3542 results about "Terminal voltage" patented technology

The invention relates to the technical field of battery management, and discloses an equalizing charging method, device and equipment for a power battery pack. The method comprises the following steps: performing parameter acquisition on a power battery pack, measuring terminal voltage values, temperature values and charge and discharge current values of each single battery, and calculating a state-of-charge value and an internal resistance change rate of each single battery; inputting into a multi-head self-attention network for feature extraction, and generating a state correlation feature matrix; constructing an Actor network and a Critic network based on the state correlation characteristic matrix, and outputting an equalization current value, a constant-current or constant-voltage equalization mode and equalization time through the Actor network; according to the equalization current value, the equalization mode and the equalization time, the DC-DC converter in the main equalization loop is controlled to generate constant current output, or the Buck-Boost converter in the secondary equalization loop is controlled to generate constant voltage output, the equalization efficiency of the power battery pack is guaranteed, meanwhile, energy loss is reduced, and temperature self-adaptive control over the equalization circuit is achieved.

The invention relates to a model predictive control charging optimization method based on a dynamic power state, which initiates a'dynamic power state collaborative optimization 'mechanism, takes a real-time power upper limit as an active optimization target instead of a fixed constraint condition, and breaks through the technical bottleneck of power limitation passive response in a traditional charging strategy. The method specifically comprises the following steps: constructing an electric-thermal-aging multi-physics field coupling model of the lithium ion battery, updating electric-thermal characteristic parameters in real time through an online parameter identification algorithm, and synchronously estimating a core temperature and an aging state in combination with a double-Kalman filtering state observer; innovatively establishing a four-dimensional objective function optimization model containing a dynamic power state, and performing multi-objective collaborative optimization on a power upper limit, a charging speed, a capacity fading rate and a current fluctuation rate; and designing a dynamic rolling optimization algorithm based on a model prediction control framework, and solving the optimal charging current meeting the dynamic power distribution requirement of the power grid in real time under the hard constraint of ensuring the maximum core temperature and terminal voltage.

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 an energy storage system state evolution trend prediction method based on multi-source data fusion. The method comprises the steps of terminal voltage, current and temperature time sequence data acquisition, time sequence segmentation normalization, multi-physics field coupling feature construction, trend prediction model construction and training and energy storage system state evolution trend prediction. According to the method, the distinguishing capacity of the model for charging and discharging physical characteristics is improved, meanwhile, the voltage change rate, the multi-dimensional feature vector of the differential internal resistance and the thermal-electric coupling effect and the explicit encoding electric-thermal-resistance coupling relation are constructed, the transient response and the temperature hysteresis effect can be effectively captured, and then the model can be used for analyzing the charging and discharging physical characteristics. A degradation-aware cross-cycle feature extraction and gating mechanism is adopted, short-term fluctuation and long-term trend are adaptively balanced in multi-scale prediction, the prediction conflict problem is relieved, finally, physical constraints based on the electrochemical law and the internal resistance temperature characteristic are embedded in a loss function, it is ensured that the prediction result is accurate in numerical value and conforms to the physical law, and the prediction accuracy is improved. And generation of physically impossible solutions is avoided.

The invention relates to the technical field of cylindrical lithium ion battery production, and discloses a method for detecting insufficient welding of a cylindrical lithium ion battery, and the method comprises the following steps: S1, appearance inspection; s2, initial internal resistance benchmark test; s3, applying a short-time large-current pulse to the battery by using a pulse generator, simulating an actual load condition, and recording an instantaneous voltage drop and current response curve of the voltage of the battery end during the pulse period; s4, performing infrared thermal imaging identification and data processing; s5, learning the picture and establishing a model; s6, comparing the test picture and the data with the model; s7, dynamic internal resistance calculation; s8, cold solder joint query and sorting processing; and S9, retesting the electrical performance. According to the invention, the technologies of dynamic internal resistance measurement, nondestructive X-ray or ultrasonic detection, rate discharge test and the like are adopted, so that efficient, accurate and nondestructive detection of the cold solder joint of the cylindrical lithium ion battery is realized, and the production efficiency and the detection precision are remarkably 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.

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 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 the technical field of voltage acquisition, in particular to a battery string voltage acquisition system with a calibration system and a voltage acquisition method. Which comprises a multi-path voltage input end, a voltage signal adjusting end, a voltage follower, an analog-to-digital converter, a main control center and a communication circuit, and is characterized in that the voltage signal adjusting end comprises a dynamic voltage division network, consists of a digital potentiometer and a high-voltage operational amplifier, and dynamically adjusts the voltage division ratio to suppress common-mode voltage; the main control center is integrated with a multi-channel synchronous sampling calibration module and generates a global synchronous signal through the main MCU to control the ADC to perform synchronous acquisition; a temperature compensation module is arranged between the voltage follower and the ADC, and a digital temperature sensor is adopted to correct the gain drift of the operational amplifier in real time; the voltage division ratio of the dynamic voltage division network is dynamically adjusted according to the real-time common-mode voltage and a preset safety threshold value. According to the scheme, dynamic partial pressure adjustment, multi-channel self-calibration, nonlinear temperature compensation and a rapid protection mechanism can be integrated.

The invention relates to the technical field of power batteries, in particular to a power battery pack health state detection method and system. The power battery pack health state detection method comprises the following steps: acquiring open-circuit voltage change data and terminal voltage fluctuation data of a battery under micro current pulse excitation, recording a change curve of voltage along with time through high-precision time sequence acquisition equipment, and obtaining an initial data set of voltage dynamic response; acquiring magnetic field intensity distribution and magnetic field gradient change data of the surface of the battery in a weak alternating magnetic field, and acquiring spatial magnetic field information through a magnetic sensor array to obtain a three-dimensional distribution diagram reflecting magnetic field nonuniformity; through a three-dimensional distribution diagram of magnetic field intensity distribution and magnetic field gradient change, a defect detection algorithm is adopted to analyze a mapping relation between magnetic field non-uniformity and internal micro cracks, and correlation distribution of defect positions and internal resistance growth rates is determined.

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 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 an electric vehicle battery health assessment method and system based on multi-source data fusion, and relates to the technical field of new energy vehicle battery application, and the method comprises the steps: collecting a Nyquist impedance curve, terminal voltage relaxation time sequence data and equalization current time sequence data based on a constant current stage cut-off event of a battery pack of an electric vehicle; extracting an impedance form embedding vector corresponding to the Nyquist impedance curve based on an auto-encoder; performing wavelet packet decomposition on the terminal voltage relaxation time sequence data to obtain a corresponding multi-scale voltage relaxation energy feature vector; calculating a dynamic equalization imbalance index based on the terminal voltage relaxation time sequence data and the equalization current time sequence data; and inputting the impedance form embedded vector, the multi-scale voltage relaxation energy feature vector and the dynamic equilibrium imbalance index into a battery SOH prediction model to output an SOH estimated value of the battery pack. Therefore, the accuracy of the SOH evaluation result is improved through the joint modeling of the coupling characteristics of the multi-physical process of the battery.

A power conversion device includes a power conversion unit and a control unit. The power conversion unit includes a first semiconductor module. The first semiconductor module includes a plurality of power semiconductor elements. Some of the plurality of power semiconductor elements are detection target elements. The power conversion unit further includes a temperature sensor and a voltage sensor. The temperature sensor detects a surface temperature of the detection target element as a sensor position temperature. The voltage sensor detects an inter-terminal voltage of the detection target element. The control unit calculates a loss in the detection target element based on the sensor position temperature and the inter-terminal voltage, and estimates a maximum temperature based on the calculated loss.

The invention provides a power system voltage control method and device based on dynamic steady state collaborative correction, and the method comprises the steps: building an electromagnetic transient model for a preset voltage node in electromagnetic transient simulation software PSCAD, simplifying a power grid with the electrical distance to a target region exceeding a preset distance into a dynamic Thevenin equivalent model, initializing load flow calculation in the BPA to obtain a dynamic-steady state hybrid model; updating dynamic Thevenin equivalent parameters of the power system in real time according to the data of the phasor measurement unit of the power system, and synchronizing the dynamic Thevenin equivalent parameters to the BPA; based on the dynamic-steady state hybrid model, the transient overvoltage of the power system is suppressed within the time shorter than the first preset duration, and the steady state voltage of the power system is optimized within the time shorter than the second preset duration in combination with wide-area coordination control, so that the parameter bidirectional correction of the dynamic and steady state models is realized, and the head end voltage prediction error is reduced. And the voltage control robustness is improved.

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 discloses a lithium battery health assessment method based on an adaptive extended particle filter algorithm. The method comprises the following steps: constructing a second-order RC equivalent circuit model of a lithium battery; determining all parameters in the lithium battery second-order RC equivalent circuit model by using a recursive least square algorithm with a forgetting factor to obtain a determined lithium battery second-order RC equivalent circuit; estimating SOC parameters of the lithium battery at the moment by combining a particle filtering algorithm with an adaptive extended Kalman filtering algorithm; correcting the terminal voltage of the second-order RC equivalent circuit of the lithium battery at the moment to obtain the terminal voltage of the equivalent circuit; and based on the lithium battery second-order RC equivalent circuit and the corrected terminal voltage, estimating the SOE parameter of the lithium battery at the moment k by using a particle filtering algorithm in combination with an extended Kalman filtering algorithm. The SOC and the SOE of the lithium battery are accurately estimated, and the sampling efficiency and the estimation stability of particle filtering are improved. Cooperative joint estimation of SOC and SOE complements each other, and the accuracy of overall state estimation is further improved.

The invention relates to the technical field of electric vehicle charging, in particular to a flexible actor commentator fast charging method based on an electrothermal aging coupling model, and the method comprises the steps: building an electrothermal aging coupling model of a battery; acquiring the temperature, the terminal voltage and the terminal current of the load at the t moment; a relational expression of the state of charge and the open-circuit voltage of the battery is determined through formula fitting; respectively identifying an electric model parameter, a thermal model parameter and an aging model parameter by adopting a recursive least square method and a genetic algorithm; determining an optimal charging strategy by using a flexible actor commentator algorithm; carrying out simulation verification on the optimal charging strategy by using PyBaMM; according to the method, multiple factors influencing the charging safety of the vehicle lithium ion power battery are analyzed in a multi-dimensional mode, the electric heating aging coupling model considering aging is established, and the dynamic behavior of the battery in the charging process is accurately described and predicted; and the safety and robustness of the flexible actor commentator fast charging method based on the electrothermal aging coupling model are improved.

The dual inverter system includes a first inverter connected to one end of a stator coil of the motor and a second inverter connected to the other end. The controller can execute a dual mode in which the motor is driven by two inverters and a single mode in which the motor is driven by only one inverter. The controller closes the upper SW elements of all of the first and second inverters, opens all the lower SW elements, and closes the connection switch when the voltage at the DC end of the first inverter exceeds the threshold voltage. Alternatively, the controllers close all the lower SW elements of the first and second inverters to open all the upper SW elements and close the connection switches.

A soft start circuit of a synchronous boost DC-DC converter is used for restraining current overshoot and voltage overshoot of the synchronous boost DC-DC converter in the starting process and comprises a first soft start module and a second soft start module. The first soft start module belongs to system open-loop work, and suppresses a channel length modulation effect through the same drain end voltage of a clamping matching tube and a power upper tube, so as to obtain a precisely controlled charging current. A second soft start module belongs to system closed-loop work, and the method comprises the following steps: in the first stage, VOUT partial voltage is synchronized; in the second stage, constant-current charging is carried out; and in the third stage, the error amplifier vref rises gently through preset voltage RC charging. According to the invention, current overshoot and voltage overshoot are effectively suppressed through two soft start stages, and the starting circuit has a wide application prospect in the field of starting circuits.

A sodium ion battery parameter identification method based on a third-order equivalent circuit model comprises the following steps: applying pulse current to a sodium ion battery to carry out charge and discharge test, and collecting terminal voltage, current and capacity data in discharge and shelving stages; constructing a three-order equivalent circuit model formed by connecting three groups of RC parallel modules and ohmic resistors in series, and establishing a terminal voltage dynamic equation; ohmic resistance is calculated by using voltage break variables at the beginning and the end of a discharge pulse, a polarization process is divided into a zero-state response stage and a zero-input response stage, and polarization voltage nonlinear dynamic equations of all RC modules are deduced respectively. Based on an LM algorithm, a terminal voltage nonlinear function is constructed, polarization resistance and capacitance parameters are iteratively optimized, the error sum of squares of model output and actual data is minimized, and finally high-precision parameter identification is achieved. According to the method, the performance prediction precision of the sodium battery is improved, and reliable data support is provided for related applications.

A method and system for mitigating low-frequency oscillations of a power system network (PSN). The method includes receiving multiple data sets from the PSN, comprising values of terminal voltage, a real power, and a reactive power. The method further employs the multiple data sets to a fuzzy c-means clustering technique, a deep learning technique and a whale optimization algorithm to generate a pair of parameter values for a power system stabilizer controlling a steady-state of the power system network.

The present application relates to the technical field of power supply circuits, and discloses a current backflow prevention circuit, a dual power supply current backflow prevention circuit, and an electronic device. The current backflow prevention circuit includes: a conduction control module, a voltage hysteresis comparison module; the voltage hysteresis comparison module, the two input ends of which are respectively connected to the first end and the second end of the conduction control module, and the output end is connected to the third end of the conduction control module; the voltage hysteresis comparison module is used to compare the voltage of the first end and the voltage of the second end of the conduction control module after connecting to the power supply, and output a corresponding level signal according to the comparison result; the conduction control module is arranged in the power supply path between the power supply and the load, and is used to conduct the first end and the second end when receiving a level signal of a first state, so as to conduct the power supply path; when receiving a level signal of a second state, the first end and the second end are turned off to turn off the power supply path. The present application can prevent the load from backflowing the current of the power supply.

The invention discloses a low-complexity, high-precision and high-robustness battery state of charge estimation method and system, relates to the field of battery state of charge estimation, and aims to solve the problems of high cost, to-be-improved precision, poor robustness and the like of the existing battery state of charge estimation technology. According to the technical key points, firstly, a second-order equivalent circuit model is established through an off-line experiment, an off-line parameter identification means is adopted, the magnitude difference between to-be-identified model parameters and fitting voltage is reduced, and the fitting precision is improved; and fitting the relationship between the model parameters obtained by the off-line parameter identification and the SOC, and fitting an OCV-SOC relationship curve by using the third-order Gaussian after the fitting points are added. A battery end voltage and a current value measured by a sensor in real time are input into an ECM model for calculating an OCV value, sliding window filtering of variable weight distribution is carried out on the calculated OCV value to smooth a curve and reduce errors, a filtered result is fitted through an OCV-SOC relation curve to obtain an SOC value, and the SOC value is used as an observation value of subsequent Kalman filtering. A predicted value is calculated by using an ampere-hour integral method, and a current model parameter is fitted by using the priori value in combination with a relationship between the model parameter and the SOC. And finally, Kalman filtering is carried out on the two to obtain a posterior SOC value.

The present invention provides a method for cooperatively suppressing a common-mode voltage and current harmonics of coupled three-level inverters, comprising: based on amplitude and phase angle of reference voltage vector, determining sector and region in which the reference voltage vector is located; selecting four basic voltage vectors with low common-mode voltage amplitudes to synthesize the reference voltage vector; writing volt-second balance equation based on selected basic voltage vectors, and calculating duty cycles thereof; and based on sector and region in which reference voltage vector is located, set value of voltage difference across capacitors on direct current side, and actual value of voltage difference across capacitors on direct current side, (1) updating duty cycles of basic voltage vectors, to realize separate control of capacitor voltage; and (2) designing and converting switching sequence into PWM drive signal of power switch, to control the coupled three-level inverter to operate.

The present disclosure relates to the technical field of high-voltage direct-current power transmission, and provides a surge protection method and apparatus for a fully controlled valve in a controllable line-commuted converter, a computer device, a computer readable storage medium and a computer program product. The method comprises: monitoring a terminal voltage of a fully controlled valve in real time; when a wave turn-off voltage per cycle of a primary IGBT valve exceeds an operating voltage of a surge arrester, subtracting 1 from an available action count of the surge arrester of the primary IGBT valve, and when a wave turn-off voltage per cycle of a secondary IGBT valve exceeds an operating voltage of a surge arrester, subtracting 1 from an available action count of the surge arrester of the secondary IGBT valve; when the available action count of the surge arrester of the primary IGBT valve is less than a second preset count, or when the available action count of the surge arrester of the secondary IGBT valve is less than the second preset count, controlling a converter to return to a power grid line-commutation operation mode; and when the available action count of the surge arrester of the primary IGBT valve is greater than a third preset count, or when the available count of the surge arrester of the secondary IGBT valve is greater than the third preset count, controlling the converter to enter a controllable line-commutation operation mode.

A calibration circuit includes a replica transconductance stage that is a replica of a transconductance amplifier in a serializer / deserializer (SerDes) interface and a replica transimpedance stage that is a replica of a transimpedance amplifier in the SerDes interface. The replica transimpedance stage has an input coupled to its output. A comparison circuit is configured to generate a difference signal representative of a difference between voltage levels of an input signal received at an input of the replica transconductance stage and an output signal representative of voltage level at the output of the replica transimpedance stage. Replica feedback resistors are configured to couple an output of the replica transimpedance stage to the input of the replica transconductance stage. The difference signal can be used to select resistance values provided by the replica feedback resistors and by corresponding gain control resistors in the transimpedance amplifier in the SerDes interface.

The embodiment of the invention provides a state of charge calibration method and device, a medium, a product, a system and equipment. The state of charge calibration method comprises the following steps: acquiring first measurement terminal voltages corresponding to a battery at a plurality of sampling time points, and first model terminal voltages corresponding to each sampling time point; determining a first voltage error corresponding to the sampling time point based on the first measurement terminal voltage corresponding to the sampling time point and the corresponding first model terminal voltage; and under the condition that the first voltage errors corresponding to the plurality of sampling time points are in a monotonous change trend, the estimated state of charge of the battery is calibrated. Therefore, whether the current state of charge of the battery needs to be calibrated or not can be more accurately identified, the correction opportunity of the state of charge is increased, the correction effect of the SOC is improved, and the problem of overvoltage or undervoltage of the battery caused by deviation of the state of charge is reduced.

The invention provides a residual value evaluation method, device, equipment and medium for a decommissioned battery, and the method comprises the steps: carrying out the terminal voltage detection of the decommissioned battery when the appearance of the decommissioned battery is judged to reach a detection standard through the three-dimensional point cloud data of the decommissioned battery, so as to determine the terminal voltage of the decommissioned battery, performing an electrochemical impedance spectroscopy test on the decommissioned battery to determine a plurality of characteristic parameters of the decommissioned battery; performing dimension reduction processing on the terminal voltage and the plurality of characteristic parameters to obtain a target characteristic data set of the retired battery; and inputting the target feature data set into a pre-trained battery residual value score prediction model to determine a residual value evaluation score of the retired battery. Through the method and the device, the battery residual value evaluation score can be quickly and accurately determined so as to evaluate the health state of the retired battery.

The invention discloses an active power distribution network state estimation method and system based on GCN and Transform fusion. The method comprises the following steps: firstly, establishing a power distribution network graph model, and abstracting nodes in a power distribution network and lines connecting the nodes into a graph structure; secondly, constructing a photovoltaic power station physical model, and describing a relation between terminal voltage and current by adopting a five-parameter model; then constructing a state prediction equation based on Transform, and predicting a system state at a future moment by using historical measurement data; a GCN-based measurement equation is constructed, and relation modeling between the node state and the neighbor node state is realized through graph convolution operation; and finally, performing feature fusion on state prediction and measurement results by using a deep neural network, and outputting more accurate state estimation of the power distribution network. According to the method, the GCN is used for conversion of the measurement data, and spatial dependence between nodes and neighbors is fully mined; and the Transform is responsible for state prediction of long sequence time dependence, so that the prediction precision and robustness of the model are effectively improved.