350 results about "Thermal model" patented technology

The invention relates to the technical field of reaction kettle temperature control, and discloses a fine chemical engineering reaction kettle temperature intelligent control method based on self-adaptive search, which comprises the following steps: S1, data acquisition: acquiring operation data of a reaction kettle, including temperature in the kettle, jacket inlet and outlet temperature, fluid flow and valve opening signals; s2, heat balance calculation: establishing a heat balance equation based on the collected data, and calculating reaction heat release and an equivalent heat transfer coefficient; s3, updating the model; s4, predictive control solution is carried out; s5, performing adaptive search; s6, carrying out security constraint; s7, stage identification; and S8, performing control execution. The reaction heat release amount and the equivalent heat transfer coefficient are calculated in real time through linkage of a heat balance calculation module and a model updating module, online parameter identification is achieved through a recursive least square algorithm, a heat model can be dynamically corrected along with working condition changes, it is ensured that a prediction model is consistent with the actual reaction process, and the prediction efficiency is improved. And the temperature prediction precision and the reliability of control response are obviously improved.

The invention discloses a new energy commercial vehicle fast charging working condition thermal management control method and system, and relates to the technical field of new energy vehicle thermal management and fast charging control, and the method comprises the following steps: collecting the temperature, voltage and current data of each subarea of a battery in a fast charging process, forming a time synchronization sequence, and based on the sequence, obtaining a new energy commercial vehicle fast charging condition; calculating internal thermal resistance and thermal capacity parameters of the battery in real time by using an online identification algorithm, updating a basic thermal model to obtain a corrected thermal dynamic model reflecting actual thermal characteristics of the current battery, importing the corrected model into a model prediction controller, constructing a multi-objective optimization function, and performing rolling solution in a prediction time domain to obtain a prediction model; and outputting an optimal target charging current instruction and a cooling total demand, adjusting the charging power according to the target current, and combining the temperature difference distribution of each partition. Accurate prediction and partition cooperative control of the thermal state of the battery in the fast charging process are achieved, the temperature rise and the temperature difference are effectively restrained while the charging efficiency is guaranteed, the service life of the battery is prolonged, and the system safety is improved.

The invention discloses a dynamic load adaptive charging pile liquid cooling control method, relates to the technical field of thermal management, and solves the problems of temperature prediction deviation, local hot spot leak detection and protective supercooling caused by a simplified thermal model based on fixed parameters and key temperature points under rapid load fluctuation. Parameterization time-varying heat supply network parallel multi-rate observation is constructed, a heat exchange coefficient and node heat capacity are identified and calibrated through recursion with physical constraints, transmission time delay is merged into an extended state in an explicit mode, short-window high-frequency sampling and local temperature field reconstruction are triggered based on residual errors, and the time-varying heat supply network parallel multi-rate observation is achieved. A control sequence is issued in parallel by adopting hierarchical rolling type control with a second-order moment uncertainty constraint; based on the scheme, the short-term temperature field prediction precision and the local hot spot identification capability are improved, and the cooling resource directional utilization efficiency and the charging system operation continuity are remarkably improved.

The invention discloses an energy storage battery thermal runaway damage diffusion early warning method based on a dynamic thermal model, and relates to the technical field of battery safety monitoring, and the early warning method comprises the specific steps: multi-source parameter collection: arranging a plurality of sensors to collect data, and preprocessing and storing the data; thermal stress modeling is conducted, units are divided through a finite element method to calculate thermal stress, and structural damage is judged in combination with a damage threshold value; multi-physics field cooperative calibration is carried out, and characteristic quantity is acquired and substituted into a formula to correct thermal stress; dynamic thermal model construction: introducing thermal stress influence to construct a model containing a damage diffusion term; thermal runaway early warning judgment is carried out, a multi-dimensional index system is constructed, a risk value is calculated, and early warning is triggered; through a multi-source parameter acquisition technology, in combination with temperature, current, voltage, ultrasonic wave and broadband impedance multi-dimensional sensor data, and assisted by external condition monitoring of environment temperature and humidity, heat dissipation system flow and rotation speed, comprehensive perception of the working state of the battery is realized, and richness and accuracy of data acquisition are improved.

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

The invention discloses a PINN-based transformer convective heat transfer coefficient acquisition method, system and device and a medium, and relates to the technical field of transformers, and the method comprises the steps: building a three-dimensional thermal field simulation model based on transformer structure parameters, obtaining temperature field data through multi-physics field coupling simulation, building a sample set, training a physical information neural network model, and obtaining the convective heat transfer coefficient of a transformer. A loss function is defined, through weight adjustment and optimization algorithm training, loss change is dynamically monitored, convective heat transfer coefficient distribution is inversed, an inversion coefficient is substituted into a thermal model for forward calculation, iterative optimization is performed through comparison with independent measurement data, and accuracy and applicability are verified through actually measured temperature data. According to the method, the dependence on experimental data is reduced, the high-precision and high-efficiency acquisition of the convective heat transfer coefficient of the spatial change of the transformer is realized, and a reliable technical means is provided for thermal design and state evaluation of the transformer.

The invention relates to the technical field of electric vehicle thermal management, discloses an electric vehicle motor and driver integrated intelligent cooling system and a control method, and aims at solving the problems that an existing motor driver thermal management system is low in cooling efficiency, high in energy consumption, complex and insufficient in space utilization rate. The method comprises the following steps: acquiring operation, thermal state and environment data; constructing a collaborative thermal model and predicting a thermal load; generating an optimal cooling strategy based on the predicted value and the target; the cooling liquid pump, the three-way valve and the cooling fan are adjusted, and cooling resources are dynamically distributed to achieve collaborative cooling. The system comprises a data acquisition module, a collaborative thermal model construction prediction module, an intelligent decision control module and an integrated cooling circulation execution module. By the adoption of the technical scheme, the cooling performance can be effectively improved, system energy consumption and complexity are reduced, space layout is optimized, the service life of parts is prolonged, and the reliability, the power performance and the endurance of the whole vehicle are improved.

The invention discloses a rapid thermal simulation method and system for a semiconductor power assembly, and relates to the technical field of semiconductors, and the method comprises the steps: collecting a voltage V (t) signal and a current I (t) signal of the semiconductor power assembly in real time through a voltage and current sampling circuit, constructing a power pulse input sequence A, and carrying out the hot carrier accumulation analysis according to the power fluctuation change amplitude; introducing a transient thermal resistance function Zth (t), fitting the transient thermal resistance function Zth (t) with a hot carrier pulse response index Mcx after numerical integration to analyze junction temperature change caused by power pulse, and evaluating an equivalent junction temperature dynamic change rate; when the analysis result is that the thermal drift out-of-control trend exists, thermal risk analysis is carried out, and thermal risk state assessment is generated; the simulation model optimizes thermal model parameters and outputs thermal control suggestions through a rollback correction path, and the working state of the semiconductor power assembly is automatically adjusted. According to the method, real-time monitoring and effective prevention and control of thermal runaway can be realized, and the reliability and safety of the semiconductor power assembly are improved.

The invention relates to the technical field of heat supply control, in particular to a heat supply secondary network branch line intelligent hydraulic equilibrium method and device, a medium and a product. The method comprises the following steps: constructing a branch topological graph of a heat supply secondary network in a target heat supply area; the environment temperature is obtained in real time, the heat supply station is controlled to supply heat to each heating single body in the target heat supply area based on the dynamic flow control strategy, and the node flow and the node temperature of each node in the branch topological graph are collected; constructing a branch heat supply loss model of the branch topological graph based on the environment temperature, the node flow and the node temperature; the monomer temperature and heat supply backflow data of each heating monomer are obtained, and a monomer heating model is constructed for each heating monomer based on the environment temperature, the monomer temperature and the heat supply backflow data; and determining a flow regulation and control strategy based on the monomer heating model and the branch line heat supply loss model, and adjusting the monomer heat supply flow of the heat supply secondary network based on the flow regulation and control strategy. According to the method, heat supply hydraulic balance can be achieved through precise flow regulation and control precision.

The invention relates to a power battery performance degradation prediction method and system based on an electrochemical-thermal-mechanical-neural network model, and belongs to the technical field of battery management. According to the method, an enhanced single particle model considering liquid phase diffusion, an integrated thermal model, a particle mechanical stress model caused by concentration gradient and an aging mechanism model considering SEI growth, lithium precipitation and active material loss are coupled to establish ETMD, and based on the ETMD, the identification efficiency of aging model parameters is low aiming at the problem that a PSO algorithm is low. Under a circulating aging experiment of a limited path, the characteristic parameters of the aging model are obtained by using the feedforward neural network, and the parameters are substituted into the ETMD model again to predict the capacity, the power attenuation curve and the corresponding main attenuation mechanism under the current path, so that the prediction precision and the interpretability of the performance attenuation of the battery under the full path are improved. According to the method, the modeling capability of battery performance degradation under different working conditions can be effectively improved, and a theoretical basis and technical support are provided for residual life prediction and safety management of the power battery.

The invention discloses a power consumption prediction and optimization method in chip design, and relates to the technical field of chip design and energy efficiency optimizing.The method comprises the steps that a circuit netlist is analyzed, functional blocks are divided, and an association diagram is constructed; generating a typical working load containing a transient peak value and a steady-state interval based on actual application; calculating dynamic and static power consumption, and considering temperature sensitive leakage current; constructing a three-dimensional thermal model, gridding a chip, solving a heat conduction equation, and outputting temperature field distribution; three-level optimization of clock gating, DVFS and data flow reconstruction is carried out on the overtemperature area; the optimized parameters are imported into a simulation tool to verify the effect, and robustness is confirmed through Monte Carlo simulation. According to the method, the power consumption prediction precision and optimization pertinence are improved, the temperature distribution uniformity is improved, the power consumption and performance are balanced, the number of design iterations is reduced, and the chip reliability and energy efficiency ratio are enhanced.

The invention relates to the technical field of battery temperature control, in particular to a temperature control method and device for a capacity grading system, a medium and equipment, and the method comprises the steps: S1, obtaining real-time data in the charging and discharging process of a battery module; s2, constructing a dynamic thermal model based on a neural network, preprocessing the real-time data, taking the preprocessed real-time data as input, and predicting a temperature prediction value after the next three seconds through the dynamic thermal model; s3, the current working mode of the formation and capacity grading system is judged based on the temperature predicted value, a PID control signal is generated, and meanwhile an optimal air volume scheme is calculated through an alternating direction multiplier method; and S4, the PID control signal is output to a thermoelectric refrigerating unit for segmented adjustment of the refrigerating power, meanwhile, the rotating speed of a plurality of independent fans is adjusted according to the optimal air volume scheme, and the temperature fluctuation range is reduced while the dynamic response speed and the energy efficiency ratio are increased.

The invention discloses an energy storage battery thermal diffusion modeling method based on P2D-solid heat transfer coupling, and relates to the technical field of energy storage battery thermal management. The method comprises the following steps: constructing a pseudo two-dimensional (P2D) electrochemical model and a three-dimensional heat conduction model of a single battery, and calculating lithium ion concentration, potential and corresponding heat source distribution; mapping the obtained heat source as a volume heat source to a thermal model and realizing bidirectional feedback; setting a thermal runaway triggering condition to simulate temperature rise abrupt change; battery SOH state modeling is introduced, and the aging process is dynamically corrected and reflected through material parameters; finally, time domain simulation is carried out, and temperature field evolution and safety behavior prediction in the whole life cycle are obtained. According to the method, the coupling process of the microelectrochemical reaction and the macroscopic temperature change of the battery can be captured at the same time, the thermal diffusion characteristics of different decline stages are accurately described, and visual modeling of thermal runaway behaviors is supported. The method has the remarkable advantages of high precision, high applicability, perfect safety assessment capability and the like.

The invention discloses a power management system in an embedded network terminal and a control method thereof, and relates to the technical field of micro-grids. The system is deployed in a building micro-grid and can communicate with a micro-grid central control unit, an adjacent terminal or an edge server. The processing unit receives the day-ahead moving trajectory as a rolling optimization initial solution, identifies building thermal model parameters based on local observation data and carries out linearization to obtain a local optimization model; local short-term prediction and edge server correction information are fused, an optimization sub-problem is constructed, distributed solution is carried out through an alternating direction multiplier method, and continuous variable candidate solutions are obtained in a calculation quota; aiming at a binary switch decision query strategy mapping table, when solving is not converged, adopting the table to output or finite depth search results; and when the communication is interrupted or the solving is failed, executing the rollback control to guarantee the indoor comfort and the energy storage safety. The method is used for improving scheduling real-time performance and communication efficiency under the condition of limited calculation and bandwidth, so that indoor comfort and energy storage safety are guaranteed.

The invention discloses an automatic shell-making wire environment simulation system. The system comprises an environmental parameter testing unit, a heat and mass transfer model establishing unit and an optimization processing unit. The environmental parameter testing unit is used for testing environmental parameters influencing the drying process of the shell-making drying cabin through testing equipment, evaluating the uniformity of airflow in the drying cabin and the change condition of the environmental parameters, and sending testing and evaluating results to the heat and mass transfer model establishing unit; the environmental parameters at least comprise wind speed. And the heat and mass transfer model building unit is used for building a shell drying heat transfer model and a shell drying mass transfer model, evaluating the shell drying heat transfer model and the shell drying mass transfer model based on the model expression indexes after building is completed, and analyzing the influence of the environmental parameters on shell heat transfer and mass transfer based on the information transmitted by the environmental parameter testing unit. And the optimization processing unit is used for introducing the influence of load, airflow in the drying cabin and air into the established shell drying heat transfer model and shell drying mass transfer model for simulation and optimization.

The invention discloses a power battery liquid cooling system flow adjusting method based on working condition prediction, and relates to the technical field of battery cooling control, and the method comprises the steps that a controller collects the module temperature, the cooling liquid inlet and outlet temperature, the current charge state and the vehicle working condition, and predicts the temperature and temperature rise trend based on a lightweight thermal model; and calculating a thermal margin, dividing an urgent area and a non-urgent area, and outputting a temperature upper limit constraint, a temperature difference constraint, a pump speed boundary and a hot spot risk weight. Entering a calibration window when the thermal margin is sufficient and the temperature rise trend is smooth, applying limited disturbance to the rotating speed of the water pump and the opening degree of the branch valve, updating the equivalent resistance parameter and the heat exchange marginal slope according to disturbance-response, and giving credibility and an abnormal mark. And then valve position distribution is carried out according to hot spot temperature changes corresponding to water pump work, the pump speed is determined in a water pump efficient working area, and conservative control is returned if abnormity occurs, so that pump energy consumption is reduced, and adaptability to assembly difference and aging drift is improved.

The invention provides a method and device for increasing the rated current breaking frequency of a direct-current solid-state circuit breaker and application of the method and device, and aims to solve the problem that the breaking frequency of an existing direct-current solid-state circuit breaker is limited due to over-temperature damage caused by performance degradation after multiple current impacts of a piezoresistor and difficult measurement of the junction temperature of a power device. The system comprises a junction temperature prediction module based on a physical information neural network, an auxiliary energy absorption module and a power device voltage clamping module. The junction temperature prediction module is used for accurately predicting the junction temperature of the power device in real time by fusing a physical thermal model and a neural network; the voltage clamping module suppresses turn-off overvoltage; and the auxiliary energy absorption module is started when the junction temperature is close to the limit to absorb residual energy. According to the design, turn-off energy is reasonably distributed, the impact frequency and strength of the piezoresistor are reduced, the service life of the piezoresistor is remarkably prolonged (for example, the service life is prolonged by about 10 times under the impact of 200A), then the rated breaking frequency of the solid-state circuit breaker is greatly increased, and the solid-state circuit breaker is low in cost, high in reliability and suitable for scenes such as a direct-current power grid.

The invention discloses a lithium battery internal temperature estimation method based on thermal parameter identification of AWPSO and IKF, and aims to solve the problems that thermal parameter identification is susceptible to noise interference and the temperature estimation precision is insufficient in the prior art, the method comprises the following steps: firstly, collecting battery voltage, current and surface temperature data, and constructing a lumped parameter thermal model; an adaptive weighted particle swarm optimization (AWPSO) algorithm is adopted to dynamically adjust the inertia weight, and thermal resistance and thermal capacity parameters are identified by taking the error minimization of the model calculation temperature and the actually measured temperature as a target; secondly, based on a thermal model of identification parameters, the internal temperature is estimated in real time through an incremental Kalman filter (IKF) algorithm, a residual feedback mechanism is introduced into the algorithm to dynamically update noise covariance, and the state prediction and measurement updating process is optimized. According to the method, the robustness of thermal parameter identification and the accuracy of internal temperature estimation are remarkably improved, and an effective technical support is provided for safety monitoring and service life management of the lithium battery.

The invention relates to a multi-split air conditioner regulation and control method, device and equipment, a storage medium and a product. The method comprises the steps that reference temperature data of a target building where the multi-split air conditioner is located and reference power data of the multi-split air conditioner within a preset reference time period are obtained, and a target thermal parameter value of the target building is obtained through a preset building thermal model, the reference temperature data and the reference power data, the building thermal model is used for calculating heat change data and heat exchange data of the target building, and the target thermal parameter value is a value of a thermodynamic parameter of the target building under the condition that the heat change data and the heat exchange data meet a preset heat balance relationship; and the actual load-shedding power amount of the multi-split air conditioner in the current regulation and control time period is determined, so that the multi-split air conditioner is regulated and controlled according to the load-shedding power amount. By adopting the method, the dynamic thermodynamic characteristics of the building under the actual operation working condition can be determined, the adjustable potential of the air conditioning system can be accurately quantified, and the regulation and control accuracy can be improved.

The invention is suitable for the technical field of power electronics, and provides an intelligent management method and system for a power amplifier, and the method comprises the steps: obtaining the output voltage and current of the power amplifier, and determining the real-time load impedance; predicting the junction temperature of the power amplifier according to a coupling thermal model associating the effective thermal resistance with the load mismatch degree; based on the predicted junction temperature and real-time load impedance, stability compensation parameters for maintaining operational stability and pre-distortion parameters for compensating for non-linear distortion are cooperatively determined and adjusted. Through cooperative intelligent management, safety and reliability can be improved, performance maximization is realized, and high fidelity of all working conditions is ensured.

The invention discloses a control method, and particularly relates to an energy storage power converter self-adaptive control method based on digital twinning, which comprises the following steps: step 1, digital twinning model synchronization and state sensing; step 2, multi-target collaborative optimization based on prediction; and step 3, optimizing instruction issuing and physical system execution. Through real-time prediction and optimization of the digital twinborn model, it is ensured that the power switch tube always works in a zero-voltage switching state, switching loss is effectively reduced, and overall efficiency is improved. Dynamic thermal balance management is realized by combining thermal model prediction, local overheating is avoided, the service life of devices is prolonged, and the reliability of long-term operation of the system is enhanced.

The invention provides a magnetic suspension turbo molecular pump rotor temperature estimation method, and belongs to the technical field of semiconductor molecular pump temperature control. According to the method, on the basis of construction of the permanent magnet thermal model, key variables influencing the rotor temperature are obtained, determination of input variables is achieved, and the method plays an important role in accuracy and high efficiency of model training and application. Meanwhile, a deep learning technology is introduced, a model structure Bi-LSTM-CNN suitable for magnetic suspension turbo molecular pump rotor temperature estimation is designed, and the model structure comprises a Bi-LSTM module, a multi-head attention mechanism module, a CNN module and a full connection layer. The temperature of the rotor can be accurately estimated according to the real-time change of the working state of the rotor and the environment condition, so that intelligent management and maintenance of the pump system are realized. Meanwhile, the initial weight of the convolutional neural network model is trained by using the genetic algorithm, so that the probability of obtaining the global optimal value is relatively high, the model is effectively trained, and the generalization performance is improved.

The invention discloses a centralized air conditioner room control system based on reinforcement learning, which belongs to the technical field of reinforcement learning and comprises a data acquisition preprocessing module, a state feature extraction modeling module, a reinforcement learning strategy generation module, a multi-stage execution cooperative control module, a dynamic effect evaluation feedback module and a monitoring fault early warning module. A spatial topology model is constructed through a graph neural network, a machine room is divided into nodes, a heat transfer path is simulated through a heat conduction weight, the problem of local overheating of a high-density equipment area is solved through introduction of a heat source intensity normalization coefficient, and triple effects of heat generation, transfer and loss are quantified through a dynamic heat model, so that the local overheating problem of the high-density equipment area is solved. Influences of adjacent node heat conduction, local heat source intensity and environment heat dissipation are comprehensively considered, the evolution process of temperature along with time is described through a differential equation instead of static steady-state analysis, and the strategy generation module adjusts air conditioner parameters in advance according to a prediction result.

The invention discloses a distribution network overhead line fault self-healing method, system and device and a medium, and belongs to the technical field of power system fault self-healing, and the method comprises the steps: laying a synchronous phasor measurement unit, and generating a unified phasor reference through cloud edge cooperative calculation; the edge coordination engine calculates the load flow change after loop closing by means of a unified phasor reference and an instantaneous topological state to form an action sequence; the local controller obtains a coordination coefficient and controls the pre-compensation of the flexible interconnection device and the closing time of the section switch; pushing the temperature rise prediction value to a wire thermal model; and the edge controller circularly and adaptively corrects the compensation parameter of the flexible interconnection device, and writes the event into the self-healing knowledge base. According to the invention, automation and intelligentization of fault self-healing are realized, the efficiency of fault self-healing is significantly improved, and long-term stable operation of the power distribution network after fault recovery is ensured; and through intelligent and adaptive design, a brand new technical path is provided for long-term reliable operation of the distribution network.

The invention discloses a lithium battery electrothermal coupling SOC estimation method based on capacity dynamic correction. The method comprises the following steps: firstly, collecting current, voltage, surface temperature and environment temperature data under wide-temperature and multi-rate working conditions; then, an electrothermal coupling frame composed of a second-order RC electrical model and a lumped parameter two-state thermal model is constructed, and bidirectional transmission of electrical-thermal parameters is achieved; coupling parameters are identified on line by using a variable forgetting factor recursive least square method, temperature-multiplying power-capacity binary high-order polynomial dynamic mapping is constructed, and available capacity is corrected in real time; and finally, the correction capacity is taken as a reference, SOC is estimated through H infinity-AUKF joint, and non-Gaussian noise is suppressed. The corresponding system comprises a data acquisition module, a model construction module and the like, can be integrated in a BMS (Battery Management System), realizes SOC high-precision estimation under wide temperature and multiple rates, is suitable for an electric vehicle and an energy storage system, and has the advantages of capacity dynamic correction, electric heating bidirectional coupling, wireless parameter identification and high robustness of non-Gaussian noise.

The invention is suitable for the technical field of sodium ion batteries, and provides a digital twinning-based intelligent operation and maintenance method for a sodium ion battery energy management system, and the method comprises the steps: constructing and operating a virtual model corresponding to a sodium ion battery system in a cloud digital twinning platform based on collected multi-source heterogeneous operation data; the virtual model simulates the internal state and evolution trend of the sodium ion battery system by coupling the electrochemical model, the thermal model and the life model; tracking and predicting the state of charge and the state of health of the sodium ion battery system, generating a charging and discharging power instruction and a thermal management strategy through a dynamic optimization control algorithm, and issuing and executing the instruction; and performing anomaly detection by applying an unsupervised learning algorithm, and generating predictive maintenance suggestions in combination with the prediction result of the health state. By constructing digital twinborn body and multi-model coupling, multi-dimensional accurate perception and multi-target dynamic optimization of the sodium ion battery system are realized, and the full life cycle value and safety level of the energy storage system are improved.

The invention discloses a flow battery external characteristic prediction method and equipment. The method comprises the following steps: constructing an equivalent circuit model and a thermal model of an all-vanadium flow battery; calculating equivalent circuit parameters of the equivalent circuit model according to the initial electrolyte temperature and the initial vanadium ion concentration of each valence state; calculating battery output voltage and electric power at the current moment according to the equivalent circuit parameters and the charging and discharging current; inputting the environment temperature and the electric power at the current moment and the pump power of the circulating pump into a thermal model, and solving the temperature of the galvanic pile electrolyte at the next moment; inputting the stack electrolyte temperature at the next moment into the dynamic differential equation of the vanadium ion concentration to obtain the vanadium ion concentration at the next moment; the electrolyte temperature and the vanadium ion concentration at the next moment are fed back to the equivalent circuit model, loop iteration is conducted till the battery charging and discharging process reaches a steady state, and the output voltage and the stack electrolyte temperature of the flow battery at each moment in the charging and discharging process are obtained. The computing resources are reduced, and the prediction accuracy is improved.

Surface temperatures for battery cells in a battery pack can be inferred or estimated using internal or core temperatures of the battery cell and a thermal model. The internal temperature may be generated using electrochemical impedance spectrometry (EIS). Delta values from the EIS estimations to the cell surface temperature may be generated based on a thermal model of the battery. The thermal model of the battery may be created using probe points on the surface of a test battery, such as thermocouples.

The invention relates to the technical field of comprehensive energy transformation, in particular to a digital twin-body-based comprehensive energy system transformation optimization method, which comprises the following steps of: constructing a digital twin body comprising a building thermal model and an energy equipment system model on the basis of static data, dynamic operation data and external environment data of a target area; global optimization with a preset objective function as a guide is carried out, an optimal system capacity configuration and architecture scheme is determined, and then local equipment type selection and control strategy optimization are carried out; issuing the optimized control strategy to a physical energy system for execution, and continuously collecting feedback data to calibrate the digital twin to form closed-loop optimization; a sensing-analysis-decision-execution-learning closed-loop system is constructed, an energy system of a physical world is deeply coupled with a digital model of a virtual world, global and local layered collaborative optimization is realized through a digital twin, and dynamic adaptability is improved through closed-loop operation.

The invention provides a simulation method and system for a solar ground source heat pump system, and relates to the technical field of solar ground source heat pump system simulation, and the method comprises the steps: firstly obtaining a soil initial temperature field, and calculating the soil temperature cumulative variation in a future time period through a one-dimensional radial simplified heat transfer model in combination with a predicted load; defining as a soil thermal imbalance risk index; according to comparison between the index and a preset threshold value, selecting an efficiency priority strategy with solar energy immediate utilization as priority or a heat balance priority strategy with soil temperature field adjustment as priority; linkage rules of all subsystems are determined according to a selected strategy, the net heat exchange amount of a geographical pipe heat exchanger and the net heat storage / release amount of a heat storage device are calculated by jointly solving a dynamic model, and then the soil temperature field and the state of the heat storage device are updated; and the simulation step length is iteratively propelled until the period is ended, and finally, the system energy efficiency coefficient, the soil heat balance degree and the time-varying curve of the key parameters are output.