2223 results about "Equivalent circuit" patented technology

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.

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 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 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 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.

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 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 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.

The present disclosure discloses a battery SOC estimation method and apparatus. The method includes: calling an equivalent circuit model of a to-be-tested battery, and acquiring offline parameters of the to-be-tested battery; calculating alternately a first SOC value of the to-be-tested battery and online parameters of the to-be-tested battery based on the equivalent circuit model and the offline parameters, and selecting the first SOC value, obtained after the calculation is performed for a preset number of times, as an SOC estimation value; and calculating a current SOC value of the to-be-tested battery according to the SOC estimation value. By using an embodiment of the present disclosure, problems of accuracy reduction and an SOC jump, caused by only using the offline parameters to estimate an SOC, are solved, and influence of electric current accuracy and temperature, caused by only using an ampere-hour integration method, is reduced, thereby improving estimation accuracy and stability.

The invention relates to a joint estimation method for SOC and SOH of a power battery, in particular to a joint estimation method for SOC and SOH of a lithium ion power battery based on FOASEKF-EKF, comprising fractional order equivalent circuit models of two parallel fractional order CPE branches, and providing a hybrid genetic algorithm HGA fusing a differential evolution strategy and an adaptive variation mechanism. Accurate estimation of SOC and terminal voltage under a fast time scale is realized by introducing a sliding-mode observer and an FOASEKF, periodic online correction is performed on model parameters and battery capacity based on an EKF under a slow time scale, and high-precision and high-robustness battery SOC and SOH joint estimation is realized. The method is suitable for complex industrial environments such as electric automobiles and rail transit, does not need to set a large number of hyper-parameters, does not excessively depend on the quality and quantity of data, has good interpretability, adaptability and engineering practicability, can still achieve high-precision cooperative estimation of SOC and SOH especially under the working conditions of frequent start and stop and unsteady operation, and has good application prospects. And misjudgment and drift estimation risks are obviously reduced.

The invention discloses a battery health state and residual life combined determination method, electronic equipment and a medium, and relates to the technical field of battery management. The method comprises the following steps: acquiring battery operation parameters at the current moment; constructing a hybrid model based on a physical model and a data driving model, and controlling the hybrid model through an adaptive gating mechanism to determine a current battery health state based on the battery operation parameters; the physical model is realized by introducing a thermal model on the basis of an equivalent circuit model, and the data driving model is realized by introducing a lightweight neural network on the basis of a neural network model; the current battery remaining life is determined based on the current battery health state through a life prediction model and an optimization algorithm, the life prediction model is achieved based on at least one of degradation curve extrapolation and multi-step rolling prediction, and the optimization algorithm is achieved based on Bayesian optimization or reinforcement learning. According to the scheme, the accuracy of determining the battery health state and the residual life can be improved.

The invention relates to a lithium battery state-of-charge and state-of-health joint estimation method, and belongs to the technical field of lithium ion batteries. The method comprises the following steps: performing a cyclic aging experiment on a battery under a cyclic working condition; an open-circuit voltage curve is extracted through data analysis, a first-order equivalent circuit model is selected to construct a circuit, and a state equation is written; carrying out parameter identification by adopting a multi-forgetting factor recursive least square algorithm, and constructing an SOC estimator of an unscented Kalman filter based on a state equation and an observation equation to carry out SOC estimation; constructing an SOH estimation observer based on an extended Kalman filter, and estimating the SOH by using the estimated SOC; carrying out segmented dynamic identification on the open-circuit voltage curve by adopting an average moving method in combination with recursive least squares; and coupling the SOC estimator and the SOH estimator, and realizing SOC and SOH joint online estimation by applying the dynamically identified open-circuit voltage curve.

A method for estimating current-dependent non-linear equivalent circuit model (ECM) parameters of a battery may include measuring a battery voltage across terminals of the battery and a battery current drawn from the battery, modelling behavior of the battery with an impedance model having impedance model parameters, dynamically analyzing the battery current drawn from the battery by a load, dynamically determining safe operating limits for an augmented current for augmenting the current drawn by the load, based on analysis of the battery current and the safe operating limits, determining the augmented current, generating the augmented current to be drawn from the battery, and estimating the impedance model parameters when the augmented current is drawn from the battery.

Provided in the present application are a balanced charging method and apparatus for a battery pack, and a computer program product. Charging current control consists of two stages, i.e., a "pulse square-wave excitation" stage and a "multi-stage current-reduction charging" stage. During the pulse square-wave excitation stage, the parameter identification of an equivalent circuit model and the joint estimation of state variables are realized, thereby providing reliable battery parameters for a subsequent balancing process. During the multi-stage current-reduction charging stage, a flyback converter is used to control a balanced current of each battery cell, and the battery cells remain in a charging state throughout a balancing process, thereby effectively mitigating the impact of a hysteresis voltage.

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 discloses a field self-adaptive retired battery health state estimation method based on an EIS. The method comprises the following steps: firstly, extracting electrochemical impedance spectroscopy data of a retired aged battery; and then a fractional order battery equivalent circuit model is constructed, and the characterization capability of dynamic degradation of an electrode interface is enhanced through a constant phase element. Fractional order battery equivalent circuit model parameters are extracted from electrochemical impedance spectroscopy data based on nonlinear least square fitting, Pearson correlation analysis is adopted to screen parameters having a significant relationship with SOH, then a recursive feature elimination method is used for further screening, and an optimal parameter subset strongly associated with the state of health is extracted from a high-dimensional parameter set. And finally, constructing a decommissioned battery state-of-health estimation method of a field adaptive method, modeling the influence of different operation conditions on battery aging through knowledge migration, and realizing cross-working-condition decommissioned battery state-of-health estimation with both physical interpretability and working condition robustness.

The invention relates to a data-driven lithium ion battery state prediction method fusing physical information, and belongs to the technical field of batteries. The method comprises the steps that S1, a lithium ion battery multi-working-condition operation database is established, and battery current, terminal voltage and operation temperature data are collected through charging and discharging experiments; s2, a lithium ion battery state prediction model fusing a physical model and a neural network is constructed, physical-prediction joint loss of the model is calculated, the model is trained through a gradient descent algorithm, and physical information is fused into the model through a mathematical relationship between equivalent circuit model parameters and neural network intermediate variables and the joint loss of the model; and S3, inputting real-time battery state data based on the trained model, and predicting a battery state value at the next moment, including SOC or SOT and the like. According to the invention, accurate prediction of the state of the lithium ion battery driven by data fused with physical information can be realized.

The invention relates to the related technical field of distribution transformer insulation aging, in particular to a frequency domain diagnosis method, system and device for distribution transformer insulation aging and a medium, and the method comprises the steps: carrying out the frequency response test of a to-be-tested transformer, and obtaining a first frequency response curve; performing a frequency response simulation test on a transformer equivalent circuit model pre-constructed based on the to-be-tested transformer to obtain a second frequency response curve; constructing a target function based on the deviation of the first frequency response curve and the second frequency response curve; taking element parameters in the equivalent circuit model of the transformer as position vectors, taking the objective function as a fitness function, and adopting a multi-objective particle swarm algorithm to solve the element parameters to obtain optimal values of the element parameters; respectively comparing the optimal values of the element parameters with corresponding normal values to obtain insulation aging degrees; according to the method, the insulation aging degree can be obtained by comparing the element parameters obtained through optimization with the normal unaged element parameters.

The invention belongs to the technical field of eddy current sensor calibration, and discloses an eddy current sensor for a high-temperature environment and a calibration method thereof. The method comprises the following steps: synchronously acquiring working current, voltage, surface position, temperature, standard displacement signals and original output voltage of an eddy current sensor, constructing an oxide layer equivalent circuit model, and optimizing to obtain thickness distribution; building a thickness increase model, and building a correlation model to calculate time-varying impedance; calculating a sensitivity coefficient function, and designing a piecewise calibration function to obtain a calibration parameter set; and finally, dynamically compensating the original output voltage in combination with the calibration parameter set to obtain the calibrated displacement. A temperature field, a standard displacement signal and an original voltage signal are collected, a temperature-oxide layer-impedance three-dimensional dynamic model is established, and the nonlinear influence of oxide layer growth and temperature gradient is compensated in real time.

A system for estimating current-dependent non-linear equivalent circuit model (ECM) parameters of a battery may include measurement circuitry for measuring a battery voltage across terminals of the battery and a battery current drawn from the battery. The system may also include an impedance model having impedance model parameters, the impedance model for modelling behavior of the battery. The system may further include an augmented stimulus signal generator configured to dynamically analyze the battery current drawn from the battery by a load, based on analysis of the battery current, determine an augmented current for augmenting the current drawn by the load; and generate the augmented current to be drawn from the battery. The system may also include a battery model estimator for estimating the impedance model parameters when the augmented current is drawn from the battery.