917 results about "Battery degradation" patented technology

The invention relates to the field of battery state evaluation, in particular to a new energy automobile battery health state evaluation method and system. The method comprises the following steps: acquiring a vehicle battery multi-mode state monitoring parameter and a historical vehicle endurance mileage log; performing battery state dynamic evolution on the vehicle battery multi-mode state monitoring parameters to obtain battery multi-mode state evolution characteristics; carrying out multi-time-point maximum endurance calculation on the historical vehicle endurance mileage log, carrying out time sequence trend evolution, and constructing a vehicle endurance evolution trend chart; performing nonlinear attenuation trend analysis on the vehicle endurance evolution trend graph, and performing battery aging mechanism evolution analysis so as to construct a battery state aging evolution knowledge graph; according to the battery state aging evolution knowledge graph, environment change influence analysis is carried out on the vehicle endurance evolution trend graph, dynamic vehicle state modeling is carried out, and a dynamic vehicle twinborn model is constructed. According to the invention, efficient and accurate battery health state evaluation is realized.

The invention discloses a multi-user energy storage power station charging and discharging control method and system based on electricity price, and the method comprises the steps: arranging a monitoring terminal at energy storage equipment, and collecting the load data of each energy storage equipment; carrying out data preprocessing on the collected load data; constructing a cascade neural network prediction model, training the model by using the preprocessed load data, and generating a regional total load demand prediction value in a future time period; designing a deep reinforcement learning model DRL by using the regional total load demand predicted value to obtain a preliminary power adjustment amount; designing a target function considering the battery aging cost by using the initial power adjustment amount, and determining an initial value range of a coefficient in the target function of the optimization model according to a constraint condition; and a multi-objective optimization problem is considered, a Pareto optimal solution set is obtained based on the initial value range, and a final power distribution scheme is determined. The method can accurately predict the load, optimize the charging and discharging strategy, reduce the power consumption and operation cost, balance the service life of the battery and the economic benefit, and improve the intelligent and efficient level of the energy storage power station.

The invention relates to the technical field of motor train unit battery pack evaluation, and provides a motor train unit battery health state evaluation and life prediction method and system, and the method comprises the steps: building a database through voltage fluctuation and temperature data in a floating charge state, and analyzing a correlation mechanism between target parameters, such as a battery capacity retention ratio and active lithium stock, and internal chemical substance loss; respectively extracting driving behavior characteristics, temperature parameters and electrochemical impedance spectroscopy by adopting second-level, minute-level and hour-level time granularity, and aligning multi-frequency data by utilizing a dynamic time warping algorithm; constructing a graph structure model taking the electrochemical parameters as nodes and cross-level association as edges, and extracting a battery aging characteristic topological relation through a graph neural network; and finally, establishing a prediction model fused with multi-dimensional features, and realizing accurate prediction of the residual life and the capacity recession inflection point of the battery. And the multi-scale characteristics are combined with an electrochemical mechanism, so that the accuracy of battery health state evaluation under a complex working condition is improved.

The invention relates to the technical field of mobile energy storage vehicle energy management, and discloses a mobile energy storage vehicle energy management method based on an intelligent algorithm, and the method comprises the steps: collecting the charging and discharging rate of a battery pack, the environment temperature, the power grid load fluctuation and other multi-dimensional energy state data; a heterogeneous layered architecture of edge computing nodes, a cloud collaboration platform and a vehicle-mounted control terminal is constructed, energy partitions are divided according to peak valleys of a power grid, and low-delay response, global optimization and local closed-loop control modes are configured; fusing the multi-source heterogeneous energy data streams and removing abnormal values; a dynamic optimization decision model is constructed through a battery degradation model and a power grid supply and demand balance equation, and a multi-parameter collaborative constraint relation is solved through simultaneous solving of a multi-target iteration solver and a parallel gradient descent algorithm; and generating a three-level adaptive response instruction sequence of local battery overload alarm, regional power grid frequency modulation early warning and global energy scheduling imbalance pre-judgment based on constraint boundary triggering conditions. According to the method, the accuracy, the collaboration and the robustness of energy management are improved.

The invention discloses a multi-level feature fusion and time sequence modeling combined lithium battery life prediction method, and relates to the technical field of lithium ion battery health management and life prediction. The method comprises the steps of receiving time sequence observation data in a battery operation process, inputting the time sequence observation data into a pre-constructed local feature extraction model, and introducing a one-dimensional convolutional neural network into the local feature extraction model to perform feature extraction on the time sequence observation data to obtain local feature representation. According to the method, three structures of local feature extraction, global context modeling and bidirectional time sequence modeling are fused, and the battery degradation modeling capability and prediction precision are effectively improved. The TFN adopts an end-to-end architecture design, has good feature perception capability and time-dependent modeling capability, and can adapt to various degradation modes and complex time sequence environments. The method is suitable for life evaluation and health state monitoring in an intelligent battery management system, and has relatively high practical value and popularization prospect.

The invention discloses a lithium ion battery health state evaluation method and device based on deconstruction physical information, a medium and a product, and relates to the technical field of lithium ion battery health state evaluation, and the method comprises the steps: extracting a multi-dimensional health factor, inputting the multi-dimensional health factor into a deep physical information neural network fusing a self-attention mechanism module and a Koopman neural operator module, and obtaining a deep physical information neural network; and outputting the SOH estimation value. By extracting the universal health factors suitable for multiple working conditions, the problem that the universality of the health factors is insufficient is solved; a self-attention mechanism is utilized to enhance features and reduce redundancy, and the perception ability of the model to key information is enhanced; physical information is deconstructed by means of a Koopman neural operator, a physical mechanism of battery degradation is fused into the model, and the physical interpretability of the model is enhanced; the deep physical information neural network is fused with multi-dimensional information for estimation, individual differences and complex working conditions of different batteries are effectively dealt with, and therefore high-precision and high-robustness lithium ion battery SOH estimation is achieved.

The invention discloses an integrated scheduling system for realizing a PCS, an EMS and a BMS, and relates to the technical field of power control, and the system comprises a multi-dimensional performance evaluation module which constructs a battery aging dynamic model, carries out the training, carries out the health state pre-judgment through the battery aging dynamic model based on a standardized state vector, and generates a multi-dimensional performance evaluation index; the multi-objective optimization module is used for generating a collaborative scheduling strategy set by combining a fuzzy analytic hierarchy process with a multi-objective optimization solver of an improved genetic algorithm based on the multi-dimensional performance evaluation indexes; the dynamic derating module is used for generating an executable instruction queue with security constraints by combining an industrial internet of things protocol stack with a dynamic derating coefficient algorithm based on the collaborative scheduling strategy set; according to the invention, through the physical driving characteristic layer and the dynamic parameter calibration layer, the nonlinear coupling modeling of the cyclic attenuation and calendar aging mechanism in the battery aging dynamic model is realized.

The invention relates to the technical field of electrical engineering, and provides an electric energy storage system optimization scheduling method and system based on reinforcement learning, so as to solve the problems that it is difficult to effectively evaluate and control the battery aging risk in the frequent charging and discharging process and it is difficult to meet the real-time scheduling requirement in the edge side low computing power environment. The method comprises the steps of obtaining battery charging state and health state data in an electric energy storage system and power grid load and power grid electricity price data of an industrial and commercial park, and generating a state input data set; using the reinforcement learning agent to obtain a charging and discharging scheduling instruction; performing power distribution on the electric energy storage system, the multi-port converter power interface and the park load to generate multi-source cooperative power flow data; in combination with historical operation data, a reinforcement learning agent is utilized to optimize the charging and discharging scheduling instruction; and performing optimal scheduling of the electric power energy storage system according to the optimized instruction. According to the invention, efficient, intelligent and adaptive optimization scheduling of the power energy storage system in the industrial and commercial park is realized.

The invention provides a battery life intelligent prediction method fusing a time convolution network and a Mama state space model. The method comprises the following steps: a TCN branch extracts local time sequence characteristics in a battery degradation process by utilizing multi-layer expansion causal convolution, and nonlinear degradation phenomena such as capacity regeneration and the like are effectively identified; the Mama branch is based on a selective state space mechanism, dynamically models a long-period dependency relationship of a battery aging track, and adaptively focuses a key degradation node through weight adjustment of context sensing. In order to enhance model robustness, a variational mode decomposition unit is integrated to carry out noise suppression and mode separation on an original capacity sequence, and the generalization ability of a cross-battery chemical system is improved in combination with normalized constraint of a hidden state matrix. The accuracy of life prediction is remarkably improved, and key features in a complex degradation mode are effectively captured; the method has excellent noise suppression capability and cross-model generalization, is suitable for multiple types of battery systems, and realizes real-time aging state evaluation.

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 discloses a novel energy storage battery module health state multi-stage evaluation method, and belongs to the field of energy storage battery management, and the method comprises the following steps: firstly, measuring the capacity of a battery cell through a variable ampere integral method, and extracting a health factor through capacity increment analysis to represent battery attenuation; thirdly, training a battery monomer capacity estimation model based on the health factors and the capacity label of battery aging; and finally, when the health state of the battery pack is evaluated, the health state of the battery pack is evaluated by combining the battery cell capacity estimation value and the inconsistency characteristic in the battery pack, the overall prediction accuracy is improved, the aging trend of a single battery cell is captured, the potential problem of the battery pack is identified through the consistency characteristic of the battery pack, and the battery pack health state is evaluated. The method has high precision and practical value, and is especially suitable for health monitoring and life management of a large-capacity energy storage system.

The invention belongs to the field of lithium battery residual life prediction, and discloses a lithium battery residual life real-time prediction method based on dynamic uncertainty modeling, and the method comprises the steps: cooperatively collecting the full life cycle data of a lithium battery through multiple sensors, constructing a dynamic health score in combination with a convolutional neural network, and effectively capturing the non-stationary and non-linear characteristics of the data. The uncertainty of health scoring is quantified by further adopting a non-stationary random process, and related parameters are synchronously adjusted through joint optimization of an objective function to enhance the adaptability of the model to a complex degradation mode. And dynamically updating non-stationary random process parameters based on a Bayesian reasoning framework, and combining conjugate prior distribution of historical data and real-time observation values to realize parameter adaptive adjustment and dynamic prediction of the residual life of the lithium battery. According to the method, the defect that a traditional data driving method lacks physical interpretability is overcome, and the problem that precision is insufficient due to the fact that a battery degradation mechanism is complex in a single model driving method is solved.

The invention discloses a battery degradation model construction method based on a Bayesian physical information neural network, and the method comprises the following steps: constructing a pseudo-two-dimensional battery data generation module based on a battery aging mechanism; designing a feature extraction network to extract IC feature parameters; constructing an aging parameter mapping network to describe the relationship between the IC characteristic parameters and the battery aging parameters; and constructing a Bayesian battery health state inference network in a parameter randomization mode. By adopting the battery degradation model construction method based on the Bayesian physical information neural network, while explicit mapping of IC features and aging parameters is realized, physical residual constraints are constructed by using an electrochemical equation in the battery, so that the battery degradation model has relatively high physical interpretability, and the risk of a black box model is avoided; through a Bayesian framework based on random parameter distribution modeling, uncertainty in a model modeling process is quantified, so that confidence quantification of model prediction is realized, and a risk sensitive decision is supported.

The invention discloses an available energy estimation method and system for a prefabricated cabin type energy storage system, and belongs to the field of energy storage systems.The method comprises the steps that probability distribution of environmental parameters is constructed; historical operation data of equipment is collected, a probability relation between the environment temperature and equipment energy consumption is established, and an equipment energy consumption confidence interval is generated according to the probability relation; determining a confidence interval of the SOH by combining the battery aging experiment data and the real-time monitoring error; inputting the probability distribution of the environmental parameters, the equipment energy consumption confidence interval and the battery health state confidence interval into a probability model to generate a probability interval of available energy; and the optimal confidence level is selected in combination with the power grid dispatching requirement and the risk tolerance, and the available energy of the prefabricated cabin type energy storage system is obtained on the basis of the optimal confidence level according to the probability interval of the available energy. According to the method, confidence analysis is introduced, environmental parameters, equipment energy consumption and uncertainty of battery attenuation are quantified through a probability model, a probability interval of available energy is generated, and a basis is provided for scheduling decision making.

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 relates to a lithium battery charging system, and relates to the technical field of battery charging, the lithium battery charging system comprises a charging module, a detection module and a data processing module, and also comprises a battery health assessment module used for collecting battery cycle index, internal resistance and capacity attenuation parameters, and constructing a battery health state assessment model; the power grid interaction module is used for acquiring real-time electricity price data and a power grid load state; the environment sensing module comprises an environment temperature and humidity sensor and is used for collecting environment temperature and humidity parameters; and the AI prediction module is constructed based on a deep learning network and is used for predicting a battery state change trend. According to the invention, the battery health evaluation module is arranged to collect parameters such as cycle index, internal resistance and capacity fading rate to construct the SOH evaluation model, the problem of single dimension of battery health state evaluation in the prior art is solved, the aging degree of the battery can be accurately identified, and the risk of overcharge and undercharge of the aged battery due to improper power adaptation is avoided.

A method for estimating the state of health of lithium-ion batteries considering user charging behavior is provided. Firstly, data during the cycle charging process of the actual vehicles are collected, and it is analyzed. Then, the charging process data is applied to extract health features. The obtained health features are used to establish a state of health estimation model, and the established model is used to estimate the state of health of the battery. It involves reading the charging data, analyzing the frequency and occurrence of the start charging voltage and stop charging voltage, integrating them into a heat map, and drawing the IC curve of the charging and discharging process. This enables any charging behavior to extract the health features of battery aging, and predicts the health values of the battery, thus completing the estimation of the state of health for lithium-ion batteries.

The invention discloses a management method for prolonging the cycle life of a carbon-lead battery. The core of the management method is a carbon-lead battery temperature control method. A multi-dimensional temperature-life loss model is constructed, battery state parameters are collected in real time to pre-evaluate a future loss predicted value, a temperature control strategy is generated according to the future loss predicted value, and temperature control and closed-loop feedback correction are executed. Multi-dimensional model construction covers collection of cycle data of different conditions of a new battery and extraction of characteristic parameters in stages, dynamic loss function modeling is performed based on an Arrhenius equation, and a fuzzy controller is established in combination with factors such as battery aging and the like to output a model parameter correction coefficient. The generation of the temperature control strategy comprises the steps of defining a temperature control target layer, constructing a strategy parameter space based on particle swarm optimization, and performing predictive control and rolling optimization. The method effectively solves the problems of poor dynamic adaptability and inaccurate temperature control strategy of a temperature-life loss model in the prior art, and prolongs the cycle life of the carbon-lead battery.

The invention discloses a multi-target dynamic optimization hydrogen energy unmanned aerial vehicle energy management method and system under an MPC framework, belongs to the technical field of energy management, comprehensively considers factors such as energy consumption, battery degradation and output fluctuation, and realizes safe and efficient operation of a hybrid power system based on a predictive control idea. The management system comprises a sensing subsystem, a control subsystem, an interaction subsystem and a power supply subsystem. The management method comprises the following specific steps: adjusting a predictive control time domain according to a historical demand power sequence; updating the aging parameter of the hydrogen fuel cell, and fixing the weight coefficient of the cost function; through a multi-target online optimization algorithm, obtaining an optimal SOC change curve under a prediction time domain; and outputting the reference power of the fuel cell through linear MPC control according to the reference change curve.

The invention relates to the technical field of lithium ion battery testing, and discloses a lithium ion battery life loss evaluation method and device, a medium and equipment, and the method comprises the following steps: S1, collecting multi-modal dynamic data of a lithium ion battery in a charge-discharge cycle process; s2, performing time-frequency domain conjoint analysis on the multi-modal dynamic data, and extracting a battery aging sensitive feature set; s3, constructing a multi-scale coupling model of battery life loss; by constructing a multi-modal data fusion mechanism and a dynamic feature extraction system, during lithium ion battery life loss evaluation, change trends of electrochemical impedance spectroscopy and heat distribution key parameters are captured in real time based on time-frequency domain conjoint analysis, sensor signal distortion and drift problems can be identified, the extraction precision of aging sensitive features is improved, and the accuracy of lithium ion battery life loss evaluation is improved. The problem of characteristic errors caused by signal interference in traditional evaluation is solved, and the accuracy and reliability of life loss evaluation are ensured.

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 relates to the technical field of battery health, and discloses a battery SOH estimation method and device, computer equipment and a storage medium, and the method comprises the steps: collecting battery operation data and environment parameters in real time; constructing an environmental stress factor model based on the environmental data and a preset weight coefficient; calculating an environmental stress factor value in real time based on the environmental stress factor model, and constructing a mapping function between the environmental stress factor value and the battery health state corresponding to the timestamp based on the battery operation data; and obtaining a real-time battery SOH estimation value based on the battery operation data, the environmental stress factor value and the mapping function. According to the method, the built environmental stress factor model and the nonlinear mapping function are utilized, online real-time estimation of the SOH under the influence of different environmental factors is considered, the aging acceleration effect of the external environment on the battery is effectively reflected, and the estimation accuracy of the SOH of the energy storage system under the atrocious weather condition is improved.

The invention discloses a lithium battery life prediction method, which comprises the following steps of: establishing an account ID (Identity) of each electric vehicle on a charging pile, and recording data of each electric vehicle during charging in the account ID to obtain historical charging data; collecting current charging data of the battery in real time through the charging pile; evaluating the health state of the battery based on the historical charging data and the current charging data, wherein the health state evaluation comprises battery capacity attenuation, internal resistance change and charging efficiency evaluation; using a battery recession model to predict the remaining life of the battery; generating early warning prompts of the health state and the residual life of the battery according to the prediction result; according to the scheme, the comprehensive analysis model based on historical data and real-time data of the charging pile is established, so that the problems of low accuracy, single evaluation dimension, imperfect early warning mechanism and the like in an existing battery life prediction method are solved, and the intelligent degree of battery life prediction and management is improved.

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 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 discloses an online safety management and early warning method for a power battery of an electric vehicle, which comprises the following steps of: acquiring multi-dimensional operation data of the power battery and preprocessing the multi-dimensional operation data to obtain a standardized data set; obtaining a dynamic working condition and a time sequence feature based on the standardized data set, generating a battery state feature set and judging whether the battery state feature set meets a preset condition, and if not, optimizing the battery state feature set to obtain an optimized feature set; constructing a risk assessment model, and combining the optimized feature set to obtain a battery safety risk probability and judge a risk level; if the risk level exceeds a threshold value, obtaining a personalized management strategy generated by the cloud based on the optimized feature set and issuing the personalized management strategy to a vehicle end controller; and fusing vehicle end real-time data and battery aging data through an online learning algorithm, dynamically adjusting risk assessment model parameters, generating an adaptive model, and outputting a graded early warning signal. According to the method, the safety risk of the power battery under the complex working condition is effectively evaluated, and a real-time early warning and personalized management strategy is provided.

The invention discloses a new energy vehicle endurance display method and system based on a power battery SOH, electronic equipment and a vehicle. The method comprises the steps that a vehicle, a vehicle control unit, a battery management system and a display module are triggered to be started through a KL15 signal wake-up signal, and power-on initialization and self-inspection are executed; after the self-inspection is completed, reading the stored current state-of-health initial value SOH0 of the power battery and the stored current state-of-charge initial value SOC0 of the power battery from the EEPROM; performing dynamic correction on the read SOC value, performing fixed value locking on the SOH value, and sending the corrected SOC value and the locked SOH value to a CAN bus of the whole vehicle; based on the SOH0 and the SOC1 sent by the battery management system, the remaining endurance mileage S is calculated in combination with the total delivery mileage L of the new vehicle; and the instrument and large-screen display module is used for displaying the current remaining endurance mileage in real time. According to the method, the problem of display deviation caused by neglecting battery aging in a traditional method is solved, and the user driving experience and the battery management efficiency are improved.

The invention relates to the technical field of battery temperature control, and discloses a new energy automobile power battery temperature adjusting method, system and equipment, and the method comprises the following steps: synchronously collecting vehicle operation data, traffic information, charging pile state and battery aging parameters, and carrying out timestamp alignment and abnormal data filtering; converting the road test data into a vehicle coordinate system, predicting an aging trend in combination with LSTM, and generating a cross-domain feature vector through feature coding and tensor fusion; and generating a basic temperature control strategy based on reinforcement learning, predicting SOH deviation in combination with digital twinning, dynamically calculating an aging compensation coefficient, and synthesizing an anti-aging enhancement strategy. According to the invention, the maximum temperature is controlled within a safety threshold value by constructing a full-link cooperative temperature control system, so that the effectiveness of a thermal management strategy in a full life cycle is ensured.

The invention discloses a digital twin drive life prolonging system and method in a lithium battery aging process, the system comprises a sensor detection module, an AI control module and a regulation and control module, the sensor detection module detects pressure, temperature, gas components, SEI film conditions and voltage data between a conductive layer and an anode, and inputs the data into the AI control module; the AI control module performs electrochemical-thermodynamic-mechanics multi-physics field coupling modeling by applying a digital twinborn technology, performs data analysis and processing in combination with cross-working-condition model migration, screens out an optimal regulation and control scheme according to a mirage optimization algorithm, sends an instruction to the regulation and control module, and controls the regulation and control module according to the instruction of the AI control module. Parameters in the energy storage cabin are adjusted, the SEI film state is maintained, and lithium dendrite precipitation is controlled, so that the service life of the lithium battery is prolonged; according to the method, the digital twinborn model is utilized to identify battery capacity attenuation inflection points, trigger lithium supplement or charge strategy optimization and other regulation measures, irreversible damage is avoided, and the replacement frequency is reduced.

The invention provides a motor train unit battery aging evaluation method and system based on mechanism and data fusion, and relates to the technical field of battery health management, and the method comprises the steps: extracting and processing multiple physical characteristic parameters of a battery in experimental data, and obtaining a processed first structured data parameter and a processed first unstructured data parameter; combining an Arrhenius equation, a quasi-two-dimensional model and an equivalent circuit model to construct a high-speed rail motor train unit battery aging mechanism model with electric-thermal-mechanical multi-physical characteristics, and predicting a first aging state of the battery; establishing a mapping relationship between battery performance and multiple physical characteristics to obtain a second aging state of prediction output; and taking the first aging state and the second aging state as two independent evidence bodies, dynamically adjusting a Dempster combination rule weight based on the conflict factor, and further determining a final aging state through a maximum confidence criterion. According to the method, the aging state prediction precision and reliability are improved, and technical support is provided for service life evaluation and maintenance strategy optimization of the high-speed rail motor train unit battery.