relates to the technical field of electric vehicle battery charging detection, and discloses an intelligent charging pile system and method integrating real-time battery state detection. A data fusion module; a battery health state prediction module; a charging optimization control module; the fault early warning module is connected with the cloud platform and edge computing cooperative processing module; the method comprises the following steps: acquiring battery data through multiple sensors, and constructing a data frame; extracting electrical characteristics, and generating a battery state vector; a digital twin model is constructed, and health state prediction is carried out; formulating a dynamic charging power regulation strategy; comparing the health trend of the battery with an expected behavior, and generating a fault early warning signal; and uploading the data and the strategy to the cloud platform, and updating the control strategy. According to the invention, a multi-sensor data fusion technology is adopted, twin modeling and a deep learning algorithm are combined, the health state of the battery is monitored in real time, the health state of the battery is comprehensively evaluated, and the fault risk of the battery is accurately predicted.

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

The invention discloses a lithium battery health state prediction method and system based on multi-parameter fusion, and relates to the technical field of lithium batteries, and the method comprises the following steps: collecting multi-dimensional parameter data in a lithium battery operation process, preprocessing the multi-dimensional parameter data, and constructing a time sequence data set; extracting multi-source degradation characteristics from the time sequence data set, and generating degradation influence factors; building a hybrid model based on an LSTM network and a Transform encoder, inputting the degradation impact factors into the hybrid model for model training until the model converges, and obtaining a lithium battery health state prediction model; and performing health detection on a to-be-detected lithium battery based on the lithium battery health state prediction model, and outputting an SOH prediction value. Multi-source battery parameters can be fused to predict the health state of the lithium battery, and the prediction precision and the real-time performance are improved.

The invention provides an optical energy storage regulation and control method and system based on multi-time scale rolling optimization in the technical field of energy storage regulation and control, and the method comprises the steps: S1, inputting a 24-hour electricity price prediction curve, a 24-hour photovoltaic output prediction curve, a 24-hour load prediction curve and an initial SOC into a day-ahead prediction model, a 24-hour SOC reference trajectory and a standard charging and discharging plan are obtained; s2, collecting feedback data, and inputting the 24-hour SOC reference trajectory, the standard charging and discharging plan and the feedback data into the intra-day prediction model to obtain a corrected charging and discharging plan; and S3, setting an SOC dynamic limit value, collecting a real-time electricity price and real-time photovoltaic output, and inputting the corrected charging and discharging plan, the SOC dynamic limit value, the real-time electricity price, the real-time photovoltaic output and the actual SOC into the real-time prediction model to obtain a charging and discharging instruction and frequency modulation output power. The method has the advantages that the precision, economical efficiency and power grid adaptability of energy storage regulation and control are greatly improved, and the attenuation speed of the service life of the battery is greatly delayed.

A battery pack includes a battery housing; a battery module disposed in the battery housing, where the battery module includes multiple battery cells, and at least one of the battery cells is a solid-state battery; and a control circuit disposed in the battery housing and configured to use the battery module to supply the electric power to the power tool. The energy W of the battery pack and the volume V1 of the battery pack satisfy the following: when the energy W is greater than or equal to 200 Wh, the volume V1 is less than or equal to 400 cm3; or when the energy W is greater than or equal to 300 Wh, the volume V1 is less than or equal to 800 cm3; or when the energy W is greater than or equal to 700 Wh, the volume V1 is less than or equal to 2500 cm3.

The invention relates to the technical field of mobile energy storage vehicles, and discloses an AI-based mobile energy storage vehicle running state monitoring and analysis system which comprises a main controller, an AI monitoring coprocessor and a state analysis coprocessor. The main controller calls a monitoring analysis instruction and sends the monitoring analysis instruction to the state analysis coprocessor; an AI monitoring coprocessor dispatches a state analysis instruction and processes multi-modal data; and the state analysis coprocessor analyzes the parameters to generate a reference monitoring signal, cooperatively monitors the battery temperature, the charging and discharging efficiency and the load fluctuation in real time, and generates an abnormal correction signal and a state control instruction in combination with an analysis optimization mode. The system realizes accurate analysis of multi-source data and dynamic control of the energy storage unit through data preprocessing, feature dimension reduction and deep neural network prediction, improves monitoring accuracy, dynamic adaptability and intelligent level, and ensures safe and efficient operation of the mobile energy storage vehicle.

The invention discloses a light storage and charging energy allocation system based on data driving and a control method, and relates to the technical field of light storage and charging energy allocation, and the method comprises the following steps: carrying out the real-time collection of the real-time power generation amount of a photovoltaic power generation system, the current power state of an energy storage battery, and the power consumption data of a load; and cleaning, normalization processing and time serialization are carried out on the collected data, so that the accuracy and continuity of the input data are ensured. According to the invention, by monitoring the power consumption data of the load in real time, combining with the long-term energy prediction model and dynamically adjusting the energy storage and discharge strategy, excessive discharge in a non-peak period is avoided, sufficient energy storage in a peak period is ensured, the power grid dependence is reduced, and the power consumption cost is reduced. Through load sudden increase detection and an adaptive scheduling algorithm, the system can quickly cope with sudden load changes, preferentially guarantee power supply of key loads, flexibly adjust a discharge mode, improve endurance time and system stability, and maximize green energy utilization efficiency.

The invention relates to the technical field of power supply devices, in particular to an energy-saving optimization method for an electric energy storage device, and the method comprises the steps: fusing a lithium ion battery solid-liquid phase change kinetic equation, a flow battery fluid mechanics equation and a flywheel rotor rigid-flexible coupling equation through a neural differential equation; constructing a dynamic meta-knowledge base containing cross-time scale nonlinear interaction features; performing dynamic parameter compensation on the polarization voltage hysteresis effect and the electrolyte flow resistance coupling effect by using a physical information neural network to generate a decoupled residual feature vector; according to the method, multi-physics field coupling modeling is achieved through the dynamic meta-knowledge base, and the energy efficiency optimization effect of the energy storage device is improved by combining the physical information neural network and the quantum optimization algorithm.