515 results about "Battery degradation" patented technology

The invention provides a health state monitoring and service life prediction method and system for an energy storage battery pack, relates to the field of energy storage batteries, and solves the problems that prediction models in the prior art mostly adopt a single machine learning algorithm and lack adaptability to a battery degradation mechanism and actual working conditions, and the prediction efficiency is poor. And the battery health state evaluation and residual life prediction precision is low. The method comprises the following steps: preprocessing an original data set, analyzing a multi-dimensional health feature vector, and constructing a health feature matrix; constructing a health state evaluation model based on the improved CNN-LSTM hybrid model and by fusing battery degradation physical mechanism constraints; inputting the health feature matrix into a health state evaluation model, and outputting a current SOH value; and predicting residual life information based on the current SOH value and the load fluctuation correction coefficient. The method is used in the process of health state evaluation and residual life prediction of the energy storage battery pack.

The invention provides a battery health state evaluation method, device and equipment and a medium, and the method comprises the steps: collecting multi-source parameters of a power battery in a charge-discharge cycle process, the multi-source parameters comprising voltage, current, temperature and electrochemical impedance spectroscopy; performing feature engineering processing on the multi-source parameters based on a battery aging mechanism, and extracting key feature parameters; the key characteristic parameters are input into a multi-physics field coupling model to simulate thermoelectric coupling behaviors of the battery under a dynamic load, lithium ion concentration distribution and temperature distribution in the battery are obtained, and the multi-physics field coupling model is used for representing a coupling relation between an electrochemical field and a thermal field of the battery; and fusing the key characteristic parameters with data output by the multi-physics field coupling model, and determining a health state evaluation result of the power battery. According to the invention, the accuracy of power battery health state evaluation is improved.

The invention discloses an electric vehicle vehicle-mounted battery health degree prediction method and system based on a neural network, and relates to the technical field of battery health degree prediction, and the method comprises the steps: collecting battery multi-source heterogeneous data, extracting key health factors through preprocessing, room temperature correction and staged feature engineering, and introducing a time window embedding strategy to construct a time sequence sample; a multi-module U-BiLSTM hybrid network is adopted as a core prediction model, and Bayesian optimization and an Adam optimizer are combined to complete hyper-parameter optimization and model training; and a prediction result is optimized through digital twinning correction, a physical compensation mechanism and residual service life label normalization processing. The method solves the problems that a traditional method is insufficient in deep feature extraction, low in time sequence information utilization rate and poor in prediction precision under complex working conditions, the aging state and the remaining service life of the battery can be accurately reflected, and reliable technical support is provided for safety management, operation and maintenance optimization and service life evaluation of the battery of the electric vehicle.

The invention belongs to the technical field of intelligent traffic and new energy power supply, particularly relates to a traffic equipment solar power supply fault diagnosis system, and aims to solve the problems that a solar power supply system is not timely in fault diagnosis, low in precision and difficult to distinguish instantaneous interference and continuous faults. The system collects multi-source data through an environment sensing and electrical parameter monitoring module, generates a power deviation sequence and extracts time sequence characteristics by combining dynamic expected power modeling with actual output comparison; the fault identification module adopts a multi-level logic discrimination and 12-hour continuous verification mechanism, accurately identifies photovoltaic panel pollution, storage battery aging, poor line contact and controller faults, and distinguishes instantaneous interference; and the decision alarm module generates graded alarms according to fault types and grades, and realizes accurate positioning and operation and maintenance scheduling in linkage with geographic information. The system also has the functions of internal resistance pulse detection, dual-channel redundancy sampling, adaptive threshold adjustment and model self-learning, and the diagnosis accuracy and the operation and maintenance efficiency are significantly improved.

The invention provides a lithium ion battery health state lightweight detection method based on a physical information neural network, and the method comprises the steps: collecting the time, voltage, current, temperature and state-of-charge data of a battery in a takeoff and landing stage discharge process, processing the data into takeoff and landing stage discharge time sequence data, and carrying out the detection of the lithium ion battery health state based on the takeoff and landing stage discharge time sequence data. The method comprises the following steps: designing characteristic factors related to battery aging, screening the characteristic factors by utilizing a Pearson's correlation coefficient and a grey relational degree algorithm to obtain optimal characteristic sequence data, inputting the optimal characteristic sequence data into a physical information neural network model constructed by two serially connected neural networks for training, and in the training process, obtaining the optimal characteristic sequence data. And performing hyper-parameter tuning on the two neural networks by adopting a Bayesian optimization algorithm, then performing fine tuning on the second neural network by adopting a hierarchical transfer learning strategy, and finally applying the trained physical information neural network model to battery health state detection. The method improves the quality of feature data, reduces the calculation complexity of features and models, and achieves the accuracy and reliability of the detection of the health state of the battery under the airborne condition.

The invention is suitable for the technical field of sodium-ion batteries, and provides a sodium-ion battery health state evaluation method based on multi-modal neural network and physical prior fusion, and the method comprises the steps: carrying out the preprocessing of collected multi-modal time sequence data and operation context information, and carrying out the feature extraction of the preprocessed multi-modal data; a physical prior constraint based on a battery electrochemical mechanism is introduced in the fusion process of the extracted feature vectors, and fused multi-modal feature representation is obtained; the method comprises the following steps: constructing a continuous time dynamic model of sodium ion battery health state degradation, and carrying out time sequence dynamic modeling to obtain a hidden state sequence representing a battery degradation state; and based on the hidden state sequence, outputting a health state point estimation value, a physical agent parameter estimation value and an uncertainty quantification result, and evaluating the health state of the sodium ion battery. Accurate description of the battery degradation process is achieved through continuous time dynamic modeling, and intelligent health state evaluation of the sodium-ion battery in the full life cycle is achieved.

The invention discloses an energy storage system intelligent energy management control method and system, and relates to the technical field of energy storage system intelligent energy management control, and the method comprises the steps: obtaining an energy storage unit operation state parameter and an environment disturbance factor matrix through a multi-source data collection device, and carrying out the data preprocessing; and constructing a long-short-term memory neural network model, dynamically adjusting a prediction time window of the model and optimizing a target function weight coefficient based on the load predicted by the model, and determining a charging and discharging control instruction. And updating and optimizing the target function weight coefficient and the charging and discharging strategy library through a reinforcement learning algorithm to realize self-adaptive optimization adjustment of the charging and discharging strategy. According to the method, high-precision energy management and intelligent optimization control of the energy storage system in a complex environment are realized. The load prediction accuracy and the energy utilization rate of the system are improved, the aging rate of the battery is reduced, the service life of the battery is prolonged, the adjusting capacity of the energy storage system is improved, and the overall stability of the energy storage system in dynamic change is enhanced.

The invention discloses a lithium ion battery health state prediction method based on a double-branch feature fusion network, and the method comprises the steps: employing a unified feature analysis method to extract a plurality of health indexes based on NASA and CALCE battery data sets, and employing a Pearson correlation analysis method to select M health indexes highly related to the battery health state; based on the selected M health indexes, performing denoising processing on the health index data by adopting a variational mode decomposition method to obtain denoised health index data; constructing a dual-branch feature fusion network, and training by using the de-noised health index data to obtain a trained dual-branch feature fusion network; and utilizing the trained double-branch feature fusion network to predict a battery health state prediction value of the next round. According to the invention, by effectively integrating the multi-scale battery degradation characteristics, the accuracy, robustness and prediction precision of battery health state prediction are improved, and the adaptability and generalization ability of the network are enhanced.

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

The invention relates to the technical field of energy storage battery management, and discloses a capacity recovery method based on the full life cycle of a lithium ion battery. According to the method, the uncertainty of power grid demand and environment temperature is modeled through a scene tree generation algorithm, a low-sensitivity temperature region is identified through sensitivity analysis to generate a robust temperature parameter candidate set, and a recovery time window is identified based on a battery aging characteristic prediction sequence. Constructing a dual-objective optimization problem of power grid auxiliary service income and battery full life cycle value, solving a conditional optimal decision scheme under each scene through a dynamic programming algorithm, selecting a decision scheme with a maximum worst condition target value by using a robust optimization criterion, extracting an execution instruction, outputting the execution instruction to a control system, and performing power grid auxiliary service income and battery full life cycle value optimization. And the decision scheme is adjusted in real time through a rolling optimization mechanism. According to the method, the robustness of balancing economic benefits and battery health in an uncertain environment by a capacity recovery decision is improved.

The invention provides a dynamic impedance matching charger based on battery aging, a method, electronic equipment and a medium. The charger comprises a master control unit, and a battery state monitoring module, an aging grade evaluation module, a dynamic charging control module, a battery cell balancing module, an optimization control module and an anti-interference module which are connected with the master control unit to cooperatively realize charging closed-loop control. The battery state monitoring module injects a preset frequency alternating current signal and outputs an impedance parameter, a charge state and a temperature parameter; the aging grade evaluation module outputs a battery aging grade in combination with a preset model; the dynamic charging control module is matched with an adaptive charging mode instruction; the battery cell balancing module calculates the battery cell impedance difference, and balancing operation is executed if the battery cell impedance difference reaches the standard; the optimization control module generates an optimized current instruction through a preset algorithm according to the related parameters, and superposes the optimized current instruction to the reference current; the anti-interference module improves the signal-to-noise ratio of collected signals to a preset standard through isolation and shielding design.

The invention relates to the technical field of power battery life prediction, and discloses a power battery residual life prediction method based on sensor fusion. The method comprises the steps of collecting and processing multi-dimensional electrical sensor signals of historical operation of a battery, and generating a voltage platform change characteristic spectrum, a current stress characteristic spectrum and an internal resistance component evolution spectrum. And inputting the maps into a feature space alignment network for space-time dimension registration and feature cross validation to generate a unified electrochemical state panoramic feature map. Based on the atlas, multi-stage battery life state backtracking and deduction are realized by constructing a dynamic attenuation trajectory tree, and a battery aging state vector set is output. After working condition constraint correction, the set drives a residual life interval prediction module, boundary estimation and probability density propagation calculation are fused, and finally a confidence interval of the residual cycle life and probability density distribution of the confidence interval are generated. According to the method, the precision of multi-source information fusion and the reliability of life prediction are improved.

The invention discloses a lithium battery residual life prediction method and system based on an electrochemical model, and relates to the field of lithium ion battery life prediction, and the method comprises the steps: constructing an initial electrochemical model according to battery powder attribute parameters, internal structure parameters and empirical parameters of a lithium ion battery; the method comprises the following steps: respectively taking new lithium ion batteries of the same model and old batteries at different aging stages as experimental objects, obtaining respective corresponding battery aging characteristic data through experimental measurement and electrochemical model simulation, and then taking the minimum value of the difference between the measurement data and the simulation data as an optimization target; empirical parameters and battery recession parameters in the initial electrochemical model are identified through a simulated annealing intelligent optimization algorithm; and according to the identified empirical parameters and the battery recession parameters, constructing a lithium ion battery full-life-cycle electrochemical model, performing working condition analog simulation on the lithium ion battery full-life-cycle electrochemical model, and predicting the remaining life of the battery. The battery is modeled from the internal electrochemical reaction level of the battery, and the prediction precision of the residual life of the battery is improved.

The invention discloses a battery remaining service life prediction method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining an impedance data set through the electrochemical impedance spectroscopy test of a battery in multiple aging states, synchronously constructing an equivalent circuit model of the battery, and carrying out the modeling in combination with relaxation time distribution; extracting mechanism features from the electrochemical impedance spectroscopy to form a mechanism feature data set; and after fusing the impedance data set and the mechanism feature data set, screening features strongly related to battery performance through correlation analysis to obtain a target feature data set, and inputting the target feature data set into a preset prediction model to output a prediction result of the remaining service life of the battery. By means of combination of electrochemical impedance spectroscopy testing and relaxation time distribution modeling, conversion from original impedance data to mechanism characteristics is achieved, the characteristics are directly associated with battery aging essence, dependence on pre-calibration is eliminated, and the problem that an empirical model is insufficient in adaptability under complex working conditions is solved; and the model input quality is optimized through feature fusion and correlation screening.

The invention relates to the technical field of electric vehicle battery health state monitoring and prediction, in particular to an electric vehicle battery health state prediction method and system. The method comprises the following steps of: performing low-frequency / high-frequency data acquisition switching on a vehicle in a driving process by setting a high-energy event triggering condition to obtain a transient electric response sequence; obtaining and constructing a vibration transfer function according to battery pack installation data to obtain a vibration transfer coefficient matrix; extracting a vibration time sequence data set from the transient electric response sequence, and performing battery stress calculation by combining the vibration transfer coefficient matrix to obtain battery stress time sequence data; analyzing the mechanical load of the battery according to the stress time sequence data of the battery to obtain a mechanical load diagram of the battery; and carrying out load damage weighted quantization on the mechanical load diagram of the battery, and carrying out damage accumulation to obtain an accumulated damage count value. According to the invention, the vehicle body vibration is converted into the accumulated stress damage of the battery component, so that the accuracy of monitoring the aging difference of the battery is improved.

The invention relates to the technical field of battery management, in particular to an intelligent charging adjustment method and system based on dynamic monitoring, and the method comprises the steps: determining a tested energy storage battery, determining a target battery based on a charging adjustment instruction, carrying out the feature extraction of a plurality of historical charging data and the previous full capacitance, and obtaining a charging emergency coefficient and a battery aging coefficient; a humidity sensor and a temperature sensor are used for obtaining current humidity and current temperature respectively, particle swarm simulation is carried out on a charging emergency coefficient, a battery aging coefficient, a constant current curve set, a constant voltage curve set, a switching curve set, the current humidity and the current temperature, and target constant current, target constant voltage and a target switching threshold value are obtained; and if the target battery voltage is greater than or equal to the target switching threshold, performing charging adjustment on the target battery according to the target constant voltage and the target switching threshold to obtain a final battery, and completing intelligent charging adjustment. The safety and stability of the charging process can be improved, and the service life of the battery is prolonged.

The invention provides a capacity attenuation trend prediction method of a low-temperature lithium battery, and relates to the technical field of battery health management and prediction. The capacity fading trend prediction method of the low-temperature lithium battery specifically comprises the following steps of S1, initial data acquisition, S2, feature extraction, S3, hybrid prediction model construction, S4, model training and offline verification, S5, online deployment and dynamic correction, and S6, SOH evaluation and early warning output. A hybrid prediction model is constructed by fusing an electrochemical mechanism and deep learning, so that high-precision prediction and full-life-cycle intelligent health management of lithium battery capacity fading in a low-temperature environment are realized, and the problems of accelerated battery aging and prominent safety risk in an alpine region are effectively solved; early warning and intelligent management of the health state of the battery can be realized, and the service life of the battery under low-temperature application can be prolonged.

The invention discloses a power control method and device for an industrial forklift charger, and the method comprises the steps: obtaining industrial forklift charging demand data and LLC resonant topology characteristic parameters, and constructing an LLC resonant converter; on the basis of the working frequency range and the power density requirement, through a probability learning algorithm and a random agent model, a random agent model for multi-objective optimization of magnetic element parameters is formed, and a high-frequency low-loss magnetic element is obtained; a high-frequency low-loss magnetic element is integrated into an LLC resonant converter, a random neural control barrier function algorithm is realized, and dynamic power control is performed on a charging process; based on the real-time data, a data-driven hybrid prediction control strategy is realized, and a charging parameter optimization instruction is generated; and in combination with the battery charge and discharge characteristic data and the battery aging state evaluation model, battery state evaluation and charging parameter adaptive adjustment are realized. The problems that a traditional charger is insufficient in control precision, low in efficiency, short in battery life and the like in a complex industrial environment are solved.

The invention belongs to the technical field of battery health state monitoring, and relates to an unmanned aerial vehicle battery health state on-line prediction method comprising the following steps: exciting a quantum dot tracer agent in a battery monitoring area to obtain an initial fluorescence spectrum, and carrying out real-time comparison to identify fluorescence quenching caused by gas production and generate a preliminary aging signal; further analyzing the spectrum drift characteristics so as to analyze lithium aging and pure gas production; triggering ultrasonic detection based on a diagnosis result, and extracting time-frequency characteristics of acoustic echoes to construct quantitative acoustic fingerprints; and finally, through cross validation of optical and acoustic information, generating a comprehensive health status report containing a risk level and an aging type. According to the method, multi-dimensional on-line diagnosis and early warning on the early aging stage of the battery are realized, and the problems of hysteresis quality, difficulty in positioning and inaccurate risk assessment caused by dependence on macroscopic electrical parameters in a traditional method are solved.

The invention discloses an energy storage power station-oriented battery aging sensing self-adaptive charging and discharging control method and system, and relates to the technical field of electric energy storage. The energy storage power station battery aging sensing self-adaptive charging and discharging control method comprises the following steps: S1, collecting battery working condition physical data, and preprocessing the battery working condition physical data; s2, acquiring a health attenuation factor, evaluating the health state of the battery, judging the health level of the battery based on an evaluation result, and generating a health level label; s3, evaluating the charging and discharging regulation and control capability of each battery, calculating a target charging and discharging current, generating a current instruction set, and implementing a current regulation task; and S4, evaluating the matching degree of the current adjustment strategy and the battery state after each round of current adjustment, and dynamically adjusting the target current. The problems that a traditional energy storage power station lacks a real-time identification and differentiation control mechanism for the aging state of the battery, and system capacity attenuation and thermal potential safety hazards are likely to be caused are solved.

The invention provides a lithium battery residual life prediction method based on multi-frequency decomposition and a hybrid neural network, and relates to the technical field of lithium battery residual life prediction. According to the lithium battery residual life prediction method, a component decomposition mechanism is introduced, and proper prediction models are designed for different frequency components, so that local fluctuation and long-term trend in a battery degradation process are effectively captured, and the accuracy and robustness of residual life prediction are remarkably improved; proper prediction models are respectively designed according to complexity differences of different components, so that redundant calculation caused by uniformly using complex models on all components is avoided, and the balance between prediction precision and calculation efficiency is realized; based on a hyper-parameter optimization strategy of hierarchical reinforcement learning, an optimal parameter combination can be automatically and efficiently searched according to different battery data characteristics, and the defects that a traditional optimization method is high in calculation cost and poor in adaptability are overcome.

The present disclosure relates to a system for predicting a life of a battery. The system may include a training data generation device configured to generate first data comprising life data of a reference battery and profile data for each battery degradation mode of the reference battery. The system may further include a prediction model generation device configured to generate, based on the first data, one or more life prediction models to predict profile data for each battery degradation mode with initial life data of a target battery as input. The system may also include a life prediction device configured to predict a life of the target battery based on second data comprising profile data for each battery degradation mode predicted by the one or more life prediction models.

The invention discloses a distributed energy storage system cooperative control method based on data fusion, and belongs to the field of power system energy storage control. The edge layer performs dimensionality reduction on multi-source data through kernel principal component analysis, the cloud constructs a space-time correlation model by combining a graph neural network with a long-short-term memory network, and data privacy is guaranteed by means of federal learning distributed updating. An upper layer model prediction control framework is combined with an improved whale algorithm to solve multi-target optimization of system economy, energy storage life and power grid stability; the lower-layer power type energy storage adopts self-adaptive droop control, and the energy type energy storage model predicts, controls and quantifies the degradation cost of the battery. When communication is interrupted, the analytic hierarchy process and the consistency algorithm cooperate to disperse power distribution; and when the equipment fails, convolutional neural network diagnosis is combined with alliance chain redundancy switching. Multi-source deep correlation, characteristic differentiation regulation and control and high robustness are realized, the energy storage efficiency is improved, the service life of equipment is prolonged, and the method is suitable for micro-grids, smart grids and other scenes.

The invention relates to the technical field of power battery data analysis, and discloses a multi-source data fusion analysis method which comprises the following steps: S1, respectively extracting multi-dimensional health factors reflecting a battery degradation state from a voltage curve, a temperature curve and an incremental capacity curve of a battery; the health factors comprise a voltage sampling value and a voltage curve slope extracted from a voltage curve, a differential temperature characteristic parameter extracted from a temperature curve, and a peak value characteristic extracted from an incremental capacity curve; s2, respectively inputting the health factors into at least two different types of basic prediction models to obtain a preliminary health state estimation result of each basic prediction model; and S3, fusing the preliminary health state estimation results through an integrated learning model to obtain a final estimation result of the battery health state. According to the method, the limitation that a traditional method depends on a single data source is overcome, and the complex degradation state of the battery can be represented more comprehensively, more stably and more accurately.

The invention provides a method suitable for rapidly evaluating the aging state of a large-scale energy storage power station battery, and belongs to the technical field of battery state evaluation. According to the method, data acquisition is carried out during battery standing, two subsequent interval voltages of standing initial voltage are recorded, equivalent capacity loss instantaneous values at different moments are calculated in combination with a calibration proportionality coefficient and a working condition correction factor, a voltage relaxation integral area is obtained through calculation, and then a health state assessment result and confidence are obtained; the technical means of synchronously collecting the temperature change rate and the internal resistance change value, dynamically allocating weight fusion calculation, and performing evaluation in parallel in the whole station to generate an aging state distribution diagram and an abnormal alarm are adopted, and the method is adaptive to an existing energy storage power station monitoring system, does not need additional transformation, avoids voltage fluctuation to obtain high-quality data, comprehensively represents the battery polarization state, and improves the quality of the battery. The problems that large-scale energy storage power station battery evaluation is low in efficiency, insufficient in precision and lack of a global view angle are solved.

The invention provides an optimization method and system of a distributed battery management system. The method comprises the following steps: acquiring battery cell parameters, scene parameters and historical charging and discharging data of a battery cell, and evaluating a battery aging stage; calling a matched battery cell state calculation algorithm according to the scene parameters and the battery aging stage; calculating battery cell state information of the battery cell according to the battery cell parameters, the scene parameters and a battery cell state calculation algorithm; according to the cell state information, the scene parameters and the battery aging stage, dynamically adjusting an equalization threshold and an equalization rate, and executing an equalization strategy to obtain equalization state information; periodically uploading the operation data to a cloud server, receiving an algorithm update package, and updating an adaptive algorithm library; and calculating the overall state information of the battery pack according to the cell state information and the equilibrium state information. The calculation precision of the battery cell state can be effectively improved, the cycle life of the battery is prolonged, and the use safety of the battery is improved.

The invention discloses a life expectancy estimation method and system for a hybrid energy storage system. The hybrid energy storage system is composed of an aqueous hydrogen ion battery and a lithium iron phosphate battery. The method comprises the following steps: defining basic parameters and operation boundary conditions of a single battery, and constructing a sub-battery aging sub-model based on two battery aging paths; operating data are collected through a mixed working condition acceleration test, differential temperature correction coefficients are calculated, and an interaction influence factor library is formed through quantification; adopting a BiLSTM neural network to complement the full life cycle data and optimize the sub-model; and integrating the multi-dimensional correction factors to calculate the corrected service life of the single battery, determining the expected service life of the system in combination with a preset judgment rule, and realizing model iterative optimization through actual operation data comparison. The system comprises a parameter defining and model building module, a data collecting and processing module, a data complementing and model optimizing module, a service life estimating module and an iterative optimizing module. According to the invention, the accuracy and adaptability of life expectancy estimation are improved, and large-scale application of the hybrid energy storage system is supported.

The invention relates to a lithium ion battery state-of-health estimation method based on multi-scale Gream matrix entropy. The method comprises the following steps: acquiring a voltage signal and a current signal of a target lithium ion battery; converting the voltage signal and the current signal into a two-dimensional image by using a Grubrum matrix, and performing multi-scale decomposition on the two-dimensional image to obtain images of different scales; the Shannon entropy is adopted to quantify the images of different scales, health feature information is obtained, and the health feature information comprises a voltage entropy feature and a current entropy feature; the health feature information is input into an MLP model, the SOH estimated value of the target lithium ion battery is obtained, and the MLP model is obtained through training of an aging data set of the battery. According to the invention, in a battery aging cycle process, health features highly related to SOH can be extracted only through current and voltage signals, and the aging degree of the battery is objectively reflected.

The invention relates to a direct current side coupling type super capacitor-battery hybrid energy storage system. According to the energy storage system, a super-capacitor module and a battery module are directly connected on the direct current side through a bidirectional DC / DC converter, the super-capacitor module is connected with a power grid through a current source type converter, the battery module is connected with the power grid through a voltage source type converter, and a modularized and extensible topological structure is formed. The system has multiple differentiated working modes, cooperative efficient operation of the super-capacitor and the battery can be achieved according to working conditions, high-frequency / short-time power is preferentially borne by the super-capacitor, and the battery is responsible for steady-state energy adjustment. And meanwhile, by virtue of the boosting capability of the current source type converter, the super capacitor can support the power grid in a short time and strong manner in a deep discharging manner. And the battery module charges the super capacitor module through the bidirectional DC / DC converter, so that the influence of frequent charging of the super capacitor on the grid-side electric energy quality is reduced. The method can effectively reduce the aging loss of the battery, improves the energy storage utilization efficiency and the power grid friendliness, assists the new energy consumption and the safe and stable operation of the power grid, and has economic and social benefits.

The invention discloses a battery aging test method and system for optimizing a charging strategy of an electric vehicle charger, and the method comprises the steps: collecting real driving information, constructing a load spectrum, defining an accelerated aging load function, and enabling a battery to generate equivalent cumulative aging damage within a preset time, and controlling the testing device to accelerate aging of the battery to a target state, testing the battery in the state by using multiple charging strategies, recording parameters, and optimizing the charging strategies after analysis and evaluation. The system comprises a load simulation module, a charging module, a data acquisition module, a control module and a processing module, and can realize load output, multi-strategy charging, parameter acquisition and data analysis optimization. According to the scheme, the data of the batteries in different aging states can be quickly obtained, the test period is shortened, the charging strategy research and development efficiency and accuracy are improved, the battery safety is guaranteed, and support is provided for efficient use and management of the batteries of the electric vehicles.