1085 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 power batteries, in particular to a battery aging state detection system, method and device. The method comprises the following steps: obtaining battery pack structure design data of the unmanned aerial vehicle; according to the battery pack structure design data, carrying out single battery health monitoring, and generating a multi-parameter feature vector; establishing a data mapping relation of the battery monomer indexes through the multi-parameter feature vectors, thereby obtaining initial health state data of the battery pack; collecting charging and discharging sample data of the unmanned aerial vehicle under different flight conditions; a dynamic current excitation sequence is constructed based on charging and discharging sample data, and current load changes in different flight stages are simulated, so that a multi-working-condition current disturbance sequence is generated. By simulating the load change of the unmanned aerial vehicle in different flight stages and combining the dynamic response capture capability of the high-frequency current sensor, the electrochemical characteristics of the battery can be extracted in real time, and the aging state of the battery under the complex working condition can be accurately identified.

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 discloses a communication and optimization method and system of an energy storage system, and relates to the technical field of communication. Real-time state parameters of the battery pack are obtained through wireless communication, the state parameters are predicted through time sequence analysis and a data compensation mechanism, the problems of numerous battery packs, communication time delay and packet loss are solved, the real-time performance and accuracy of data are ensured, and the real-time performance of the battery pack is improved by combining a digital twinborn model. The system can optimize a control strategy based on battery aging evaluation and simulation analysis, realizes dynamic adjustment and refined energy management, adopts a differential privacy technology to encrypt and transmit data, ensures privacy protection of the control strategy, and ensures the safety of the control strategy through real-time monitoring and self-adaptive adjustment mechanism and minimization of the difference between a digital twin model and an actual system. The stability, efficiency and safety of the energy storage system are further improved, and long-term reliable operation of the system is guaranteed.

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 relates to the technical field of battery detection, in particular to a new energy vehicle battery fault intelligent detection system which comprises a voltage change analysis module, an internal resistance micro-analysis module, a current response monitoring module and a fault diagnosis integration module. According to the invention, by refining voltage monitoring and internal resistance analysis, the capability of early recognition of battery performance degradation and faults is remarkably improved, the early warning of the faults is more accurate by recognizing abnormities through an advanced data processing technology and refining fluctuation analysis, the risk of false alarm and missing alarm is reduced, and the internal resistance data is analyzed by introducing a fractal theory. Microscopic changes of battery aging can be more accurately captured, early detection of the aging trend is realized, pulse current testing and current response data analysis are combined, evaluation of battery performance stability is further enhanced, comprehensiveness and depth of a detection result are ensured, and detection accuracy is improved. And the accuracy of battery fault detection and the efficiency of determining the fault type are greatly improved.

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 an optical storage and charging integrated intelligent management system and method based on a virtual power plant platform, relates to the technical field of optical storage and charging, and aims to solve the problems that in energy storage battery management, long-time storage may cause increase of self-discharge rate of batteries and aggravated capacity attenuation, but an existing system often adopts a unified management strategy, batteries with different storage times are not distinguished, and the energy storage efficiency is poor. According to the optical storage and charging integrated intelligent management system and method based on the virtual power plant platform, the storage time and temperature of the energy storage battery are incorporated into a charging and discharging management strategy, so that the service life of the battery is prolonged; the charging and discharging logic can be dynamically adjusted according to charging requirements and light energy input changes, the adaptability of the system to complex conditions is enhanced, energy waste and charging delay are avoided, and the energy utilization efficiency is improved; the maintenance cost and the downtime are reduced, and the overall reliability and flexibility of the system are improved.

The invention discloses an active power distribution network two-stage island division method and system considering flexible resource support. The method comprises the following steps: establishing a first resource allocation model considering energy storage and controllable load, the first resource allocation model comprises energy storage battery charging and discharging upper and lower limit constraints, an energy storage battery degradation cost model, a controllable load model and a controllable load related user protocol; obtaining parameter data of a target power distribution system, wherein the parameter data comprises a line parameter, a load level, a load level, a network topology connection relationship, a system operation voltage level and branch current limitation, a system fault position, a reference voltage and a reference power initial value; and according to the parameter data, establishing a mathematical model for two-stage island power supply recovery based on a first resource allocation model. The method for solving the island is simple and convenient, the island recovery amount can achieve the optimal recovery effect, meanwhile, the search time is greatly shortened, the solving efficiency is higher, and the rapid power supply recovery capacity of the power distribution network is improved.

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 belongs to the technical field of lithium ion battery state prediction, and particularly relates to a Transform-based lithium battery health state estimation method. Comprising the following steps: carrying out abnormal value processing and feature extraction on a battery aging data set to obtain feature factors; calculating the relevancy between the characteristic factors and the original capacity sequence of the lithium ion battery, and obtaining the characteristic factors meeting a preset relevancy threshold value; decomposing the original capacity sequence based on a completely adaptive noise set empirical mode decomposition algorithm to obtain intrinsic mode functions of different frequencies; combining sin chaotic mapping, an adaptive dynamic weight factor and a reverse learning-Cauchy alternate variation strategy to improve a sparrow search algorithm, and optimizing the trained Transform model by using the improved sparrow search algorithm to obtain a lithium ion battery health state estimation model; and estimating the health state of the lithium ion battery. The method can accurately estimate the health state, and facilitates the user to optimize the use of the battery.

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 a lithium ion battery health state estimation method based on a cross attention mechanism, which comprises the following steps of: firstly, acquiring voltage, current and temperature operation data of a lithium ion battery, and dividing the lithium ion battery into a charging segment and a discharging segment; and extracting and quantizing health indexes reflecting a battery aging mode and a battery use behavior from the charging segment and the discharging segment. Then, a stacked convolutional neural network is adopted to carry out convolution operation on the charging fragments to obtain charging data depth features, and cross attention weight distribution is utilized to carry out complementary fusion on the charging data depth features and the health indexes to obtain fusion features most relevant to the battery health state; and finally, inputting the fusion features into a full-connection neural network, predicting the state of health of the battery and analyzing main factors causing reduction of the state of health of the battery. According to the invention, the battery health state can be accurately predicted under the condition of random charging and discharging of the vehicle battery, and meanwhile, main factors causing reduction of the battery health state are obtained.

The invention relates to the technical field of battery management, in particular to a new energy vehicle battery data analysis method and system, and the method comprises the following steps: merging a state feature set of a battery based on the charging and discharging period and use environment data of the new energy vehicle battery, extracting the voltage, current and temperature features of the state, and obtaining a state feature set; and carrying out feature combination on the battery degradation state, classifying and associating feature items and temperature features in the feature combination, and obtaining a state feature merging result. According to the method, through merging the new energy vehicle battery charging and discharging period and the use environment data, the battery state characteristics are accurately extracted, refined classification and correlation analysis are realized, the discrimination capability and adaptability of the state data are improved, and the state transition trend is flexibly captured based on the linkage trend of the state transition frequency and the current observation data; and dynamic evaluation is realized through correlation of periodic temperature and current, an accurate and reliable state change evaluation scheme is provided, and the dynamic response of battery management and the full-life periodic performance regulation and control quality are optimized.

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.

The invention discloses a dynamic real-time online measurement method and system for the internal temperature of a lithium ion battery, and relates to the technical field of temperature measurement of the lithium ion battery, and the main scheme is as follows: obtaining the battery working data of the current lithium ion battery and calculating the actual working environment temperature; obtaining relaxation time; in the relaxation time, continuously measuring a dynamic impedance phase angle; acquiring historical use data of the battery, and calculating an influence factor and a battery aging coefficient; calculating a static impedance phase angle through the influence factor, the battery aging coefficient and the dynamic impedance phase angle; calculating the static temperature according to the static impedance phase angle and the actual working environment temperature; calculating a dynamic temperature according to the static temperature and the battery working data; setting a dynamic temperature threshold set, comparing the dynamic temperature with the threshold, judging whether the battery works in the optimal performance range, and if the dynamic temperature does not meet the threshold, triggering abnormal early warning; according to the scheme, the use safety of the battery is improved, and the performance of the battery is optimized.

An intelligent battery management system and method, and in particular a battery management system utilizing a method for the estimation of the instantaneous positive and negative electrode potentials used during a battery charging / discharging process. The intelligent battery management system and method may be used in a battery control system, such as a battery charging / discharging system to preserve the health of a connected battery over multiple cycles, or in a battery diagnostic system for predicting or modelling battery performance. During a battery charging / discharging process, one or more set points may be set for the electrode potentials on the negative and or positive electrodes. Using the battery overpotential in the determination of electrode potential, allows the battery management method and system to exhibit a high level of adaptivity to battery ageing and battery degradation.

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 battery remaining life prediction method capable of automatically identifying battery health characteristics. Firstly, a lithium ion battery data set is obtained, an automatic health feature extractor is used for extracting health features related to the aging state of the battery, the extractor can efficiently recognize key features in the aging process of the battery, the accuracy and automation degree of feature extraction are improved, and errors caused by human intervention are reduced. Then, a health feature filter is used, contribution of each feature to prediction of the remaining life of the battery is analyzed and quantified through an SHAP model, and features with remarkable influence are screened out; and the screened features are divided into a training set and a test set, a BiLSTM model is trained, verification is performed through a forward and backward dependency relationship, and a feedback mechanism is added for optimization. And if the prediction precision of the model meets the requirement, the residual life of the battery data can be accurately predicted.

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.