1765 results about "Battery energy storage" patented technology

The invention relates to the technical field of battery energy storage, and discloses an energy storage operation optimization method and system considering battery life loss, and the method comprises the steps: initializing battery energy storage electrical parameters, constructing a battery life loss model, and obtaining the health state change of a battery; constructing a battery operation cost model, defining a multi-objective optimization algorithm and constraint conditions, and optimizing the battery operation cost; and carrying out adaptive scheduling through a model prediction control and reinforcement learning algorithm, and determining an optimal charging and discharging strategy of the energy storage system. By constructing the battery life loss model and the multi-target optimization algorithm, economical operation of the energy storage system and prolonging of the battery life are achieved, the charging and discharging strategy of the energy storage system can be dynamically adjusted according to the power market cost fluctuation, the load requirement and the battery health state, and the operation cost of the energy storage system is reduced.

Disclosed a decision-dependent chance-constrained optimal model for enhancing resilience of power distribution system under renewable generation uncertainty through strategically setting-up and activating dispatchable diesel generators, renewable distributed generations, battery energy storage systems, and switchable devices. By incorporating the information of decision variables, a moment-based ambiguity set is employed to depict the uncertainty arising from renewable distributed generators. By leveraging convex approximations to handle the considered joint chance constraints, the disclosed model is transformed into a tractable mixed-integer second-order conic programming problem to be solved.

The invention relates to the technical field of battery management systems, in particular to a distributed battery state estimation and compensation method based on voltage and current sampling, which comprises the following steps: constructing an original characteristic matrix by synchronously acquiring voltage and current waveforms and extracting ripple characteristics and slope abrupt change points, and generating a characteristic tensor with environment correction through a coupling compensator; then constructing a state parameter space based on adaptive particle injection and an electrochemical-thermodynamic coupling model; adopting a multi-source data fusion engine to realize distributed collaborative estimation of a health state index and a charge state confidence interval, and completing SOC, SOH and temperature trend prediction and residual compensation correction through a multi-state dynamic prediction model; and based on the prediction result and the uncertainty boundary, constructing an energy compensation optimization model, and searching a Pareto optimal compensation strategy. The method can be widely applied to intelligent management and control in the fields of battery energy storage systems and electric power equipment.

The invention discloses a battery energy storage safety management system based on a BMS, and relates to the technical field of battery energy storage safety management, the system comprises a data acquisition module, a data analysis processing module, an equalization strategy generation module, an equalization execution module and a safety monitoring early warning module; operation parameters of the battery pack and the single batteries are comprehensively acquired through the data acquisition module, the actual operation state of the battery pack is accurately sensed, health factors are calculated by comprehensively considering multiple factors, weight coefficients are dynamically adjusted, the characteristics of the single batteries are comprehensively considered, and the charging and discharging states of the battery pack are calculated according to the health factors, the charging and discharging stages and the environment temperature. The equalization strategy and current are determined through a self-adaptive algorithm, dynamic response to the running state of the battery pack and environment change is achieved, equalization operation is accurately executed through the equalization execution module, accurate equalization control is achieved, and through real-time monitoring of the safety monitoring and early warning module, safe running of the battery in different environments is guaranteed, the performance of the battery is improved, and the service life of the battery is prolonged.

The invention discloses a DC bus voltage fluctuation suppression method of a renewable energy hydrogen production system, and relates to the technical field of renewable energy electrolysis hydrogen production. The method comprises the following steps: collecting system operation parameters in real time; bus voltage disturbance power is estimated based on a disturbance observer, and frequency decomposition is carried out; fusing the ARIMA model and the LSTM model to predict the load power, and calculating a prediction deviation; dynamically setting upper and lower limits of a bus voltage control interval according to the predicted deviation and the operation state; a multi-objective optimization function is constructed, bus voltage disturbance power, prediction deviation and a control limit value are comprehensively considered, and power regulation instructions of battery energy storage, a fuel cell and an electrolytic cell are solved; and executing power regulation control to realize bus voltage stabilization. The method can improve the grading response capability of voltage disturbance, the prediction accuracy of load power change, the adaptability of the control boundary and the overall operation stability of the system, and is suitable for the electric energy quality control of the hydrogen production system in a multi-source fluctuation scene.

The invention provides a battery energy storage health state and residual life prediction method driven by artificial intelligence. The method is applied to the technical field of artificial intelligence, and comprises the following steps: obtaining time sequence data of various characteristics and state information of a historical time point of a battery, and preprocessing the time sequence data; inputting the preprocessed time sequence data into a feature extraction model based on a multi-scale patch hybrid network for feature extraction; in the self-supervised comparative representation learning stage, a nearest neighbor and a farthest neighbor which are meaningful semantically are found for each sample by utilizing learned feature representation, and in the self-supervised classification stage, classification is carried out based on information of the nearest neighbor and the farthest neighbor of all feature samples, and a classification model is trained through a self-defined loss function; and performing classification prediction on the new time sequence data of the battery by using the trained model, and outputting the health state of the battery. According to the invention, the accuracy, stability and generalization ability of battery energy storage health state monitoring and residual life prediction are improved.

The invention relates to the technical field of battery energy storage, and discloses a battery early-stage micro short circuit fault detection method and device, computer equipment and a medium, and the method comprises the steps: respectively obtaining the charging process sample data of a normal battery and a fault battery; extracting fault characteristic features from the charging process sample data, and dividing the fault characteristic features into a training set and a test set; training a preset initial boosting tree model by adopting the training set to obtain a trained to-be-tested boosting tree model; testing the boosting tree model to be tested by adopting the test set to obtain a tested battery fault prediction model; and acquiring voltage and current time sequence data when the to-be-predicted battery is charged, and inputting the voltage and current time sequence data when the to-be-predicted battery is charged into the battery fault prediction model so as to detect the fault state of the battery. The method focuses on pre-extraction of fault features, model parameters are small and easy to deploy, and normal operation of the battery pack is ensured through early diagnosis of the micro short circuit fault of the battery.

A frequency regulation control method and apparatus, a device, and a storage medium. The method comprises: according to the frequency at a current moment and the calibration frequency of a wind / photovoltaic grid-connected system, determining the frequency deviation value at the current moment and, according to the frequency at the current moment and the frequency at the previous moment, obtaining a frequency deviation change rate (S110); if the absolute value of the frequency deviation value is greater than the absolute value of a preset frequency regulation threshold value, then, according to the frequency deviation value and the frequency deviation change rate, determining inertia response power (S120); according to the frequency deviation value, the preset frequency regulation threshold value, and a preset system maximum frequency deviation, determining droop response power (S130); according to the state of charge of battery energy storage, the inertia response power, and the droop response power, determining battery energy storage target charging and discharging power (S140); and, according to the battery energy storage target charging and discharging power, regulating the frequency of the system (S150).

The invention discloses a battery energy storage power station risk evaluation method based on fuzzy comprehensive evaluation, which belongs to the field of battery energy storage power station safety risk evaluation, and comprises the following steps: comprehensively considering risk factors influencing the safety of a battery energy storage power station, carrying out induction and classification on the risk factors, and selecting representative index parameters; and establishing a battery energy storage power station risk evaluation index system. And determining subjective weights and objective weights of the evaluation indexes through an analytic hierarchy process and an entropy weight method according to the established evaluation indexes of the battery energy storage power station. And calculating the comprehensive weight of each evaluation index of the battery energy storage power station by combining the subjective weight and the objective weight of the evaluation index of the battery energy storage power station with a game theory. According to a fuzzy comprehensive evaluation method, a fuzzy relation matrix is established by using a trapezoidal membership function, and the safety risk evaluation level of the energy storage power station is determined according to a maximum membership principle. Guidance is provided for operation and maintenance of the battery energy storage power station, and the safety of system operation is improved.

Embodiments provide modular and reconfigurable battery energy storage systems (RBESSs) exhibiting improved performance and power management capabilities. A power management system is provided to determine a power configuration for a plurality of power storage modules, each having a power storage device (e.g., a battery). The optimized power configuration balances a power load across active power storage modules (e.g., power storage devices being charged or discharged). The power management system is configured to control each power storage module / device on an individual basis, thereby enabling a greater degree of control and flexibility to achieve optimal balancing across a plurality of separate power storage devices within a power system (e.g., a battery pack).

The invention provides a lithium ion battery energy storage cabinet fire early warning system based on multi-source data fusion, and belongs to the technical field of fire early warning. According to the system, through multi-source sensor data fusion and deep learning analysis, the state of a battery in the energy storage cabinet is monitored in an all-around mode, the accuracy and reliability of thermal runaway early warning are improved, and operation safety of energy storage facilities is guaranteed; the system comprises a sensor module, a data acquisition module, an image acquisition module, an edge calculation module, a sound-light alarm module and a remote monitoring module. The lithium ion battery energy storage cabinet fire early warning system has the advantages that the deep learning and multi-source data fusion technology is adopted by the lithium ion battery energy storage cabinet fire early warning system; according to the invention, the arrangement scheme of the industrial camera is innovatively designed; according to the invention, a multi-scale-based early warning evaluation framework is innovatively designed; according to the invention, an edge computing architecture is adopted and an Internet of Things cloud platform is combined, so that an integrated solution from data acquisition, preprocessing and deep learning analysis to remote monitoring is realized.

An electrified vehicle include a chassis, a front axle coupled to the chassis, a rear axle coupled to the chassis, an electric motor supported by the chassis, and a trailer coupled to a rear end of the chassis and configured to be towed by the electrified vehicle. The electric motor is configured to drive at least one of the front axle, the rear axle, or a component of the electrified vehicle. The trailer includes a trailer frame, a trailer axle coupled to the trailer frame, and an energy storage device supported by the trailer frame. The energy storage device includes a plurality of batteries. The energy storage device configured to power the electric motor.

The invention discloses a battery prefabricated cabin intelligent refrigeration control method and system based on data driving, and relates to the technical field of battery energy storage system thermal management, and the method comprises the steps: collecting the operation multi-source data of a battery prefabricated cabin, and constructing the current thermal environment state expression; constructing a battery cabin short-term internal heat change trend prediction model based on the operation multi-source data of the battery prefabricated cabin; and according to the current thermal environment state expression and the thermal trend prediction result of the battery compartment short-term internal thermal change trend prediction model, an optimal control strategy for an air cooling, liquid cooling or composite refrigeration mode is formulated. According to the method, a closed-loop thermal management system driven by data is formed by constructing a thermal state expression module, a thermal trend prediction module and an intelligent refrigeration decision module, accurate recognition and active regulation and control of the thermal risk of the battery prefabrication cabin are achieved, the temperature control precision and the response efficiency are improved, energy consumption and over-control risks are effectively reduced, and the method is suitable for popularization and application. And the system has the advantages of remarkable engineering practical value and system intellectualization.

The invention discloses a multi-electrolytic cell collaborative optimization control method suitable for wide-range power fluctuation, and belongs to the technical field of new energy. According to the method, an optimization framework is adopted for collaborative optimization of hydrogen production power supply power, electrolytic cell power, electric energy storage power and electrolytic cell time sequence production state, a finite time domain rolling solving method is adopted in optimization, the rolling time domain length is hour level, the sampling time is minute level or hour level, an optimization objective function and a constraint model are established respectively, and the optimization objective function and the constraint model are optimized. And solving the optimization model by adopting a mathematical programming or heuristic algorithm to obtain the operation power of each device of the multi-electrolytic cell hydrogen production system and the electrolytic cell state. According to the method, under the wide-range power fluctuation working condition, battery energy storage weakening of frequent starting and stopping of the electrolytic cell is achieved, under the narrow-range power fluctuation working condition, battery energy storage is achieved, it is guaranteed that the electrolytic hydrogen production system operates at the optimal efficiency, and maximum energy utilization and efficient operation of the system are guaranteed; and a new method is provided for collaborative optimization control of the multi-electrolytic-bath hydrogen production system.

The invention relates to the technical field of battery energy storage, and discloses a battery energy storage health state assessment method and system, and the method comprises the steps: analyzing the constant-power charging and discharging process of a lithium battery, and obtaining health factors highly related to the battery energy storage health state through Pearson correlation analysis; constructing a bidirectional long short-term memory network model, taking the health factor highly related to the battery energy storage health state as model input, and outputting an energy storage battery health state estimation value; and optimizing the bidirectional long-short-term memory network model by improving a Kepler optimization algorithm and introducing a differential evolution strategy. According to the invention, the improved Kepler optimization algorithm is combined with the bidirectional long short-term memory network (BiLSTM) and the attention mechanism (Attention), so that the accurate estimation of the health state of the battery energy storage system is realized, the safety and reliability of the battery management system are improved, the high-precision evaluation of the health state of the lithium battery is realized, and the reliability of the battery management system is improved. And the robustness and generalization ability of the model under complex working conditions are improved.

The invention discloses a capacitive battery active equalization circuit and a method thereof. The capacitive battery active equalization circuit comprises a plurality of single batteries, a power MOS (Metal Oxide Semiconductor) tube circuit, an energy storage circuit and a control loop, the control loop comprises an ADC module, a PID module, a constant current controller and a power MOS tube driving module. The corresponding end of the ADC module is electrically connected with the corresponding ends of the plurality of single batteries and the PID module; the corresponding end of the PID module is connected with the corresponding end of the power MOS tube driving module through the constant current controller; and the corresponding end of the power MOS tube driving module is electrically connected with the corresponding end of the power MOS tube circuit. Compared with the scheme in the current market, the device has the advantages of low cost, small size, easiness in integration, simplicity in control, high safety and the like, and can store the energy of the battery with high voltage to the capacitor and carry the energy stored on the capacitor to the battery with low voltage, so that the aim of balancing the electric quantity of the battery is fulfilled.

In an approach to a state of health for grid applications, a system includes one or more battery energy storage systems having a plurality of batteries; energy sources; power distribution systems; and computing devices. The computing devices are configured to: for each of the battery energy storage systems: receive battery parameters for each of the batteries from the battery energy storage systems; determine a lithium plating state, a solid electrolyte interface (SEI) thickness, and a dendrite length for each of the plurality of batteries; determine a battery state of health (SOH) for each of the batteries based on at least one of the lithium plating state, the SEI thickness, and the dendrite length; determine a battery charge profile to mitigate aging for each of batteries based on the SOH; and send updated battery parameters and control thresholds for each of batteries to each of the battery energy storage systems.

The invention relates to the technical field of battery intelligent charging, in particular to an intelligent charging system for a solid-state lead battery energy storage power station. The method comprises the following steps: acquiring a real-time impedance characteristic parameter set and a real-time battery operation parameter set, and determining a multi-dimensional battery state data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set; obtaining a renewable energy power generation data set, and based on the renewable energy power generation data set and according to the multi-dimensional battery state data set, carrying out evolution analysis on solid electrolyte impedance fluctuation, and constructing a solid electrolyte impedance evolution model; determining an adaptive battery charging strategy according to the solid electrolyte impedance evolution model; and according to the self-adaptive battery charging strategy, power station charging parameters are adjusted, and a charging adjustment report is determined and output. The passivation speed of the solid electrolyte in the solid lead battery can be slowed down under the condition of intermittent fluctuation of renewable energy power generation, and the service life of the battery is prolonged.

The invention relates to a liquid cooling pipeline and phase change material compounded novel energy storage system and method, which are mainly applied to the field of battery energy storage in an extremely cold environment. The system comprises a liquid cooling unit, a liquid cooling pipeline system, a battery cluster, a phase change module, a multi-stage bypass system, a valve system and a circulating pump. Through cooperative work of the liquid cooling unit and the phase change module, the system can recover the heat of the cooling liquid after the battery is heated and store the heat in the phase change module, and the heat stored in the phase change module is preferentially used for heating when the temperature of the battery is relatively low, so that the energy consumption of the liquid cooling unit is reduced, and the energy efficiency of the system is 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.

Disclosed is a BMS-based battery energy storage safety management system. The system comprises a BMS module, a control center module, an evaluation analysis module, a coupling analysis module, a prediction module, and a data storage module. The BMS module collects monitoring data of a battery under different operation states and then, according to the monitoring data, performs initial monitoring of a real-time health state of the battery. The evaluation analysis module, according to a change in data indicators in the monitoring data, evaluates and analyzes all fault analysis models in the BMS module to obtain an evaluation analysis result. Then the coupling analysis module combines the evaluation analysis result with the data indicators of the monitoring data to retrieve multidimensional spatio-temporal data, and constructs a coupling field model to perform coupling analysis on battery operation state. The prediction module then constructs a fault prediction model by using a coupling data set as training data, accurately locates coupling faults of different coupling surfaces, and implements comprehensive analysis and location of coupling faults.

The invention discloses a Fourier transform and S transform combined battery impedance spectroscopy measurement method and device, and aims to improve the precision, speed and reliability of impedance measurement of a battery energy storage system. According to the method, a multi-frequency component excitation signal is generated through a controllable current source, and current and voltage response signals of a battery are synchronously acquired and are respectively input into a Fourier transform module and an S transform module for frequency domain analysis. The Fourier transform module extracts impedance spectrums of low and medium frequency bands, and the S transform module extracts impedance spectrums of medium and high frequency bands through Gaussian window time-frequency analysis. And a data fusion algorithm is adopted to perform composite processing on results of the two modules, including a direct selection method, an arithmetic average method, an amplitude and phase calibration method and a weighted average method, so that smooth transition and optimization between frequency bands are realized, and finally the electrochemical impedance spectrum is generated. Through collaborative analysis of Fourier transform and S transform, parameter optimization and a fusion algorithm, the efficiency and accuracy of impedance spectroscopy measurement are remarkably improved, and the method is suitable for rapid monitoring and diagnosis of the battery state.

The invention discloses a container type battery environment monitoring system based on edge calculation, which relates to the technical field of battery environment monitoring, and is characterized in that sensors and edge nodes are arranged in cabins of a battery energy storage device to acquire internal operation environment data of the container type battery energy storage device; a pneumatic environment data set and a water vapor critical data set are formed after data processing, cross-cabin air exchange efficiency Eex is constructed for air exchange qualification evaluation, a condensation nucleation potential index Cnp is constructed when air exchange is abnormal, and a comprehensive condensation risk index Rall is generated in combination with the cross-cabin air exchange efficiency Eex for condensation risk evaluation. Instructions such as fan power adjustment, dehumidification device start and stop and power limitation are issued according to the risk level, and information labels are added to the evaluation data set and uploaded to the cloud in real time. According to the invention, the real-time monitoring of the container type battery environment, the dynamic prediction of the condensation risk and the automatic control are realized, and the safety and reliability of the operation of the energy storage system are improved.

The invention relates to the technical field of new energy, in particular to an efficient solid-state battery energy storage heat management system applied to new energy. The battery management unit is used for monitoring the voltage, the current and the temperature of each battery unit and generating a charge state and a health state; the power conversion unit is used for executing a charging and discharging instruction and a charging and discharging rate instruction transmitted by the battery management unit; the heat management unit is used for dynamically adjusting cooling and heating measures; and the safety protection unit is used for taking corresponding alarm and protection measures according to data monitored by the battery management unit. The system not only enhances the physical authenticity of the model, but also improves the capability of adapting to complex working conditions, and can more efficiently transfer redundant energy to a battery unit in a low charge state, thereby reducing energy loss and heat loss, and improving the stability and economic benefit of the system.

The invention relates to the technical field of power systems, and particularly discloses a multi-energy coupling system optimal scheduling method considering grid-connected safety, and the method comprises the steps: constructing a multi-energy coupling system architecture model; the multi-energy coupling system architecture model comprises a renewable energy power generation unit, a traditional energy power generation unit, an energy storage unit, an energy conversion unit and a combined cooling heating and power generation unit; constructing a distributed energy storage optimization operation model based on the multi-energy coupling system architecture model; the constraint conditions comprise charging and discharging constraint, charge state constraint and peak clipping constraint of battery energy storage; carrying out output constraint on the energy conversion equipment; electric vehicle charging station operation constraint; new energy power generation constraint; power balance constraint; public network line transmission power constraint; constraining transmission power of an internal distribution network line; and solving the distributed energy storage optimization operation model by adopting an optimization algorithm to obtain an optimization scheduling scheme. The method has the advantage that scientific and reliable technical support is provided for efficient operation of the multi-energy coupling system.

The present invention provides a battery energy storage parallel equalization converter topology and a control method therefor. The battery energy storage parallel equalization converter topology comprises a series-connected battery energy storage parallel equalization converter, a main converter, and a plurality of groups of filter inductors; in the main converter, the input side of a DC / DC or DC / AC converter is connected in parallel to a parallel voltage-stabilizing capacitor, and the output side of the DC / AC or DC / AC converter is correspondingly connected to an AC or DC source load; the series-connected battery energy storage parallel equalization converter comprises a plurality of groups of small-capacity DC / DC converters and a common DC bus capacitor; the output sides of all the small-capacity DC / DC converters are connected in parallel by means of the common DC bus capacitor to form a common DC bus; the input side of one group of small-capacity DC / DC converters is connected in series to the input side of the main converter, and the input sides of other groups of small-capacity DC / DC converters are connected in series to a battery pack by means of the filter inductors, respectively. According to the present invention, in a battery charging and discharging process, only a small amount of power flows through the converter, reducing the capacity requirements of the converter, and reducing the transmission efficiency.

The invention discloses a network construction type battery energy storage system and a control method thereof, and relates to the technical field of battery energy storage. Electrical parameters and operation state parameters are preprocessed, and feature vectors are generated; analyzing offset features based on the feature vectors, and performing feature drift marking; analyzing the spacing characteristics of each battery energy storage device, and performing clustering analysis according to the spacing characteristics; analyzing the equivalent capacity of each cluster in the clustering result; obtaining a historical regulation and control record set, and analyzing the association degree between any two moments; the composite energy fluctuation value at the current moment is analyzed, and a dynamic regulation and control threshold value is analyzed in combination with the correlation degree of the real-time moment and the historical moment; matching degree indexes of all clusters are analyzed in combination with the equivalent capacity and the total regulation and control instruction, an ordered cluster index sequence is generated after sorting, and hierarchical regulation and control instruction analysis is conducted on the ordered cluster index sequence; the allocation proportion is dynamically adjusted, resource allocation is optimized, and the regulation and control risk is reduced.

The invention belongs to the technical field of battery energy storage systems. The invention provides a power distribution method and system for a battery energy storage system, a medium and computer equipment, when the target power of the energy storage system is not zero and the target power is greater than or equal to the sum of the maximum charging and discharging power of all energy storage units, all the energy storage units operate at the maximum charging and discharging power; when the target power of the energy storage system is not zero and the target power is smaller than the sum of the maximum charging and discharging power of all the energy storage units, determining the number of required energy storage units according to the target power and the maximum charging and discharging power of each energy storage unit, and selecting the energy storage units to be used according to an energy storage unit priority rule; performing global optimization on the target power distribution to obtain an optimal or approximately optimal power distribution value of each energy storage unit to be used; according to the invention, the SOC balance degree of the battery energy storage system is improved, and the risk of over-charging and over-discharging is reduced.

The invention discloses an intelligent control method and system for a solar street lamp, and particularly relates to the technical field of intelligent control. The method comprises the following steps: collecting photovoltaic power generation data and battery charging and discharging data of a solar street lamp node, analyzing difference characteristics of photovoltaic power generation capacity of the solar street lamp node in space and fluctuation characteristics of a battery energy storage state in time, and outputting space difference characteristic data and time fluctuation characteristic data; power generation space imbalance evaluation data and energy storage time abrupt change evaluation data are output by evaluating power generation imbalance distribution characteristics and energy storage state abrupt change characteristics caused by dynamic shielding; a local energy link imbalance prediction model is constructed, energy link imbalance prediction data of solar street lamp nodes are output, power distribution and brightness control strategies of solar street lamps are dynamically adjusted, and intelligent sensing and regulation of energy link instability under local shielding are achieved. And the operation stability and the energy-saving performance of the whole illumination system of the solar street lamp group are ensured.

The invention discloses a fire prevention and control method and system for battery energy storage equipment. The method comprises the following steps: collecting real-time fire characteristic parameters of the battery energy storage equipment; based on the real-time fire characteristic parameters and a preset fire control strategy, making a fire control decision and generating a fire control instruction; obtaining effect feedback after the fire-fighting instruction is executed; based on the real-time fire characteristic parameters and effect feedback, optimizing and updating a fire-fighting strategy; and applying the optimized and updated fire-fighting strategy to a subsequent fire-fighting decision of the battery energy storage equipment. According to the method, through real-time monitoring, digital twinborn simulation and a closed-loop optimization mechanism, the accuracy and adaptive capacity of fire prevention and control of the battery energy storage equipment are remarkably improved, dynamic adjustment and low-risk verification of a fire-fighting strategy are achieved, and safety and reliability are effectively enhanced.