1986results about "Fuel cell control" patented technology

The invention discloses a hydrogen fuel cell vehicle ultralow temperature cold start thermal management system, and relates to the technical field of hydrogen fuel cell vehicle thermal management, the hydrogen fuel cell vehicle ultralow temperature cold start thermal management system comprises a thermal management platform, the thermal management platform is in communication connection with the following modules: a temperature monitoring and predicting module, and the temperature prediction module is used for monitoring the temperature of key parts in the hydrogen fuel cell by using the temperature sensor and predicting the change trend of the temperature in the fuel cell in combination with the temperature prediction model. By utilizing the high-precision temperature sensor and the long-short-term memory network model, the internal temperature change trend of the fuel cell is pre-judged in advance, the heating power is dynamically adjusted, it is ensured that the temperature of the cell is rapidly increased to the proper working temperature in the ultralow-temperature environment, starting difficulty caused by local supercooling is avoided, the low-temperature starting time is remarkably shortened, and the starting efficiency is improved. The reliability and the stability of the system in an extreme environment are improved, and the normal operation of the hydrogen fuel cell vehicle in a cold region is guaranteed.

Provided is a method and a system for accelerated life testing (ALT) of a proton exchange membrane fuel cell (PEMFC). In the present disclosure, a collected voltage-time sequence data of the PEMFC is filtered and subjected to empirical mode decomposition (EMD), such that a voltage data is decomposed to obtain K intrinsic mode functions. A constructed bidirectional long short-term memory-based artificial neural network (BiLSTM) shows majority input characteristics and can model each of the intrinsic mode functions independently, thereby reducing a difficulty of long-cycle life prediction in limited training data scenarios. In addition, optimal parameters of the BiLSTM are optimized through a sparrow search algorithm, which greatly improves a prediction accuracy of a remaining useful life for the PEMFC. The method and the system of the present disclosure exhibit low computing cost, simple parameter setting, and high prediction accuracy, and are extremely suitable for operation and maintenance of the PEMFC.

The invention relates to a proton exchange membrane fuel cell (PEMFC) high current density performance prediction method, system, equipment and medium. The method comprises the following steps: establishing a multi-physical field coupling model which comprehensively considers complex processes such as electrochemical reaction, proton conduction, gas diffusion and heat transfer, and describing the change of each physical quantity by adopting a partial differential equation based on a basic physical law; performing grid division and numerical discretization on the proton exchange membrane fuel cell model; selecting model parameters, and verifying the model through experimental data of different working conditions; inputting actual working condition parameters into a model to predict performance, and analyzing a simulation result; using a convolutional neural network, a recurrent neural network and an auto-encoder to extract features from different types of data and fuse the features to form a comprehensive feature vector; a deep neural network prediction model is constructed, and a cross entropy loss function and an Adam optimizer are adopted for training; dropout, L1 and L2 regularization, k-fold cross validation and transfer learning are utilized to optimize the model, and the generalization ability is improved; the system, the equipment and the medium realize high current density performance prediction of the proton exchange membrane fuel cell (PEMFC) based on the method; the prediction precision is improved, the experiment cost is reduced, the internal mechanism can be deeply understood, and powerful support is provided for design optimization, operation management and fault diagnosis of the fuel cell.

The invention discloses a fuel cell hydrogen circulation cooling integrated system, and relates to the technical field of fuel cells, the fuel cell hydrogen circulation cooling integrated system comprises a hydrogen circulation cooling management center, the hydrogen circulation cooling management center is in communication connection with the following modules: a multi-source state sensing module, which is used for collecting multi-dimensional state data of a hydrogen circulation-cooling system in real time, extracting state characteristics, and sending the state characteristics to a control module; and forming a state feature sequence table. The multi-dimensional state data of the hydrogen circulation-cooling system is collected, the dynamic cooperative control strategy of the reinforcement learning decision module is combined, the precision and stability of temperature control are remarkably improved, the Markov decision process model is used for predicting the instantaneous heat production change caused by load sudden change, and the stability of the system is improved. And through combined adjustment of hydrogen circulation and a cooling system, temperature fluctuation is rapidly restrained, compared with traditional PID control, the temperature overshoot is reduced under the dynamic working condition, it is ensured that the galvanic pile works within the optimal temperature range all the time, the service life of the galvanic pile is prolonged, and the energy conversion efficiency is improved.

The invention discloses a power distribution method and system based on fuel cell multi-stack health state difference, and belongs to the technical field of fuel cell application. The specific method comprises the following steps: monitoring operation parameters of a plurality of fuel cell stacks through a sensor; obtaining the health state of each fuel cell stack and the health state of the lithium battery; establishing a hydrogen consumption model, a fuel cell SOH loss model, a multi-stack SOH difference equivalent loss model, a lithium battery SOH loss model and a lithium battery start and end SOC loss model; the calculation loss is converted into equivalent hydrogen loss, and a target function model is constructed; and optimizing the target function by using a reinforcement learning algorithm, distributing power output of the fuel cell and the lithium battery according to the current required power and the health state through an energy management system, and regularly updating parameters of a parameter identification module and a model to realize dynamic optimization. The method can identify the life difference between multiple piles, balance the life difference to realize multi-pile performance balance and improve the overall life of a multi-pile system.

The invention discloses an intelligent energy scheduling method and system for a flow battery, and relates to the technical field of flow batteries, and the method comprises the following steps: collecting energy operation parameters of the flow battery in real time, and dynamically estimating an electrolyte state to obtain multiple pieces of energy state information; a target to be dispatched is set, and a fuzzy control strategy is formulated through retrograde multi-target optimization; executing a fuzzy control strategy to generate a regulation and control signal set and scheduling; and finally, according to a scheduling result, performing reverse tracing to optimize a fuzzy control strategy, and updating a regulation signal set to obtain a multi-stage regulation instruction to perform intelligent scheduling on the energy of the flow battery. The method solves the technical problems of low matching degree between a scheduling strategy and an actual demand, large power loss and fast battery life attenuation caused by difficulty in accurately capturing an energy state in real time in a traditional flow battery scheduling method, and achieves the purposes of intelligent flow battery energy scheduling, power loss reduction, battery life prolonging and energy conservation. And the matching degree between the scheduling and the actual demand is improved.

The invention discloses a gas-heat-electricity balance management and control method and system for a kilowatt-level SOFC (solid oxide fuel cell) cogeneration system, and belongs to the field of solid oxide fuel cells. The method comprises the following steps: setting initial operation parameters according to structure parameters and design working conditions of an SOFC electric pile, a heat exchanger, a reformer and a catalytic burner; a module start-stop coordination instruction is generated through the integrated controller, and temperature and pressure changes of all the modules in the start-stop process are controlled; system operation data are monitored in real time through a sensor, real-time diagnosis is conducted through a multi-dimensional data fusion algorithm, and abnormal states such as over-burning, over-air and temperature runaway are recognized; and according to the diagnosis result, the fuel flow, the air flow, the heat exchange power and the like are adjusted through a multivariable dynamic regulation and control strategy, so that reactor temperature fluctuation is controlled, and a temperature runaway early warning signal is generated. According to the method, the overall stability and efficiency of the SOFC combined heat and power system are improved by optimizing gas-heat-power balance management and control.

The invention provides an efficient and stable ship hybrid power generation system which comprises a hydrogen production unit and an ammonia supply system, and the hydrogen production unit comprises an ammonia decomposer, a hydrogen separator, a high-pressure hydrogen storage tank, a fuel cell and an ammonia-hydrogen internal combustion engine. The ammonia decomposer enables the ammonia flow to enter the ammonia decomposer in a segmented constant mode for catalytic decomposition through four set working conditions; the hydrogen separator is used for separating and purifying the generated mixed gas, and the separated and purified hydrogen is respectively fed into the fuel cell and the ammonia-hydrogen internal combustion engine through valves for power generation; the molten salt system recycles high-temperature tail gas of the ammonia-hydrogen internal combustion engine through the heat exchanger for heat energy storage and power generation through the steam Rankine cycle system. The regulation and control method is used for stabilizing the system and prolonging the service life of equipment by limiting the dynamic loading rate and start-stop times of the fuel cell and the ammonia-hydrogen internal combustion engine, and is used for waste heat recovery power generation through the molten salt and the steam expansion machine, so that the efficiency of the system is improved.

The invention discloses a fuel cell output voltage control method and system based on fuzzy neural network PID, and the method comprises the steps: building an air-cooled fuel cell voltage model based on an MATLAB / Simulink simulation platform, and obtaining the temperature changes of an air-cooled fuel cell under different working conditions; establishing an air-cooled fuel cell test bench, and obtaining a polarization curve of output voltage changing along with current; according to experimental data, calibrating the semi-empirical equation by adopting a nonlinear least square method, and comparing experimental results to verify the accuracy of the model; the input hydrogen flow is changed through the PID controller, so that stable control of the output voltage of the galvanic pile is realized, and the power generation performance of the battery is improved; a fuzzy neural network is adopted to optimize a PID controller, control parameters of PID are adjusted in a self-adaptive mode according to the error between expected voltage and actual voltage and the error change rate, and therefore a better temperature control effect is achieved. The method has the advantages of high robustness, high response speed and the like, and ensures that the voltage output of the fuel cell stack is maintained at a fixed preset value.

The invention relates to the technical field of energy power, in particular to a vehicle power system based on a solid hydrogen pool, a passenger vehicle and a control method. The vehicle power system comprises a power driving module, a fuel cell module, a solid hydrogen pool module, a heat dissipation module and a whole vehicle control module. The fuel cell module comprises a cell stack unit, a hydrogen feeding and discharging unit, an air feeding and discharging unit and a tail discharging unit, and the cell stack unit is electrically connected with the power driving module. The solid hydrogen pool module comprises a solid hydrogen storage unit, a water supply unit and a hydrogen release buffer unit. The heat dissipation module is in butt joint with the cell stack unit and the solid hydrogen storage unit, and the whole vehicle control module is in communication connection with the power driving module, the fuel cell module and the solid hydrogen pool module. The passenger vehicle comprises a vehicle power system. The fuel cell module based on the solid hydrogen pool module is used for stably supplying power to the power driving module, so that power is provided for the passenger car, and the endurance requirement of the passenger car is met.

The invention discloses a fuel cell fault-tolerant control method and device based on reinforcement learning in the field of fuel cells, and the method comprises the steps: obtaining monitoring parameters of a fuel cell in an operation state, and constructing a state space based on the monitoring parameters; obtaining an executable operation in the running state of the fuel cell, and constructing an action space based on the executable operation; constructing a reward function based on the state space and the action space by taking control cost minimization as a target; constructing a fault-tolerant control strategy model based on a DDPG algorithm, and training and verifying the fault-tolerant control strategy model through a state space, an action space and a reward function; and fuel cell fault-tolerant control is carried out through the trained and verified fault-tolerant control strategy model. According to the method, the DDPG algorithm is introduced, target state parameters are monitored in real time in the operation process, a control strategy is automatically adjusted by using reinforcement learning when a fault occurs, it is ensured that the system can still keep stable power output under the fault condition, and optimal control is achieved.

The invention relates to the technical field of battery energy management, in particular to an energy management system and method for a hydrogen fuel cell module, and the system comprises a monitoring collection module which collects and contrasts the readings of a sensor based on the voltage parameter, the current parameter and the temperature parameter of the hydrogen fuel cell module to obtain original monitoring data, and performing numerical comparison on the load demand of the power distribution network to generate supply and demand matching information. In the invention, the difference statistics and the difference operation improve the accuracy of energy distribution, so that the matching between the energy distribution and the actual load demand is more accurate, and the overall energy efficiency is improved while the energy waste is reduced. And frequency band extraction and rate recording can capture subtle changes output by the battery, and the response speed is increased. In addition, the analysis and adjustment of the phase feature group provide adaptive ability, and the stability and reliability of the load change of the power grid are enhanced.

Disclosed in the present invention are a fuel cell integrated with a heat pump energy recovery system, and an energy recovery control method. The main features are that: a heat pump system is arranged between a cooling system and an air supply system, and after absorbing waste heat in the cooling system, the heat pump system heats exhaust tail gas discharged by a cell stack module, so as to increase the temperature of exhaust tail gas entering an expander, thereby improving the energy recovery efficiency of the expander. The heat pump system has a simple structure, and an energy recovery strategy is reasonable, thereby greatly simplifying the structure of the energy recovery system of the fuel cell, reducing manufacturing costs, and eliminating the risk of temperature runaway.

The invention discloses a fuel cell vehicle thermal management system and a construction method thereof, and the fuel cell vehicle thermal management system comprises a thermodynamic modeling module, a double-layer fuzzy sliding mode controller, an actuator cooperative control module, a working condition adaptive module and a fault protection module. According to the thermal management system of the fuel cell vehicle and the construction method of the thermal management system, the dynamic thermodynamic model is established, and the double-layer fuzzy sliding mode controller and the actuator cooperative control module are designed, so that millisecond-level high-precision tracking of the temperature of the electric pile and online real-time compensation of unknown disturbance are realized; meanwhile, stable operation of the system in the whole working condition domain range can be ensured through the working condition self-adaption module and the fault protection module, and the system has the advantages of being high in control precision, high in anti-interference capacity and good in system reliability.

The invention discloses a multi-dimensional multi-physics field coupling analysis platform of a hydrogen fuel cell based on artificial intelligence, and relates to the technical field of hydrogen fuel cells, the platform comprises a user interaction interface module, a multi-physics field model construction module, a data acquisition and preprocessing module, an integration module and an analysis and result output module; an electrochemical reaction model, a temperature field model and a gas and liquid flow model are respectively built through a multi-physical field model building module, the coupling strength among different physical fields is quantified, and the electrochemical reaction model, the temperature field model and the gas and liquid flow model are integrated into a multi-physical field model; physical action parameters such as electrochemical reaction rate, temperature value and the like are associated with time change, spatial distribution and fault conditions, so that the platform can simulate the multi-physical field coupling effect of the hydrogen fuel cell under complex working conditions, and the performance of the fuel cell is analyzed; and a reliable basis is provided for the geometric structure, material selection and operation condition optimization of the fuel cell.

The invention relates to the technical field of energy power, in particular to a vehicle power system based on a solid hydrogen pool, a commercial vehicle and a control method. The vehicle power system comprises a power driving module, a power battery module, a fuel battery module, a solid hydrogen pool module and a whole vehicle control module. The power battery module is connected with the power driving module, the fuel battery module is connected with the power driving module, the solid hydrogen pool module is connected with the fuel battery module, and the whole vehicle control module is in communication connection with the power driving module, the power battery module, the fuel battery module and the solid hydrogen pool module and used for controlling the whole vehicle according to the vehicle power requirement. And the operation of each module is controlled in a closed-loop manner. A commercial vehicle includes a vehicle powertrain. The fuel cell module based on the solid hydrogen pool module is used for providing a stable hydrogen source for the fuel cell module, and the mode that the fuel cell module and the power cell module conduct hybrid power is adopted, so that rapid load changing and high dynamic response coping are achieved, and the requirement of a high-power commercial vehicle is met.

The invention relates to an electrolytic hydrogen production and fuel cell waste heat gradient utilization system and method based on an organic Rankine cycle, and belongs to the field of new energy. The system comprises a wind power generation unit, a photovoltaic power generation unit, a storage battery energy storage system, an electrolytic hydrogen production device, a hydrogen storage system, a fuel cell power generation unit, a waste heat utilization unit and an organic Rankine cycle device, and can be used for realizing multi-element consumption and gradient utilization of renewable energy sources. The method specifically comprises the following steps: establishing equipment models in a system, wherein an electrolytic bath considers five states of shutdown, cold start, standby, hot start and standby; on the basis of a mixed integer linear programming method, operation constraints of all equipment, renewable energy output prediction and electricity, heat and hydrogen load requirements are integrated, an optimization scheduling model meeting the electricity, heat and hydrogen load requirements and economical efficiency of the system is constructed, output of all the equipment is coordinated, and finally the optimization targets of maximizing the economical efficiency of the system and meeting the electricity, heat and hydrogen load requirements of the system are achieved.

The invention discloses a fuel cell rapid cold start method considering initial water content optimization, and belongs to the technical field of fuel cell automobile optimization control. According to the fuel cell rapid cold start method considering initial water content optimization, a closed-loop model prediction controller is firstly designed to accurately control the current of a galvanic pile, and then the initial film water content is optimized by using a particle swarm optimization algorithm, so that the rapidity of a cold start system is improved. The method can be structurally divided into the following parts: establishment of a cold start model, design of a parameter self-tuning model prediction controller and optimization of the optimal initial membrane water content. In the cold start process, the current output of the electric pile is adjusted in real time according to different environment temperatures and membrane water contents, controller parameters are adjusted in a self-adaptive mode, the freezing rate and the heating rate of water produced by the electric pile can be effectively balanced, and the performance of a cold start system is improved.

The invention belongs to the technical field of fuel cell equipment degradation prediction, and provides a PEMFC degradation prediction method and system based on spatial dynamic non-stationary reconstruction attention, and the technical scheme is as follows: obtaining multi-sensing historical operation data of a fuel cell PEMFC; training the constructed PEMFC degradation prediction model based on the multi-sensing historical operation data of the fuel cell PEMFC to obtain a trained PEMFC degradation prediction model; the construction process of the PEMFC degradation prediction model comprises the steps that statistical information of multi-sensor time sequence data in the spatial dimension is acquired, and the statistical information in the spatial dimension is combined and mapped to a non-stationary factor; obtaining battery degradation characteristics in a time dimension; based on a non-stationary factor, dynamically correcting an attention weight in a self-attention mechanism during spatial feature extraction to obtain a corrected battery degradation spatial feature; the method can better cope with the non-stationarity of multiple sensor signals at different moments and under different working conditions, thereby capturing more critical and richer degradation feature expressions.

The invention discloses a power control method for a methanol reforming fuel cell power generation system, and relates to the technical field of power generation system power control, and the method achieves the precise representation of the internal dynamic coupling relation of the system through the construction of a digital twin model covering the whole methanol reforming process and the whole electrochemical conversion process of a fuel cell. Therefore, the problem of hydrogen supply lag caused by reforming reaction delay in the traditional control is effectively eliminated, and the output power of the fuel cell can be highly synchronized with the load demand; the Smith predictor is adopted to perform online estimation and compensation on methanol reforming inherent time delay, and the MPC is predictively controlled to perform real-time optimization scheduling under the constraints of economical efficiency and equipment health state in combination with a multi-target model, so that the power generation efficiency is improved, the fuel consumption and the operation cost are remarkably reduced, and the inhibition capability of the system on load fluctuation is enhanced.

The invention relates to the technical field of fuel cells, in particular to a method and system for accurately controlling the hydrogen stoichiometric ratio of a fuel cell, and the method comprises the steps: firstly, obtaining a quantitative anode health state value through a fusion calculation model by collecting load current, anode inlet and outlet pressure, temperature, high-frequency resistance and other multi-source signals in real time; a feed-forward model based on a load current change rate predicts sudden change of hydrogen demand, and an optimal hydrogen flow instruction is jointly generated in combination with output of a PID controller which adaptively adjusts parameters according to a health state value. According to the method, the integrated diagnosis parameter, namely the anode health state value, is created, so that the internal state of the fuel cell can be accurately perceived and prospectively judged. Through deep fusion of multi-source heterogeneous information such as high-frequency resistance, anode pressure difference, current and temperature, a comprehensive index capable of comprehensively and quantitatively reflecting anode water content, gas concentration and runner smoothness is generated.

The invention relates to the technical field of battery energy storage system evaluation, and relates to the technical field of battery energy storage, in particular to a method for balancing the service life and performance of a fuel cell, which comprises the following steps of: 1, acquiring multi-source data of the fuel cell, and applying a series of preset pulse current load sequences to the fuel cell, measuring response parameters output by the fuel cell in real time; 2, Fourier transform processing is carried out on the response parameters, and frequency domain signals of the response parameters are obtained; the performance and service life balance model is constructed based on the fusion feature vector containing the dynamic performance, the service life characteristic and the operation state of the fuel cell, the load distribution weight can be dynamically adjusted, the performance and the service life of the single fuel cell are kept balanced, and therefore the overall service life of the fuel cell pack is prolonged, and the reliability of the fuel cell pack is improved. And the influence on the performance of the whole system due to overload or too fast loss of the single fuel cell is avoided.

The invention relates to the technical field of fuel cell control, and discloses a control method of a hydrogen fuel system controller, which comprises the following steps: in response to a system shutdown instruction, controlling a DC / DC converter to maintain operation and request a preset scavenging current, and consuming residual reaction gas in a flow channel and a pipeline by using an electrochemical power generation reaction in an electric pile; meanwhile, hydrogen-cut air-down operation is executed, and the average single voltage of the electric pile is collected in real time; when it is monitored that the average monomer voltage is reduced to be below a preset anti-corrosion safety threshold value, the DC / DC converter is controlled to be disconnected, and mechanical purging is started. A time sequence interlocking mechanism of electrochemical energy depletion and mechanical purging is established, a high-potential retention path in the shutdown process is blocked from the physical and chemical source, and the safety of the shutdown is improved. Reverse polarity corrosion caused by a hydrogen-air interface effect is avoided, and the service life of the galvanic pile is prolonged.

The invention relates to the technical field of fuel cells, in particular to an output voltage analysis method, device and equipment of a proton exchange membrane fuel cell, and the method comprises the following steps: establishing an equivalent circuit model, a gas flow model and a thermodynamic model of a target proton exchange membrane fuel cell; constructing a simulation model of the proton exchange membrane fuel cell according to the equivalent circuit model, the gas flow model and the thermodynamic model, and simulating stack starting based on the simulation model to obtain an output voltage change result and a temperature change result of the target proton exchange membrane fuel cell; and obtaining an output voltage analysis result of the target proton exchange membrane fuel cell according to the output voltage change result and the temperature change result. Therefore, the problems that modeling based on an electrochemical model is limited in applicable scene and low in modeling precision are solved, the applicable scene is wide, and the modeling precision is accurate.

The invention relates to the technical field of vehicle-mounted hydrogen fuel cells, and particularly provides a vehicle-mounted hydrogen fuel cell pack uncertainty quantification method and system, and the method comprises the steps: receiving an uncertainty quantification instruction, confirming an analysis environment, and constructing a running state association diagram; retrieving initial uncertainty influence factors based on an environment data set acquired by an external environment monitoring end, and confirming a target management strategy; updating the operation state association diagram and obtaining a fuel cell performance prediction sequence; key updating nodes are extracted to generate updating results, and dynamic quantization is achieved. According to the method, the operation data and the environment data are combined, the uncertainty management strategy is dynamically optimized, and the intelligent analysis and prediction capability of the operation state of the fuel cell stack is remarkably improved, so that the accuracy and efficiency of uncertainty management are enhanced.

The invention relates to the technical field of fuel cell monitoring and health management, and discloses an intelligent cooperative high-power proton exchange membrane fuel cell life prediction and fault diagnosis method and system, and the method comprises the following steps: S1, collecting multi-dimensional data of a stack key region in real time through a distributed sensing array; s2, removing noise and abnormal values of the collected data, and carrying out feature extraction; s3, constructing a dual-model collaborative prediction framework; s4, inputting the real-time features into the double models; s5, generating a maintenance strategy and early warning; according to the invention, special hardware accelerators such as a graphic processing unit or a field-programmable gate array are integrated on the edge computing module, so that the processing speed and efficiency of high-frequency and multi-source sensor data at a local end are remarkably improved; a distributed computing framework is adopted in a cloud analysis module to process mass feature data transmitted by an edge end, so that the capability and scale of the system for performing deep analysis on the health state of the fuel cell, accurately predicting the remaining service life and quickly tracing the fault are enhanced.

The invention provides a modeling method for SOFC electrode poisoning and performance degradation based on multiple factors, and belongs to the technical field of fuel cell numerical simulation. An SOFC three-dimensional geometric model is established, hypothesis conditions of the SOFC three-dimensional geometric model are established, physical parameters and boundary conditions of an SOFC are globally defined, physical field coupling setting is performed on a single cell geometric model of the SOFC, and the SOFC electrode poisoning and performance degradation modeling method based on multiple factors is established. Based on the Ni coarsening numerical model, the carbon deposition numerical model and the chromium poisoning numerical model, finally establishing a mathematical model of SOFC electrode poisoning and performance attenuation with multiple comprehensive factors; carrying out transient solution on different numerical models of the three-dimensional SOFC, and integrating the influence of various poisoning to obtain an SOFC electrode poisoning and performance attenuation rule; and based on the model, solving attenuation data of poisoning on electrochemical and mechanical properties of the SOFC to predict the residual service life of the SOFC.

The invention relates to the technical field of energy power, in particular to a fuel cell system based on a solid hydrogen pool and a hydrogen-electricity conversion efficiency determination method. The fuel cell system comprises a solid hydrogen pool module, a fuel cell module and a coupling module which are integrally assembled, the solid hydrogen pool module comprises a hydrolysis hydrogen releasing pool and a solid hydrogen storage component, the fuel cell module comprises an electrolytic tank and a proton exchange membrane, and the coupling module comprises a hydrogen conveying unit and a water circulation unit. The hydrogen conveying unit is used for conveying hydrogen released by the solid hydrogen storage component into the electrolytic tank for electrochemical reaction, and the water circulation unit is used for extracting an aqueous solution containing unelectrolyzed hydrogen in the electrolytic tank into the hydrolysis hydrogen release tank for hydrolysis hydrogen release reaction. The hydrogen element in the aqueous solution can be recycled for the second time in the hydrolysis hydrogen release tank and the electrolytic tank, so that the double hydrogen production effect is achieved, and the energy storage and hydrogen-electricity conversion efficiency is greatly improved.

The subject matter described herein generally relates to look ahead energy management and control systems and methods for detecting, incorporating, and leveraging look ahead technology data to improve the performance, durability, and life of fuel cell systems.

Provided in the present invention is a method for controlling intelligent health management of a fuel cell. The method comprises: taking, as control target values of a fuel cell, a current, a temperature, a hydrogen emission, an oxygen emission, a hydrogen reflux quantity, a hydrogen-side pressure and an oxygen-side pressure, which correspond to each of a calculated value of the total voltage of a fuel cell stack, a calculated value of a single-cell voltage mean square error of the fuel cell stack, a calculated value of a single-cell voltage change rate of the fuel cell stack and a calculated value of a single-cell voltage minimum amplitude of the fuel cell stack, or correspond to each of an optimized value of the total voltage of the fuel cell stack, an optimized value of the single-cell voltage mean square error of the fuel cell stack, an optimized value of the single-cell voltage change rate of the fuel cell stack and an optimized value of the single-cell voltage minimum amplitude of the fuel cell stack, and thus a membrane electrode is always in a "water balance" state, thereby improving the performance of cells, reducing the occurrence of cell faults, and improving the self-adaptability, reliability and stability of a fuel cell system.