849 results about "Equivalent circuit" patented technology

The present disclosure provides a fault measurement impedance calculation method and device for a hybrid grid-following / grid-forming wind power system. The method comprises: acquiring characteristics of a grid-following converter and a grid-forming converter, and corresponding low-voltage ride-through control strategies; establishing an equivalent circuit corresponding to a fault state; performing simplified analysis on a hybrid wind power system to obtain an equivalent network diagram; obtaining a branch circuit diagram under individual action of each power excitation; obtaining short-circuit currents and grid-connected point voltages on two sides of the system corresponding to the individual action of each power excitation; performing superimposition to obtain a total short-circuit current and a total grid-connected point voltage at the two sides of the hybrid wind power system; and obtaining a measurement impedance at a protection installation point on a new energy side. In the method, a measurement impedance value at a protection installation point of a hybrid wind power system after a fault occurs can be accurately calculated, so that the setting calculation of distance protection is ensured, so as to accurately reflect fault characteristic information of a hybrid AC system, thereby providing a basis for the distance protection applicability research of the hybrid wind power system, and thus ensuring the safe and stable operation of new energy systems.

The invention discloses a broadband wave-absorbing material based on an MAX-phase metasurface structure and an impedance matching optimization method of the broadband wave-absorbing material. The wave-absorbing material comprises an MAX-phase ceramic substrate and periodic metasurface structures which are arranged on the surface of the MAX-phase ceramic substrate in an array mode and composed of conductive resonance units. The resonance units are isolated through gaps, an equivalent R1L1C1 series resonance circuit is formed in an electromagnetic field, and a base body is equivalent to a parallel resistor R2. By establishing an equivalent circuit model and optimizing parameters R1, L1, C1 and R2, impedance matching of the wave-absorbing material in a target frequency band can be regulated and controlled, so that the wave-absorbing material at least has an absorption peak of which the reflection loss is less than-10 dB. According to the optimization method, electromagnetic simulation and circuit simulation are combined, optimized circuit parameters are reversely mapped into structure geometric parameters, material preparation is guided, and accurate design and broadband absorption of the wave absorbing performance are achieved.

The invention provides a general electromechanical transient modeling method and system for a network-building converter, and relates to the technical field of electromechanical transient modeling, and the method comprises the steps: simplifying the electromagnetic transient models of network-building converters of different topologies into a grid-connected equivalent circuit of an electromechanical transient time scale; uniformly representing by using an internal potential series reactance model controlled by a power outer ring so as to establish an electromechanical transient general grid-connected interface model of the grid-forming converter; combining mathematical transformation of active power control with a reactive power control mode to form a general power outer loop control model of the network construction type converter; through a method based on internal potential reconstruction, reconstructing a new internal potential satisfying a current limiting constraint, and establishing a general current limiting model suitable for a network construction type electromechanical transient model; according to the method, various topological structures, various power outer loop control and various current limiting strategies can be simulated, flexible use of models in large-scale power system simulation is facilitated, and the complexity of model development and maintenance is reduced.

The invention relates to the technical field of aircraft part processing, testing or inspection and the like, and provides an aircraft aluminum alloy plate aging evaluation method based on an equivalent circuit model, and the method comprises the following steps: collecting electrochemical impedance spectrums of samples with different exposure age limits, and extracting electrochemical impedance spectrum characteristics of the samples; establishing an equivalent circuit model comprising solution resistance, coating resistance, coating capacitance, Warburg impedance, anodic oxide film charge transfer resistance, interface capacitance, aluminum alloy matrix charge transfer resistance, interface electric double-layer capacitance, inductance and corresponding resistance of the inductance based on the characteristics, wherein the equivalent circuit model comprises the solution resistance, the coating resistance, the coating capacitance, the Warburg impedance, the anodic oxide film charge transfer resistance, the interface capacitance, the aluminum alloy matrix charge transfer resistance and the interface electric double-layer capacitance; and analyzing a resistance curve and a capacitance curve obtained by fitting the model to complete the evaluation of the corrosion and aging degree. According to the method, systematicness and accuracy of aging evaluation are improved, an electrochemical mechanism in the corrosion process can be disclosed, time correlation modeling of the aging process can be achieved, and the method is suitable for long-term service performance monitoring and service life prediction of the aviation aluminum alloy structure.

The invention belongs to the field of electrical variable measurement, and particularly relates to a single battery equalization management method for a lithium battery BMS (Battery Management System) protection board, which comprises the following steps of: acquiring voltage, temperature, total current and cycle index of each single battery in real time; based on a second-order equivalent circuit model and an extended Kalman filtering algorithm, multi-source information is fused, and the health state and the charge state are estimated with high precision; constructing a dynamic equilibrium trigger threshold fusing the charge state standard deviation, the range and the health state attenuation factor; a bidirectional flyback or multi-winding transformer active equalization strategy is selected according to the inconsistent distribution characteristics and the temperature field, and equalization current is accurately controlled through pulse width modulation; and continuously monitoring the efficiency, the temperature rise rate and the convergence in the equalization process, and dynamically optimizing the equalization parameters by using the fuzzy logic controller until the inconsistency reaches the standard. According to the technical scheme, accurate, self-adaptive, high-efficiency and safe single battery equalization management can be realized, the service life of the battery pack is effectively prolonged, and the operation reliability of the system is improved.

The invention discloses a battery failure reason prediction method and a computer storage medium, and the method comprises the steps: outputting EIS prediction data through a time sequence model according to the data in the charging and discharging process of a battery; according to EIS prediction or actual measurement data, relaxation time characteristic peak time and corresponding intensity are obtained; constructing a candidate circuit topology in the element library according to the characteristic peak time and intensity and EIS prediction or actual measurement data, and calculating and outputting an optimal equivalent circuit model character string and a parameter set; fusing relaxation time characteristic peak time and intensity, an optimal equivalent circuit model character string, a parameter set, a structural characteristic and a current / voltage derivative characteristic, splicing into a low-dimensional vector, and constructing an electrochemical state fingerprint; carrying out embedded representation on the electrochemical state fingerprints, and recognizing typical degradation state clusters through dimension reduction and clustering; and performing failure cause semantic attribution, and outputting a failure cause category and a prediction report. According to the invention, automatic identification and output of potential failure reasons of the battery are realized.

The invention discloses an adaptive weighted physical information neural network-based monomer voltage prediction method. The method comprises the steps of constructing a sample set according to real vehicle battery multi-dimensional time domain data; establishing a physical branch output monomer voltage physical prediction vector based on an equivalent circuit model; constructing data branches based on a graph attention mechanism to extract node time sequence features to obtain a data-driven prediction vector; a residual error is calculated, a compensation module generates a correction amount, and the correction amount is superposed to a physical prediction vector to obtain a final prediction result; and constructing a multi-step loss function of physical loss and data loss, and introducing an adaptive weighting mechanism to dynamically adjust loss weight iteration optimization parameters. According to the method, the precision and stability of single voltage prediction under a complex operation condition are effectively improved, and the adaptability and generalization performance of the model are enhanced.

The invention discloses a lithium battery dynamic rapid charging track generation method based on multi-state estimation and risk criterion, belongs to the technical field of electric vehicle lithium ion battery charging, and aims to solve the problem that the charging speed, the energy efficiency and the battery life are difficult to consider in the rapid charging process of a lithium ion battery in a wide temperature range. And a relatively high lithium plating safety risk exists. The method comprises the following steps: firstly, constructing an electrochemical thermal aging coupling model, and establishing a lithium plating risk criterion and risk map based on the model; then, on-line multi-state estimation is realized based on an electrothermal coupling equivalent circuit model, and a multi-objective optimization function and a safety constraint system which comprehensively consider charging time, energy efficiency and life loss are constructed; and finally, a model prediction control framework is utilized, a state estimation result and a risk criterion are fused, and an optimal charging current track is generated in a real-time rolling manner.

The invention provides a battery power state prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the data preprocessing of the working condition data of a battery, and determining the processed working condition data; constructing a battery model based on the microscopic electrochemical process, the mesoscopic equivalent circuit characteristic, the macroscopic thermal behavior and the processed working condition data; key parameters of the battery model are identified and updated based on a hierarchical online updating mechanism, so that the battery model determines internal state parameters of the battery; determining a dynamic security boundary in real time based on the internal state parameters; and by taking the dynamic safety boundary as a constraint condition, optimizing and solving the real-time available charging and discharging power of the battery through a predictive control algorithm. By constructing a multi-scale hybrid model, the precision and robustness of battery power state prediction are improved; and a hierarchical online parameter identification mechanism is adopted, so that the dynamic adaptive capacity of the model to battery aging and environment change is enhanced, and the prediction performance is ensured not to be degraded along with the use time.

The invention discloses a biconical antenna balun equivalent circuit modeling optimization method based on a multi-conductor transmission line, and belongs to the technical field of electromagnetic simulation. The method comprises the following steps: firstly, establishing a three-dimensional electromagnetic model of a biconical antenna balun based on a finite element method, and setting ports and defining boundary conditions in combination with an actual current transmission path; then, distribution parameters among conductors are extracted through a multi-conductor transmission line theory, and a Balun circuit topological structure is constructed; and finally, taking the antenna coefficient as an optimization target, carrying out iterative optimization on equivalent circuit element parameters by adopting a multi-target genetic algorithm, considering a plurality of optimization targets in a broadband range, and finally obtaining a high-precision equivalent circuit model. Through combination of electromagnetic simulation and an intelligent optimization algorithm, high-efficiency extraction of complex Balun structure distribution parameters and adaptive optimization of circuit parameters are realized, and the simulation precision of the biconical antenna is remarkably improved.

The invention discloses a method for improving stability of a network-forming converter by considering voltage and phase transients, which solves the problem of over-current control blind areas existing at the initial stage of a fault and at the moment of clearing in a traditional single current limiting method and overcomes the defect that the stability is sharply deteriorated when a traditional strategy is used for coping with a composite fault. The method comprises the following steps: S1, obtaining a virtual synchronous machine swing equation under a synchronous rotating coordinate system and an equivalent circuit model under the action of a circular current limiter; and S2, obtaining a nonlinear relation between the output power and the power angle of the converter, and analyzing a transient stability domain of the converter during a fault period. And S3, obtaining a mixed current limiting strategy, deducing an equivalent circuit model corresponding to the strategy, and further obtaining an output power expression corresponding to the strategy. And S4, obtaining a power damping term acting on an active power control loop, further obtaining a synchronization stability improvement strategy, and deducing a closed-loop model describing the system at the moment.

The invention provides a lithium ion battery SOC multi-model fusion estimation method based on a neural network, and belongs to the technical field of lithium ion battery management, and the method comprises the steps: carrying out the segmentation preprocessing of the current, voltage and temperature data of a battery according to the charging and discharging direction; fitting an SOC-OCV relation curve based on a second-order RC equivalent circuit model, and optimizing dynamic response parameters; synchronously predicting OCV and SOC by adopting a multi-task neural network; a high-precision SOC initial value is obtained by looking up a table through an OCV predicted value, and dynamic updating is carried out in combination with an ampere-hour integral method; comparing the ampere-hour integral result with the SOC directly predicted by the neural network in real time, and dynamically fusing according to an error; for a long-term standing scene, a life model is constructed based on historical data to predict SOH, the nominal capacity is updated, and the integral reference is reset. According to the method, a physical model and a data driving method are fused, and the SOC estimation precision and reliability under complex working conditions and long-term standing are improved through dual-path collaborative estimation and SOH closed-loop calibration.

A converter station AC filter group circuit breaker abnormal arcing modeling analysis method comprises the following steps: firstly, carrying out time mark alignment, amplitude calibration and filtering de-noising on wave recording signals such as AC bus voltage and filter branch current to obtain high-quality input data; then, combining a primary wiring diagram, enabling an alternating current system to be equivalent to an ideal power supply and series impedance, enabling circuit breaker arcing to be equivalent to arc resistance changing along with time, and constructing an equivalent circuit model comprising the power supply, the impedance, a capacitor, an electric reactor and dynamic arc resistance; establishing a state space equation according to a voltage and current constraint relation, and performing time domain solution by adopting a two-step two-order or three-order diagonal implicit Runge-Kutta method to obtain voltage and current responses of the whole process of the fault; on this basis, the arc resistance change is inversed according to the ratio of arc voltage to current, the current zero passage is identified through the resistance symbol change at adjacent moments, and the zero passage section is smoothed; and finally, the inversion result is verified again by means of the simplified equivalent model.

The invention discloses a power transmission line lightning stroke current waveform inversion method and device, terminal equipment and a storage medium, and belongs to the field of electric pulses. The method comprises the steps of obtaining electrical parameters of a to-be-processed power transmission line and fault waveform data of a plurality of detection points; constructing a lossy transmission line equivalent circuit according to the electrical parameters and determining a telegraph equation; replacing a space differential in the telegraph equation with a central difference, and replacing a time differential in the telegraph equation with a forward difference to determine a lightning wave propagation model; inputting the fault waveform data into a lightning wave propagation model, and traversing each detection point according to a preset time step to obtain intermediate waveform data; the fault waveform data comprises a first voltage parameter and a first current parameter of the head end; the intermediate waveform data comprises a second voltage parameter and a second current parameter of the tail end; and determining the original waveform of the lightning stroke current based on the intermediate waveform data and a preset shunting coefficient. By implementing the invention, the problem that the real lightning waveform during the lightning stroke fault is difficult to accurately obtain in the prior art can be solved.

This invention relates to the field of battery management technology, and in particular to a method for estimating the state of health (SHS) of a battery. This invention involves testing the battery under set experimental conditions and constructing a data table and estimation model based on test data obtained from various temperatures, currents, and depths of discharge. It does not require covering historical charge-discharge data for all battery operating conditions, nor does it require establishing complex electrochemical or equivalent circuit models. This improves the generalization, adaptability, and robustness of the data table and estimation model, and is easy to implement. Furthermore, the data table and estimation model fully consider the influence of temperature, current, and depth of discharge on the estimated SHS value. When using the data table and estimation model to estimate the SHS value, accurate SHS values ​​can be obtained based on the battery's temperature, current, and depth of discharge under actual operating conditions, thus improving the accuracy of SHS estimation.

The invention relates to the technical field of energy storage battery management, and provides an energy storage battery management system based on Bayesian fusion and model prediction control, and the system comprises a data collection and preprocessing module which collects the voltage, current and temperature data of an energy storage battery pack in real time; the electrochemical model prediction module is used for updating parameters by adopting a recursive least square method based on a second-order RC equivalent circuit model to obtain a capacity prediction value and prediction uncertainty; the data driving model prediction module is used for obtaining a capacity prediction value and prediction uncertainty through Monte Carlo dropout reasoning; the Bayesian fusion module is used for calculating an evidence weight based on the prediction uncertainty and carrying out Bayesian fusion on the capacity prediction value; and the model prediction control module is used for solving an optimal equalization strategy based on the fusion capacity prediction value and generating a control instruction to realize intelligent equalization scheduling of the energy storage battery pack. According to the invention, the accuracy of energy storage battery capacity prediction is improved, and adaptive battery management under complex working conditions is realized.

The invention belongs to the technical field related to operation control of a power system, and provides a transient instability judgment method and system of a network-building type converter in order to solve the problem that an existing transient instability judgment method of the network-building type converter is prone to causing maloperation or missed judgment of a control strategy, and a nonlinear equivalent circuit model of the network-building type converter is established. Calculating transient total energy and critical instability energy of the system in real time, and adaptively switching a control mode according to the magnitude relationship of the transient total energy and the critical instability energy; during normal operation, an original control strategy of the virtual synchronous machine is maintained, and oscillation is suppressed by using the damping characteristic of the virtual synchronous machine, so that the voltage and frequency supporting capacity of a power grid is maximized; and when an instability risk is detected, entering a fault ride-through mode to realize rapid and accurate identification and control of the instability risk.

The invention relates to the technical field of semiconductor device design and simulation, in particular to a multi-physics-field coupled photoelectric detector modeling method and system, and the method comprises the steps: calculating the spatial distribution of the carrier generation rate, the carrier recombination rate, the potential, the distribution of electron concentration and hole concentration, and heat effect feedback data of a surface incidence semiconductor photoelectric detector; calculating the current density distribution of the target surface incident semiconductor photoelectric detector under the dynamic input optical power; based on an automatic differential Newton iteration method, a multi-physics field coupling equation set is solved according to spatial distribution of a carrier generation rate, current density distribution, a carrier recombination rate, potential, distribution of electron concentration and hole concentration and heat effect feedback data; and establishing an equivalent circuit model of the surface incidence semiconductor photoelectric detector, and carrying out co-simulation with an external circuit. Therefore, the problems of insufficient physical field coupling, poor numerical stability, low calculation efficiency, inaccurate simulation and the like in a traditional photoelectric detector simulation method are solved.

The invention discloses an I-Nodal node instrument battery pack aging test method, which combines relaxation time distribution (DRT) and multi-resolution analysis (MRA) technologies, performs multi-time scale separation on electrochemical impedance spectroscopy (EIS) data of a node instrument battery pack, and constructs an equivalent circuit model (ECM) with strong physical interpretability and unique structure. And high-precision online estimation of the SOC is realized based on an optimization algorithm and extended Kalman filtering (EKF). Meanwhile, SOC and DRT high-order features are combined and input into a machine learning model, and collaborative prediction of SOH is achieved. According to the method, the false peak occurrence rate is lower than 3%, the SOC estimation error does not exceed + / -1.5%, the SOH prediction RMSE does not exceed 0.015, high precision and high robustness are kept under the complex working conditions of low temperature, high temperature, dynamic load and the like, and the method is suitable for an unattended node instrument energy management system running for a long period.

The invention relates to the technical field of industrial big data and unmanned aerial vehicle power management, in particular to an unmanned aerial vehicle power supply management system based on industrial big data. An ideal reference reconstruction module, a fault simulation injection module, a coupling judgment module and a feedback execution module are included; the system reconstructs a noise-free reference voltage curve by using dynamics and a battery equivalent circuit model through collecting an operation instruction, an attitude and environment data, and synchronously superposes fault features to generate a simulation curve; the core of the method is to calculate the waveform similarity between a real observation difference sequence and a theoretical obstacle-containing difference sequence, and execute threshold determination and strategy adjustment according to the waveform similarity. According to the method, conversion from static threshold judgment to dynamic form matching is realized, transient voltage drop caused by normal maneuvering can be automatically counteracted, and the problem of false alarm caused by incapability of distinguishing dynamic response and circuit failure is solved.

The invention discloses a lithium ion battery charge state estimation method, a battery management system and a storage medium, and belongs to the technical field of battery management systems. The method comprises the following steps: performing a pulse discharge experiment on a battery, and collecting charge-discharge data; then establishing a three-order RC equivalent circuit model, and fully considering ohmic internal resistance, electrochemical polarization and concentration difference polarization phenomena; performing optimization fitting on model parameters by adopting an improved I-LM algorithm, wherein the I-LM algorithm adaptively updates a damping coefficient through a multi-factor collaborative decision-making mechanism; and finally, estimating the SOC of the battery in real time based on fitting parameters and an ampere-hour integral method. Compared with the prior art, the method has the advantages that the prediction precision of the battery model is improved, the convergence speed of parameter extraction is remarkably increased, the adaptability to complex dynamic working conditions is enhanced, the calculation efficiency is high, and the method is suitable for being applied to an embedded system with limited resources; and a more accurate and more efficient SOC estimation solution is provided for a lithium ion battery management system.

The invention discloses a lithium ion battery health state estimation method based on conditional attention fusion, and belongs to the technical field of battery health management. Comprising the following steps: acquiring relaxation voltage and capacity data after a battery is charged; 6 voltage statistical features and 6 physical features based on second-order RC equivalent circuit model fitting are extracted based on the relaxation voltage, and preprocessing is carried out; constructing a health state estimation model comprising a condition attention fusion module and a multi-layer perceptron regression module, fusing working conditions and health features through a multi-head attention mechanism, and adopting residual connection; training and evaluating the basic prediction model on the source domain data set; and on the target domain data set, performing fine tuning on the last two layers of parameters of the multi-layer perceptron regression model by adopting a layered freezing strategy to obtain a fine tuning model, and performing online estimation on the SOH of the battery. The method has excellent cross-working-condition and cross-system migration capability, model interpretability is provided through attention visualization, and feature representation capability and model generalization performance are enhanced.

The invention relates to the technical field of battery management, and discloses a battery health state evaluation method and system for an electric tool energy supply device, and the method comprises the steps: obtaining multi-source sensor data in the operation process of an electric tool, and carrying out the preprocessing of the data; identifying and classifying multi-stage pulse discharge events, extracting multi-scale voltage response features and performing temperature compensation; equivalent circuit model parameter identification is carried out, abnormal identification results are checked and removed, and parameter aging characteristics are extracted; constructing a time sequence prediction model for prediction; evaluating the overall health state and the monomer consistency of the battery pack; defining a judgment standard of effective cycles, predicting the number of remaining effective cycles, calculating a confidence interval of a prediction result, and generating a battery management suggestion; formatting into a standardized evaluation report, carrying out data synchronization and model optimization through edge cloud collaboration, and recording a system operation log; according to the invention, accurate health state evaluation and residual life prediction are realized.

The invention relates to the technical field of battery management systems, and discloses a lithium battery SOC estimation method based on model predictive control and dynamic parameter calibration, and the method comprises the steps: constructing a second-order RC equivalent circuit model of a lithium battery, and obtaining a nonlinear function relation between an open-circuit voltage and a state of charge; real-time current and voltage data are collected, model parameters are identified on line through a recursive least square method with a forgetting factor, and dynamic calibration of the parameters is achieved; constructing a discrete state space prediction model based on the identified parameters; and solving an optimal control sequence through rolling optimization under a constraint condition by utilizing a model prediction control algorithm, and calculating a state-of-charge estimation value of the lithium battery in combination with feedback correction. According to the method, on-line parameter identification and model prediction control are combined, the problem of model mismatch under battery parameter time varying and dynamic working conditions is effectively solved, and the precision and robustness of lithium battery SOC estimation are improved.

The present disclosure relates to a method and apparatus for determining a state of charge (SOC) of a battery and a battery management system (BMS), so as to resolve a problem such as inaccurate estimation of the SOC. The method includes: acquiring state data of the battery, where the state data comprises current data and voltage data; determining each element parameter value in an equivalent circuit model of the battery based on the equivalent circuit model, error information, battery characteristic data, and the state data by using a recursive least square (RLS) prediction model; and determining an estimated value of the SOC of the battery based on the element parameter value in the equivalent circuit model, the state data, and the battery characteristic data according to a technique of an observer.

This application provides a method and apparatus for simulating the performance degradation of a high-power power supply. The method includes: obtaining a spatial state equation for the output voltage of the high-power power supply system based on an equivalent circuit model of the high-power power supply; acquiring performance degradation models of multiple components with degradation characteristics in the high-power power supply system, and importing the performance degradation models of the multiple components with degradation characteristics into the spatial state equation to obtain a spatial state model; using the spatial state model to obtain performance degradation array data of the high-power power supply corresponding to each number of runs; and concatenating the performance degradation array data of the high-power power supply corresponding to each number of runs to obtain performance degradation time sequence data of the high-power power supply.

The invention provides a battery state estimation method and system and a medium, and relates to the technical field of a battery management system.The method comprises the steps that a second-order RC equivalent circuit model is established, and parameters such as ohm internal resistance, polarization internal resistance and polarization capacitance are identified online through an RLS with a forgetting factor; constructing a microscopic state SRCKF channel and a macroscopic parameter SRCKF channel, performing real-time correction on an SOC influence factor and a current influence factor based on a filtering performance index, adaptively adjusting process noise and measurement noise covariance according to sub-channel logic, and introducing cross-channel cooperative self-adaption; the state channel outputs SOC and terminal voltage estimated values, the parameter channel outputs dynamic internal resistance and maximum available capacity, and SOH is calculated and output. The method improves the estimation precision and robustness under the working conditions of low SOC and large current, has good numerical stability, and is suitable for monitoring the whole life cycle of the power battery.

The invention provides a method for estimating the state of charge of a marine lithium iron phosphate battery based on model compensation and improved Kalman filtering. The method comprises the following steps: acquiring voltage and current data of the marine lithium iron phosphate battery; constructing a novel compensation model taking an improved Play hysteresis operator as a core, and quantifying the hysteresis voltage of the battery; on the basis of a second-order RC equivalent circuit model, hysteresis voltage is used as voltage input of an ideal voltage source in the battery and is injected into model internal resistance and capacitance parameter values obtained through identification at different temperatures, and an equivalent circuit model considering hysteresis at different temperatures is formed; performing parameter identification on the equivalent circuit model with hysteresis compensation by adopting an adaptive forgetting factor recursive least square method, and adaptively optimizing a forgetting factor and an initial parameter value, so that an error between a terminal voltage value simulated by the equivalent circuit model and an actual voltage value is minimum; and carrying out joint estimation on the battery end voltage and the SOC by adopting an adaptive square root unscented Kalman filtering algorithm, so that the error of the end voltage is minimum, and an estimated value of the battery SOC is obtained.

The invention relates to a method for calculating the power-on and power-off delay time performance of an electromagnetic valve, and the method comprises the following steps: obtaining the mass of a valve element of the electromagnetic valve, the spring stiffness and coil resistance data; establishing an electromagnetic valve equivalent circuit model, and listing a current equation; establishing an electromagnetic valve magnetic circuit mathematical model, and listing a magnetic field equation set; establishing an electromagnetic valve flow field mathematical model, and listing a flow field equation set; establishing a kinematic model of the electromagnetic valve, and listing a kinematic equation; using a finite element method to obtain a valve core electromagnetic force-displacement-current data table and a coil inductance-displacement-current data table; a finite element method is used for obtaining a valve element aerodynamic force-displacement data table; and obtaining the power-on and power-off delay time through data analysis processing and solving of the partial differential equation set. According to the method, prediction of the opening and closing delay response time of the electromagnetic valve is more accurate, visual adjustment in the simulation process is achieved, and the time for obtaining the response time of the electromagnetic valve and the test cost are greatly reduced while the preset precision requirement is met.

The invention relates to a valve-regulated lead-acid storage battery cross-scale joint estimation method and system, and the method comprises the steps: synchronously collecting voltage, current, temperature and ultrasonic signals, generating a high-dimensional feature tensor through space-time alignment and feature construction, and dividing the high-dimensional feature tensor into a physical mechanism channel and a data driving channel; the standardized features are input into a Group-Radial KAN network, orthogonal grouping modeling is carried out according to physical attributes, and nonlinear mapping is carried out by adopting a rational fraction; generating a physical prior weight based on equivalent circuit parameters of physical mechanism channel identification, and dynamically fusing the physical prior weight with a data-driven attention weight to realize feature weighting of physical guidance; sOC and SOH are jointly estimated through double-branch output, and the physical constraint weight and the capacity regeneration suppression coefficient are dynamically adjusted by a degradation stage self-adaptive module according to the cycle index and the SOH. The method significantly improves the estimation robustness of extreme working conditions and aging stages, effectively inhibits the capacity regeneration interference, has the full-life-cycle adaptive capability and the edge deployment feasibility, and greatly improves the estimation efficiency on the premise of not increasing the reasoning time consumption. The problem of estimation distortion caused by poor robustness of a data-driven model and capacity regeneration in the later stage of aging under extreme working conditions is solved.