2705 results about "Equivalent circuit" patented technology

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

Disclosed in the present invention are a method and system for small-signal stability analysis of a power plant containing grid-forming converters. The method comprises: establishing a grid-following converter impedance model; establishing a grid-forming converter impedance model; constructing a new energy power plant grid-connected system model containing grid-following converters and grid-forming converters; establishing a simplified equivalent circuit of a power plant grid-connected system; establishing a small-signal stability Nyquist criterion for a grid-connected new energy power plant containing grid-forming converters; and using a Nyquist plot to analyze the small-signal stability characteristics of the power plant grid-connected system. According to the present invention, the problem in the prior art of lacking quantitative and effective analysis of small-signal stability in new energy power plants containing grid-following converters and grid-forming converters is solved, and establishing a small-signal stability criterion for the power plants on the basis of established small-signal models enables quantitative analysis of the small-signal stability characteristics of a new energy power plant containing grid-forming converters under low grid strength conditions, and compared with conventional impedance modeling methods for new energy power plants, the impact of grid-forming converter integration on the small-signal stability of the new energy power plants is better reflected.

A system-side harmonic impedance estimation method and apparatus, and a storage medium and an electronic device. The method comprises: acquiring an h-th harmonic parameter equivalent circuit simplified on the basis of power grid characteristics, h being a positive integer greater than or equal to 2 (S1); on the basis of the h-th harmonic parameter equivalent circuit, establishing a system-side harmonic impedance estimation mathematical model (S2); acquiring amplitude and phase data of a harmonic voltage and a harmonic current at a PCC (S3); dividing the amplitude data of the harmonic voltage at the PCC into several stable intervals (S4); on the basis of the amplitude and phase data of the harmonic voltage and the harmonic current in the several stable intervals, establishing a variable coefficient regression model between a harmonic voltage phasor and a harmonic current phasor (S5); selecting an optimal window width of a kernel function of the variable coefficient regression model (S6); and calculating a system harmonic impedance on the basis of the variable coefficient regression model and the optimal window width (S7).

The invention relates to a storage battery quick charging safety control method, device and equipment and a storage medium. The method comprises the following steps: modeling an equivalent circuit and a thermal equivalent circuit of the storage battery to obtain a dynamic decoupling matrix; battery safety state data are obtained through parallel calculation; performing abnormal mode recognition to obtain an abnormal mode recognition result; inputting an abnormal mode recognition result into a heterogeneous graph gate loop unit, and extracting spatial-temporal features through heterogeneous graph convolution and a GRU structure to obtain deep fault features; calculating the charging safety capacity, and carrying out multiple constraint control on the thermal management and the charge state of the battery to obtain a charging power distribution scheme; the charging power distribution scheme is input into the two-stage model prediction controller for distributed power redistribution, a charging control instruction is obtained, the battery state can be monitored in real time, the abnormal mode can be accurately recognized, self-adaptive control is carried out based on multiple constraints, and the safe and efficient fast charging process is achieved.

The invention provides an energy consumption prediction and anomaly detection method and system based on digital twinning, and the method comprises the steps: collecting physical parameters in real time through a sensor, and the physical parameters comprise the cell temperature, the total current of a battery pack, the total voltage of the battery pack, the gradient, the vehicle speed and the mass; collecting battery state parameters by using a battery management system, wherein the battery state parameters comprise real-time energy consumption; the collected physical parameters are input into a Simulink and Cruise combined model for simulation, and model output prediction energy consumption is obtained; wherein the Simulink is used for establishing an equivalent circuit model of a power battery, and the control system, the sensor simulation module and the Cruise simulation model are used for establishing a whole-vehicle simulation model of the pure electric vehicle; comparing the model output energy consumption with the real-time energy consumption, and obtaining an energy consumption deviation value; whether the energy consumption deviation value exceeds a preset range or not is judged, and if yes, energy consumption is judged to be abnormal; and if not, evaluating the energy consumption of the whole vehicle so as to solve the technical problem that the actual energy consumption analysis, energy management and endurance mileage evaluation of the whole vehicle are difficult.

The invention relates to the technical field of battery state monitoring, and discloses a sightseeing vehicle battery state monitoring method, device and equipment and a storage medium, and the method comprises the steps: collecting the voltage, current and temperature data of a sightseeing vehicle battery, and carrying out the preprocessing, and obtaining a battery operation parameter data set; constructing a battery equivalent circuit model, and performing global and local two-stage optimization to obtain an optimal parameter set of the battery equivalent circuit model; constructing a state equation and an observation equation, and carrying out initialization setting on a covariance matrix of the triple forgetting factor adaptive extended Kalman filter to obtain a battery state observation model; priori state estimation is obtained through time updating operation, posterior state estimation is obtained through measurement updating operation, hierarchical estimation is carried out on the charge state and the polarization voltage of the battery, and the state characteristic quantity of the battery is obtained; according to the method, the reliability and the safety of the battery management system of the sightseeing vehicle are improved.

The invention relates to the technical field of battery model construction, in particular to a construction method and system of a lithium ion battery equivalent circuit model and a medium. The method comprises the following steps: collecting initial battery electrochemical parameters, obtaining ion migration characteristic data through charge density gradient decoupling, further reconstructing a polarization potential field to generate a lithium ion battery charge-discharge curve, performing potential migration analysis on the curve, mapping the curve into time domain charge mapping data, and calculating the time domain charge mapping data according to the time domain charge mapping data. Voltage distribution data are captured through transient voltage, circuit topology is reconstructed, circuit temperature distribution data are obtained, electric field-temperature field cross locking is carried out to determine a temperature influence coefficient, an initial equivalent circuit model is corrected based on the temperature influence coefficient, charging and discharging simulation is carried out on the corrected model, and aging factors are embedded. And finally generating an equivalent circuit model of the lithium ion battery. The method for constructing the equivalent circuit model of the lithium ion battery is more reliable and more intelligent.

The invention discloses a DC voltage divider parameter anomaly detection method based on harmonic characteristics, and the method comprises the steps: deploying an isolation type voltage sensor at a bus of a DC power transmission system, carrying out the voltage sampling through combining with an ADC, carrying out the data buffering and preprocessing, extracting a fundamental wave component and a target harmonic component through the fast Fourier transform, and calculating the harmonic distortion rate. And constructing a low-voltage arm capacitance and harmonic distortion rate model based on a voltage divider secondary circuit equivalent circuit and an energy distribution principle, and calibrating the low-voltage arm capacitance and harmonic distortion rate model. And designing a multi-objective optimization scheduling algorithm based on energy storage capacity and capacitance characteristics, and realizing dynamic power distribution and harmonic suppression of the system. And after abnormal triggering, the system automatically loads a capacitance compensation coefficient, adaptively adjusts algorithm parameters in combination with a measurement loop PI control link, reduces a harmonic amplification effect, and ensures that a measurement error is within an acceptable range. Harmonic monitoring precision and capacitance compensation optimization capability of the direct-current power transmission system are effectively improved, and stable operation of the system is guaranteed.

The invention discloses a lithium ion battery state-of-charge estimation method based on F-FOMIAEKF multi-working-condition wide temperature, and the method comprises the steps: calculating an SOC reference value according to a capacity value at a first working condition wide temperature; after the first working condition wide temperature experiment, constructing a data set containing W groups of data points, and deducing an OCV-SOC relational expression by using a polynomial fitting method; establishing a lithium ion battery fractional order second-order equivalent circuit model and a state-space equation; identifying parameters to be identified in the model; and under different working condition wide temperatures, state variables and observation variables in a state-space equation of the fractional-order second-order equivalent circuit model are estimated by adopting an SOC estimation algorithm according to the identified parameters, and an SOC estimation value is obtained. On one hand, a fractional order model is introduced; on the other hand, a filter updating process in the whole estimation process is optimized by introducing a multi-information theory, and in addition, an estimation algorithm is optimized by updating statistical characteristics of process noise and measurement noise in real time by introducing an adaptive theory, so that the accuracy and the stability of SOC estimation are improved.

The invention discloses a lithium ion battery internal short circuit fault detection method based on time-frequency fusion, and the method comprises the steps: firstly constructing a fractional order equivalent circuit model fused with an electrochemical aging mechanism, and simulating the paths of conductivity reduction, active material loss and lithium inventory reduction in combination with an aging empirical formula; joint modeling of the aging process and the random internal short circuit is achieved, and multi-cycle voltage and current and electrochemical impedance spectroscopy data are obtained; secondly, extracting time domain features by using a time domain attention enhanced long-short-term memory network, and analyzing frequency domain information by using a multi-scale frequency sensing convolutional neural network; and then weighting and screening two types of modal features through a dynamic gating fusion module, introducing a cross attention mechanism to establish dependency mapping between time-frequency domain features, and finally outputting an internal short circuit fault detection result. The method can solve the problems that in the lithium ion battery aging process, due to lithium dendrite growth, the internal short circuit early fault is high in concealment, and single time-frequency characteristics are difficult to detect, and early high-precision recognition of the internal short circuit fault can be achieved.

The invention relates to an agricultural biomass characteristic intelligent detection method, system and device based on multi-frequency conductance coupling and a medium, and the method comprises the following steps: applying an electric excitation signal to an agricultural biomass sample, and synchronously collecting multi-physics coupling response data; based on the multi-physics field coupling response data, constructing a multi-scale equivalent circuit model comprising a resonance unit, a constant phase unit and a diffusion impedance unit; synchronously optimizing a resonance unit parameter, a constant phase unit parameter and an impedance unit parameter of the multi-scale equivalent circuit model through a Bayesian joint inversion algorithm to obtain an optimal parameter combination; and according to the optimal parameter combination, calculating a preliminary detection result of biomass components containing cellulose crystallinity, lignin crosslinking degree and water content, combining the collected environment temperature and humidity sensing data to implement dynamic compensation, and outputting a final detection result. Therefore, the detection precision, the environment robustness and the operation reliability are synergistically improved in three aspects of hardware design, model construction and algorithm optimization.

The invention provides a multi-dimensional grid-connected test system for a photovoltaic string inverter, and the system comprises a power grid simulation module which is used for constructing a power grid simulation environment based on a multi-dimensional coupling mechanism of voltage fluctuation, frequency deviation and harmonic distortion; the photovoltaic array simulation module is used for constructing IV characteristic dynamic deduction models of different irradiance and temperature gradient effects based on a photovoltaic array equivalent circuit physical model to realize a natural simulation environment; the loading module is used for loading the power grid simulation environment and the natural simulation environment to the photovoltaic string inverter to be tested; the data acquisition module is used for monitoring running state data of the photovoltaic string type inverter in real time and constructing a running state data set containing multi-physical-quantity coupling characteristics; and the diagnosis module is used for inputting the running state data set into the trained LSTM neural network model to obtain a test result of the photovoltaic string inverter. According to the invention, the efficiency and convenience of the grid-connected test of the inverter can be improved, and the accuracy and reliability of the grid-connected performance of the inverter are ensured.

The invention belongs to the technical field of battery management, and relates to a battery equivalent circuit model parameter identification method and system based on particle swarm optimization, and the method comprises the steps: obtaining the dynamic test data of a battery; an OCV-SOC relation is obtained; calculating a real-time SOC sequence and performing interpolation to obtain a search OCV sequence; defining a second-order RC equivalent circuit model; constructing an objective function based on an error between prediction of the OCV sequence and search of the OCV sequence; and searching an optimal model parameter by adopting a particle swarm optimization algorithm and taking minimization of the target function as a target. By optimizing the matching degree of predicting the OCV and searching the OCV instead of directly fitting the terminal voltage, the sensitivity and accuracy of parameter identification on the dynamic characteristics of the battery are improved, and efficient and accurate identification on the parameters of the second-order RC model is realized in combination with the global optimization capacity and robustness design of PSO (Particle Swarm Optimization).

The invention discloses a lithium battery SOC estimation method based on an ordinary differential equation neural network, and the method comprises the steps: S1, collecting the cycle charging and discharging voltage and current data of a lithium battery, and forming an original data set; s2, establishing a second-order RC equivalent circuit model of the lithium battery, and determining a state equation, an output equation and an ordinary differential equation set; s3, integrating an ordinary differential equation set into a recurrent neural network to parameterize a hidden unit, and establishing an ODE-RNN Net model fused with an equivalent circuit model ordinary differential equation; step S4, initializing parameters of the ODE-RNN Net model, and training the ODE-RNN Net model by using the original data set; and S5, inputting the working current of the lithium battery at the current moment by using the trained ODE-RNN Net model, and predicting the subsequent working state output and the state of charge of the lithium battery. According to the method, the demand of training data volume can be reduced, and the accuracy of battery modeling and battery state-of-charge estimation is improved.

The invention relates to a data-driven lithium ion battery voltage prediction modeling method fusing physical information, and belongs to the technical field of batteries. According to the method, a multi-working-condition battery operation database is constructed, a terminal voltage prediction model fusing a physical model and a neural network is designed, a mathematical constraint relation is established for equivalent circuit parameters and neural network intermediate variables, and data driving characteristics and physical parameters are jointly optimized by using a gradient descent method. The method specifically comprises the steps of collecting battery current, voltage and temperature data; constructing an input feature and an output tag; outputting an intermediate variable through a parameter layer and associating an equivalent circuit parameter; and introducing physical loss function constraint model training to ensure the physical rationality of parameters. The method has high precision of data driving and interpretability of a physical model, improves multi-working-condition adaptability through temperature compensation and parameter constraint, and can be widely applied to battery state estimation and health management of electric automobiles and energy storage systems.

The invention discloses a harbor shore power system oscillation damping suppression method based on heuristic probability optimization. According to the method, sliding window preprocessing is carried out on an alternating current bus side current time domain oscillation signal, modal decomposition is carried out based on a Prony algorithm, and modal energy is calculated to judge an oscillation mode; the discriminated oscillation modes are aggregated and mapped into equivalent circuit elements, a corresponding three-order circuit state matrix is constructed, and the damping ratio of each mode is determined according to the characteristic value of the matrix; taking a damping ratio as an evaluation index, introducing an influence factor to construct a multi-objective optimization function, parameterizing the parallel adjustable impedance, and limiting an adjustable range; and searching the control parameters by adopting a heuristic probability optimization strategy to obtain final damping control parameters, and downloading the final damping control parameters to the parallel adjustable impedance device to realize online dynamic adjustment. According to the method, multi-modal identification, physical magnitude mapping and online adaptive optimization can be realized, and the suppression capability and the operation stability of the shore power system on sub / super synchronous oscillation are improved.

The disclosure provides a circuit modeling method for a dual-active-bridge-type micro-inverter. The method equates dual-active-bridge-type micro-inverter to a standard dual-active-bridge circuit by establishing an equivalent circuit model, thereby establishing a third-order large signal model and a third-order small signal model of the micro-inverter, so as to facilitate the solution of a circuit state variable and the analysis of the dynamic performance.

The invention relates to the technical field of power electronics, in particular to a filter equivalent circuit design simulation system suitable for an energy storage converter, which comprises a main circuit topology unit, a control strategy unit, a parameter optimization and design unit, a simulation engine unit and a performance evaluation unit, wherein the main circuit topology unit is used for building a filter equivalent circuit topology of the energy storage converter and configuring passive element parameters of an inductor, a capacitor and a resistor. According to the method, automatic high-robustness optimization of filter parameters is realized through genetic algorithm-particle swarm hybrid optimization, multi-objective collaborative optimization and digital twinning auxiliary optimization, and a'design-optimization-verification-iteration 'closed loop is formed by combining dynamic working condition evaluation, reliability modeling and artificial intelligence auxiliary analysis, so that the method has the advantages of high robustness and high robustness. The harmonic suppression capability, efficiency and long-term operation reliability of the filter are remarkably improved, the design period is shortened, and the development cost is reduced.

The present disclosure provides a method and device for calculating fault measurement impedance of a hybrid-connected grid-following and grid-forming wind power system. The method includes: obtaining characteristics of the grid-following and grid-forming converters and a corresponding low voltage ride-through control strategy; establishing an equivalent circuit under a corresponding fault state; conducting a simplified analysis on the wind power system to obtain an equivalent network diagram; obtaining a sub-circuit diagram of each power source excitation acting alone; obtaining short-circuit currents on both sides of the corresponding system and a grid connection point voltage; superimposing to obtain total short-circuit current on both sides of the wind power system and total grid connection point voltage; and obtaining a measurement impedance at a protection installation location on the new energy source side. The method can accurately calculate the measurement impedance value at the protection installation location after the wind power system has fault.

A method for battery power management based on battery nonlinearity characterization and compensation using a nonlinear resistor-capacitor (RC) equivalent circuit model (ECM) may include: measuring a current, a terminal voltage, and a temperature of a battery; implementing the nonlinear RC ECM comprising fixed, logarithmically spaced time constants and current dependent resistances and capacitances; constraining resistances of the nonlinear RC ECM to be nonnegative and performing a nonlinearity characterization procedure using the nonlinear RC ECM configured to characterize variations of a battery impedance with current amplitudes over a range of frequencies and create ECM parameter variations with current amplitudes by fitting impedance variations by nonlinear RC ECMs; performing online estimation of the nonlinear RC ECM using a battery current and a terminal voltage of the battery over time; and modifying the online estimated nonlinear RC ECM using ECM parameter variations to compensate for nonlinearity of the battery.

The invention relates to the technical field of wire harness fault diagnosis, and discloses a vehicle-mounted wire harness fault diagnosis method and device and a storage medium. The method comprises the following steps: carrying out equivalent circuit modeling on a measurement node and a wire harness section of the vehicle-mounted wire harness to obtain a node connection relation matrix; performing feature analysis on the voltage data and the current data of the measurement nodes to obtain a fault feature vector and a fault type discrimination rule base; inputting the voltage data, the current data and the node connection relation matrix into a recursive variational graph automatic encoder for data reconstruction to obtain time sequence feature reconstruction data and topological relation reconstruction data; carrying out distributed sub-model division and model fusion to obtain a standardized feature vector; and performing matching analysis on the standardized feature vector and the fault type judgment rule base to obtain fault position information and fault type information, so that automatic optimization and screening of fault features are realized, and the reliability and interpretability of a diagnosis result are improved.

The invention discloses a sodium ion battery SOC estimation method based on extended Kalman filtering. The method comprises the following steps: acquiring charging and discharging voltage, current and open-circuit voltage OCV data of a sodium battery through an HPPC test and a capacity test; and fitting a function relationship between the OCV and the SOC based on the standing balanced voltage data. And constructing a three-order RC equivalent circuit model, and identifying ohmic resistance, polarization resistance and polarization capacitance parameters in the model through curve fitting. Based on the model, a state vector containing SOC and polarization voltage is defined, and a nonlinear state equation and a terminal voltage observation equation are established. And predicting the state vector and the covariance matrix at the current moment according to the state vector and the covariance matrix at the previous moment by using an extended Kalman filtering algorithm, calculating Kalman gain by combining the actually measured terminal voltage, correcting the prediction result, and updating the SOC estimation value. And through loop iteration, real-time high-precision dynamic estimation of the SOC of the sodium-ion battery is realized. The method provides an efficient and accurate SOC estimation solution for a sodium ion battery management system.

A method for state-of-health monitoring of a powertrain component in an electric vehicle system includes: determining an equivalent circuit model of the powertrain component; modeling heat losses in the powertrain component considering both transient and steady-state conditions; modeling heat flow through the powertrain component based on one or more material properties of the powertrain component; determining a temperature of a particular structure within the powertrain component; and determining, using a Rainflow algorithm, a number of temperature cycles until failure of the particular structure based on the temperature of the particular structure.

The invention discloses a harmonic traceability method based on a harmonic power direction of an electric power system, which belongs to the technical field of electric power quality of the electric power system and comprises the following steps of: constructing matrixes of fundamental waves and harmonic signals, initializing active and reactive power deviation values of nodes, and adopting a decoupling algorithm combined with a Jacobian matrix iteration method to carry out harmonic traceability. And sequentially calculating the fundamental wave voltage and each harmonic current, and finally obtaining the power flow direction and judging the harmonic source attribute. When harmonic power flows from a load to a power grid, the node is identified as a harmonic source; and when the harmonic power flows to the load from the power grid, identifying the node as a harmonic receiving point. An equivalent circuit model is used for separating harmonic influences of a linear load and a non-linear load, and a real-time monitoring technology is combined to realize dynamic tracking and accurate positioning of a harmonic source. The method is low in calculation complexity, excellent in convergence performance and suitable for a modern power distribution network scene where distributed energy and power electronic equipment are widely connected, the harmonic suppression efficiency is remarkably improved, and the safety and stability of power grid operation are ensured.

The invention relates to a lithium battery SOC estimation method based on a multi-scale physical information neural network, and belongs to the technical field of battery SOC estimation. The method comprises the steps that S1, a first-order RC equivalent circuit model is adopted to describe the dynamic characteristics of a battery, and electrical parameters in the first-order RC equivalent circuit model serve as Mama model output and serve as physical knowledge; s2, performing multi-scale fusion on the voltage features, the current features, the time features and the edge features to obtain new features, and taking the new features as input of a Mama model; s3, constructing a Mama model, wherein the Mama model comprises SSM, a convolutional layer, a linear layer and an element operator; the SSM represents a selective state space model and is formed by combining a structured state space sequence model with a selection mechanism and a scanning module; and S4, estimating the SOC of the lithium battery by using a Mamba model, and improving the model training efficiency by adopting an SPKF adaptive weight. According to the invention, the accuracy and reliability of SOC prediction are improved.

The present invention discloses a prediction method for a crosstalk spike in a half bridge of current-source inverter with silicon carbide devices, where, the prediction method includes calculation of the crosstalk voltage spike in the circuit of current-source type double-pulse test and analysis of the effect of each parameter on the magnitude of the crosstalk spike. The prediction method of the present invention analyzes the horizontal conduction crosstalk mechanism between the gate-source junction of the MOSFET caused by the high-frequency switching action of the silicon carbide (SiC) MOSFET and the SiC Schottky diode connected in series, and derives the prediction model for the crosstalk voltage spike in the half-bridge of current-source inverter. Based on the equivalent circuit of the current-source-type double-pulse test and the crosstalk-induced mechanism, the extreme value of crosstalk voltage spike is accurately predicted, which is helpful to analyze the effects of the drive circuit and the parameters of the power circuit on the crosstalk voltage spike through the prediction model.

The invention discloses a first-order ECM-based parameter and SOC joint estimation method and system, and the method comprises the steps: constructing a first-order RC equivalent circuit model, and constructing a state-space equation of a lithium ion battery based on the first-order RC equivalent circuit model; corresponding open-circuit voltage OCV values under different SOCs are collected according to an HPPC experiment, and the OCV values are fitted to obtain an OCV-SOC relation curve; constructing a state-space equation based on model parameters, performing parameter identification on the model by using a Kalman filtering algorithm optimized based on a suburb wolf algorithm, and calculating a terminal voltage value through an identification result to verify the identification precision; updating a state-space equation related to the SOC based on a parameter identification result; and on the basis of the state-space equation, rapid convergence from the SOC estimated value to the SOC actual value is realized by using a self-adaptive Kalman filtering algorithm based on an innovation matrix. The method is high in estimation precision.

The invention discloses an aged battery state collaborative estimation method and system, and the method comprises the following steps: calculating a battery health state value according to the relation between the battery health state and the battery internal resistance, and collecting the current battery discharge data when the battery health state value reaches a preset point; establishing a second-order RC equivalent circuit model, combining the SOC-OCV fitting curve with the second-order RC equivalent circuit model, calculating to obtain a characteristic equation and a state-space equation of the second-order RC equivalent circuit model, and outputting an observation equation; carrying out parameter identification on the second-order RC equivalent circuit model, estimating the real-time charge state value of the battery, calculating the health state value of the battery by utilizing the calculated real-time charge state value of the battery and the discharge current, and updating the health state value of the battery and an SOC-OCV fitting curve in time by judging the variation of the health state of the battery, or replacing the battery in time; according to the invention, the estimation precision of the state of charge SOC of the aged battery can be improved, and the requirement of algorithm complexity on a hardware system is remarkably reduced.

The invention relates to the technical field of batteries, and discloses a battery charge state estimation method and device, equipment and a storage medium. The method comprises the following steps: constructing a fractional order equivalent circuit model of a to-be-tested battery; constructing a first state equation set according to the fractional order equivalent circuit model, and discretizing the first state equation set to obtain a second state equation set; performing parameter identification on the fractional order equivalent circuit model by adopting a particle swarm optimization algorithm; and estimating the state of charge of the to-be-tested battery by adopting a self-adaptive extended Kalman filtering algorithm according to the second state equation set after the parameters are identified. According to the method, the equivalent circuit model is constructed by adopting the fractional order model, the fractional order model can better reflect the nonlinear characteristic of the battery, so that the precision of the circuit model is improved, and on the basis of the established equivalent circuit model, the state of charge is estimated in combination with the adaptive fractional order extended Kalman filtering algorithm, so that the accuracy of the circuit model is improved. Therefore, the accuracy of state-of-charge estimation can be improved.

The invention discloses a transient optimization control method based on a parallel system of a virtual synchronous machine and a grid-following converter. The method comprises the following steps: 1, constructing a topological structure of the parallel system of the virtual synchronous machine and the grid-following converter; 2, based on the topological structure, establishing a mathematical model of a parallel system, including an active loop equation and a reactive loop equation of a virtual synchronous machine and a phase-locked loop equation of a grid-following converter; 3, constructing an equivalent circuit, a network equation and a transient analysis model of the parallel system based on the mathematical model; 4, on the basis of the equivalent circuit, the network equation and the transient analysis model, determining conditions met by respective balance points of the virtual synchronous machine and the grid-following converter by using a balance point method; and 5, when the power grid has a fault, constructing a transient optimization strategy according to a condition satisfied by the balance point so as to realize transient optimization control of the parallel system. According to the method, the control modes of a virtual synchronous machine power loop and a grid-following type converter phase-locked loop can be improved, so that the transient stability of a parallel system converter is improved.