In order to prolong proton exchange membrane fuel cell （PEMFC） lifespan， it is necessary to design a state observer to monitor the internal states， and control the internal states at the expected level through feedback control. Since the anode hydrogen concentration directly determines the output performance of PEMFC， and circulation of anode hydrogen as well as nitrogen accumulation caused by diffusion across membrane leads to the difficulty of anode gas concentration estimation. Therefore， this paper focuses on the cutting-edge technology of PEMFC anode gas concentration estimation， and the existing problems as well as the future development trend of existing research are also described， hoping to make contributions to the research of gas， water and heat management for PEMFC.

A one-dimensional transient multiphase fuel cell model and a global sensitivity analysis model were established to investigate the effects of operating temperature， relative humidity， stoichiometry， and pressure on output performances. The result shows that the variation of operating temperature leads to more significant performance fluctuations at high load conditions. Increasing gas pressure and stoichiometry is beneficial to performance enhancement. The membrane dehydration phenomena caused by low relative humidity becomes more severe in small current density regions. Based on the quantitative sensitivity analysis， the nine operating parameters are classified as very sensitive， rather sensitive， and not sensitive parameter. With current density rising， cathode stoichiometry， anode inlet relative humidity， and cathode inlet relative humidity are more sensitive， which also show a typically upward trend.

Aiming at the requirement of sufficient gas supply and rapid response to hydrogen pressure in the hydrogen supply system of proton exchange membrane fuel cell, a matching design method for the components of the hydrogen supply system was proposed. First， the method of mechanism modeling and semi-empirical modeling was used to establish the lumped parameter model of the hydrogen supply system of proton exchange membrane fuel cell， and the selection scheme of the hydrogen supply system was proposed. In order to verify the rationality of the selection scheme， a simulation was carried out for an 80 kW stack. The results show that under the action of the feedforward+PI control strategy, the anode pressure rise time from the idle speed step to the rated operating condition is 0.5 s, and the overshoot is 3 kPa. The pressure fluctuation under the action of hydrogen discharge disturbance is less than 0.8 kPa; The ejector ratio of the ejector is 2.14. The flow rate of the pressure relief valve is sufficient to ensure the safe operation of the stack. The matching method meets the requirements of the dynamic response, hydrogen circulation and safety characteristics of the hydrogen supply system, and provides a theoretical basis for the selection and matching of the hydrogen supply system of the proton exchange membrane fuel cell.

Aiming at the problem of optimal oxygen excess ratio （OER） control method of vehicle proton exchange membrane fuel cell （PEMFC） system， a control oriented third-order nonlinear air system model based on 60 kW PEMFC system was constructed， and dynamic feedforward+PI controller based on steady-state operating point approximate linearization model and feedforward/feedback linearization controller based on global linearization model were designed respectively. The simulation results show that the feedforward/feedback linearization method solves the problem of steady-state error in OER response due to model error based on approximate linearization model control method， and eliminates the influence of load current change on OER response by introducing nonlinear feedforward structure. In addition， the feedforward/feedback linearization control method can track the best OER under different working conditions， and effectively improve the efficiency of PEMFC system.

In order to meet the requirements of the power performance and load capacity of the fuel cell hybrid electric tram， a parameter configuration method based on the fish swarm optimization algorithm was proposed. Based on the tram dynamics model， the traction calculation under three driving states is completed， the fuel cell/lithium battery/supercapacitor hybrid power system is selected， and the multi-objective function with the smallest system volume and mass is established. The algorithm solves the configuration number of fuel cells， lithium batteries and super capacitors， and analyzes the parameter configuration results and the dynamic performance verification. The results show that compared with the particle swarm method， the method proposed in this paper has faster convergence speed， smaller volume and mass， higher power density and energy density， and makes full use of the transmission advantages of each power source to ensure the stable operation of trams.

In order to improve the operation efficiency of fuel cell， its working temperature must be effectively controlled. In this paper， the thermal management system model is built on the Matlab/Simulink platform. The model can be used to analyze the flow distribution， pressure loss and heat exchange between various parts. Based on this model， an adaptive linear quadratic regulator （LQR） controller is proposed and compared with LTI-LQR and LQI controllers under different working conditions. The simulation results show that under the step response test， the adaptive LQR controller has no steady-state error， no overshoot and the rising time is 15 s； under the dynamic performance test， the stack outlet temperature can quickly track the reference value in the global range， which fully reflects the advantages of the controller.

In view of the current high-power fuel cell system, a high-power fuel cell test platform was designed and developed, and the performance test of the 120 kW fuel cell was completed through the design, selection, construction, and debugging of the system. The test platform integrates hydrogen flow， air flow and thermal management control systems, integrates and optimizes the layout between the various subsystems, and makes post-maintenance more convenient.At the same time, the test platform uses LABVIEW software to design the control interface of the upper computer, uses Simulink software to design the control program of the lower computer controller and writes the determined parameters of the components into the controller, and then realizes the communication through the CAN box between the upper computer and the controller. The online real-time control of the system by the host computer control interface, and can automatically optimize the operating parameters of each component according to the load change. By analyzing the test data collected by the test platform, it is possible to evaluate whether the fuel cell system meets the expected design requirements. The test platform has certain guiding significance for the research and production of high-power fuel cell systems and the development of fuel cell test platforms, and provides a guarantee for the development of fuel cell systems.

Combined with the temperature change and icing in the process of fuel cell cold start， a control oriented third-order fuel cell cold start model was established. Aiming at the unmeasurable ice volume fraction of cathode and anode， an ice volume fraction estimation method based on extended state observer was proposed. On this basis， according to the characteristics of constraint and coupling nonlinearity in the cold start process of fuel cell， an optimal control method of fuel cell cold start system based on nonlinear model predictive control was proposed， which realizes the double optimization objectives of improving the rapidity of cold start system and reducing hydrogen consumption. Finally， simulation experiments verify the effectiveness of the designed optimal control system of cold start system.

A kind of propulsion system of a 2.5-ton fuel cell hybrid forklift was matched， which mainly consists of a 10 kW fuel cell system and a 25 A·h lithium battery pack （2 kW·h）. And a power following energy management strategy of state recognition was established. The forklift model was created and simulated to verify the system and strategy. The results show that decreasing the keeping state of charge could improve the fuel economy and a reasonable switching state of charge could improve the stability of the system. The rated power of fuel cell system should be at least 15% higher than the average power under working conditions. During the dynamic process of state of charge decline after startup， the large capacity of battery could reduce the average power of the fuel cell system then improve the fuel economy. On the contrary， it wouldn't support the system to work a long time and could lead to an extreme state of charge volatility which would shorten its service life.

To study the effect of porosity in the gas diffusion layer （GDL） of a proton exchange membrane fuel cell on the anisotropic transport properties， stochastic numerical method was used to reconstruct the 3-D microstructure of the Toray TGP-H GDL. The anisotropy of carbon fiber distribution and all phases in the gas diffusion layer including pores， carbon fibers， binder， and polytetrafluoroethylene （PTFE） were taken into account in the reconstructed structure. The pore-scale model was employed to obtain the relationship between porosity and effective gas diffusivity， tortuosity， liquid water permeability， effective electrical and thermal conductivity in the in-plane and through-plane. The results show that the porosity has a significant effect on the transport properties， and the transport properties of the Toray TGP-H GDL have obvious anisotropy in the in-plane and through-plane.

Adaptive neural network （NN） output feedback optimal control design problem and stability analysis were studied for nonlinear lithium battery systems based on the second-order resistor-capacitor （RC） equivalent circuit model. Firstly， NN was used to approximate the uncertain nonlinear dynamic of the controlled system， and a time-varying gain nonlinear observer was designed to solve the unmeasurable problem of battery resistance and capacitance voltage and state of charge （SOC）. Under the framework of Actor-Critic network， an observer-based adaptive optimal NN control algorithm was designed. According to the Lyapunov stability theorem， it is proved that all signals of the closed-loop system are semi-global uniformly ultimately bounded （SGUUB）. Finally， the effectiveness of the proposed optimal control theory was verified by simulation.

In order to improve the hydrogen utilization rate of fuel cell engines， a fuel cell anode loop model was developed based on the AMESim platform,and completed model integration and calibration. Based on practical engineering experience, a new purging strategy that correlates anode loop temperature, pressure and reaction current was proposed. Using the anode loop model of the fuel cell engine, the parameter optimization boundary was calculated, and the optimization and fitting of the purge control parameters was completed. According to the simulation results， compared with the original purge strategy， the new strategy can make the hydrogen consumption rate decrease from 0.866 kg to 0.8 kg per 100 km under NEDC cyclic condition， and the theoretical economy can be improved by 7.5%. Then the new strategy was verified on a fuel cell vehicle， and the hydrogen consumption rate is 0.81 kg per 100 km， which is basically consistent with the simulation results. The driving range of the vehicle is increased from 484 km to 510 km， by 5.4%. And the effectiveness of model-based purge strategy optimization scheme is verified.

In order to improve the operating economy of the fuel cell bus, the real vehicle operating conditions were selected, and the simulation solution of the dynamic programming energy management of the fuel cell bus was completed on the basis of the mathematical model of the fuel cell, lithium battery and power system. By transforming the solution results into fuzzy control rules and formulating fuzzy control strategies, an energy management method based on working condition update to construct variable fuzzy rules was proposed. Taking the equivalent hydrogen consumption per 100 kilometers and the change of state of charge （SOC） as indicators， the control effects of the real vehicle energy management strategy and the fuzzy control strategy applied to the real vehicle were compared. Finally， the influence of the initial SOC of the lithium battery of the fuel cell bus on the equivalent hydrogen consumption of 100 kilometers for long-distance driving under different strategies was compared. The results show that the fuzzy control strategy based on the dynamic planning results of the actual vehicle operating conditions reduces the equivalent hydrogen consumption per 100 kilometers by 3.97% compared with the actual vehicle energy management strategy.

Aiming at the cathode flow control problem of proton exchange membrane fuel cells， considering the dependence of existing model-based control schemes on system dynamics and the influence of unmodeled dynamics on system control performance， a new data-driven model-free adaptive control scheme was designed. Firstly， the non-parametric dynamic linearization technology was used to obtain the dynamic linearized data model of cathode flow， which can realize the adaptability and robustness to the internal parameter perturbation and external perturbation of the controlled system. On the basis of this data model, an adaptive second-order sliding mode controller was designed to improve the transient quality and steady-state accuracy of the system. In addition， under the Lyapunov stability framework， it is proved that the cathode flow system finally enters a convergent quasi-sliding mode driven by the proposed control law. Finally， the parameters in the model-free adaptive sliding mode algorithm are determined by quantum particle swarm optimization. The effectiveness of the proposed control system is verified under multiple operating conditions.

A coordinated optimal dispatch strategy of wind and photovoltaic generation and new energy vehicles was proposed for the problem， which is caused by large-scale new energy vehicles disorderly charging in power grid. Firstly， the model of new energy vehicles power battery was built， and a calculation method of charging load based on Monte Carlo method was proposed. Secondly， based on the principle of wind and photovoltaic power generation， the mathematical models of wind power generation and photovoltaic power generation were established. Finally， The objective function of the dispatch strategy is to minimize the standard deviation of the comprehensive load of the power grid， and the active set algorithm was utilized to solve the quadratic programming problem to obtain the coordinated optimal dispatch strategy. The simulation results show that the strategy can effectively avoid the aggravating difference between peak and valley load， which is caused by large-scale new energy vehicles disorderly charging in power grid.

Long short-term memory (LSTM） multi-classification algorithm can effectively realize the online intelligent fault diagnosis of vehicle fuel cells. However， in practical applications， the internal characteristics of vehicle fuel cells will decline with the increase of operating time， and the initial diagnosis model may not be able to meet long-term fault conditions. Aiming at this problem, PEMFC original and decay models based on AVL CURISE M software were built, and fault data was generated using the models. Then， an adaptive algorithm was designed， and the data generated by the model was used for adaptive training， so that the diagnosis model can adapt to the decline of the stack and ensure the accuracy of the online intelligent diagnosis of vehicle fuel cells. Based on this scheme， the actual fuel cell system has been tested and verified， which proves the effectiveness of the scheme. This scheme can adaptively update the weight of the diagnosis algorithm of the fuel cell system based on the "vehicle-cloud" platform and complete the aging of the stack. It has a good application prospect.

In order to improve the power performance and economy of fuel cell vehicles， a fuel cell vehicle based on AVL-CRUISE was modeled， and the energy management strategy of fuzzy control based on Simulink was established. Then， the fixed gear was optimized into a two-speed AMT gearbox based on Isight/Cruise co-simulation. Simulation results show that the established fuzzy control energy management strategy is effective. Compared with the rule-based energy management strategy， the economy is improved by 16.4% and 8.5% respectively under NEDC and WLTP conditions. Compared with the fuel cell vehicle without optimized transmission ratio based on fuzzy control， the fuel cell vehicle with optimized transmission ratio based on fuzzy control has improved economy by 1.1% and 2.8% respectively under NEDC and WLTP conditions.

In order to solve the energy management problems of an electric vehicle based on fuel cell and ultracapacitor. Firstly， the models of fuel cell and ultracapacitor are established， including the performance degradation model of fuel cell. Secondly， an optimal energy management control strategy based on power following strategy is proposed. The required power is decomposed into the target power of fuel cell and ultracapacitor by partial differentiation of quadratic utility function and combined with Karush-Kuhn-Tucker conditions. Finally， the multi-objective artificial bee colony algorithm and Pareto solution set iterative algorithm are used to solve the internal optimal balance coefficient， and the vehicle economy and fuel cell durability are both improved. The simulation results show that compared with the traditional power following strategy， the proposed optimal-power following strategy can reduce the equivalent hydrogen consumption by 2%， reduce the fuel cell degradation by 92.66%， the vehicle can travel 88.52 km and only 1.2 kg hydrogen is consumed.

Aiming at the phenomenon of ice accretion and shutdown caused by the imbalance of water and heat during the cold start-up of proton exchange membrane fuel cell， a numerical model for heat transfer and temperature calculation of the cell is established based on the study of the mechanism of heat generation and heat transfer characteristics of the system. The evolution law of heat generation and heat dissipation of the system under different ice volumes is analyzed， the water and heat balance conditions for predicting cold start shutdown is established， and the effects of system parameters such as current densities on start-up performance are studied. The results show that when the heat generation and heat dissipation of the system are the same， the heat generation of the system will be lower than the latent heat of ice melting， resulting in shutdown. The high current density can increase temperature of the membrane electrode and reduce the heat transfer to the outer layer， which is beneficial to shorten the cold start time.

Aiming at the problem that the mechanical properties of the membrane electrode structure of the proton exchange membrane fuel cell are complex, which makes the simulation analysis results in engineering difficult to match the real situation, the complex mechanical behavior of the gas diffusion layer in the membrane electrode structure was described, and then the accuracy of fuel cell mechanics simulations was improved. First, an improved model of the microstructure of the gas diffusion layer was proposed, and the model parameters were determined by means of experiments. Then, the finite element simulation verification was carried out in Abaqus using the UMAT user subroutine. The experimental and simulation results show that the improved mechanical model can better simulate the nonlinear mechanical behavior of the gas diffusion layer, improve the accuracy of fuel cell engineering simulation, and can be applied to the engineering analysis of fuel cell assembly and mechanical life prediction.

A new decoupling control strategy was proposed for the air supply system of proton exchange membrane fuel cell （PEMFC）， which solves the problem of the cooperative control of the air flow rate and pressure， then realizes the high precision control of the both. A linear variable parameter model of the air flow rate and pressure was established， based on the model， a control scheme combining sliding mode observer and state feedback controller was designed， in which the gain value of state feedback was solved by linear quadratic regulator （LQR）. The control scheme is verified on PEMFC cathode experimental platform. The high precision control of the air flow rate and pressure can be realized， and the decoupling control of flow and pressure can be achieved.

For the thermal management system of fuel cell vehicle engine， taking the optimal temperature value at the inlet and outlet of the stack under different operating conditions as the objective function and considering the interference of components and environment， the simulation model based on parameter estimation is established， and the thermal management control strategy based on adaptive control is designed. The problems of thermal management interference uncertainty and system state hysteresis of fuel cell engine are improved. The simulation results show that the thermal management strategy has good adaptability under different reactor operating conditions， and can achieve the goal of stabilizing the inlet and outlet temperature of the reactor at the set value and minimizing the change of the system temperature when the large and small cycles are turned on.

In order to improve the dynamic response performance of the air system of the proton exchange membrane fuel cell， the one-dimensional simulation model of the fuel cell system was established by AMESim software. The process of the air dynamic response of the fuel cell was simulated， and the air dynamic control strategy was optimized. The optimized control strategy was tested and verified on the fuel cell system bench. The test results showed that the new control strategy successfully achieved the decoupling of air pressure and flow， which greatly reduced the air starvation and avoided the low voltage of the stack during the dynamic loading process of the fuel cell system. The new control strategy improves the dynamic response rate of the fuel cell system.

To improve the efficiency and lifetime of the proton exchange membrane fuel cell（PEMFC）， multiple states of the fuel cell were accurately controlled. Firstly， a 12-order fuel cell model is established in Simulink， and the modeling parameters are fitted using experimental data， and the maximum error of the simulation model output voltage is 3.68%. Secondly， a 3-input and 3-output sliding mode controller is designed based on the established model to control the stack oxygen excess ratio， cathode and anode gas pressure difference and temperature through the compressor voltage， anode inlet gas flow and cooling water flow， respectively. The simulation results show that compared with the PI controller， the designed sliding-mode controller shortens the transition time of oxygen excess ratio control by 3 seconds when the load current changes abruptly， reduces the fluctuation of cathode and anode pressure difference from 40 Pa to about 2 Pa， and controls the temperature fluctuation within 0.05 K.

A long-term and short-term memory neural network （LSTM） based on particle swarm optimization （PSO） was proposed to predict the life of PEMFC. First， the degradation mechanism of PEMFC was analyzed. Then， the voltage degradation prediction model was established by using LSTM neural network and the Dropout layer was used to prevent overfitting to improve the generalization ability of the model. In addition， PSO was used to optimize the learning rate and Dropout rate in LSTM to improve the prediction effect. Finally,the actual aging data of IEEE 2014 Data Challenge Data fuel cell were used to verify. The results show that this method can accurately predict the degradation of fuel cells， and the prediction accuracy is improved by 50% compared with the traditional LSTM.

To address the problem that the internal state variables of the Proton Exchange Membrane Fuel Cell （PEMFC） system are difficult to be measured directly by sensors， which makes it impossible to troubleshoot the PEMFC system and design a model-based controller， the High Order Sliding Mode （HOSM） observer was designed. Firstly， an 11th-order anode dead-end PEMFC system model suitable for observation was built. Then，on the basis of this model， the HOSM observer was designed by adjusting the errors between the measured sensor values （stack voltage， compressor outlet flow rate and stack temperature） and the estimated values by the HOSM algorithm. Finally, the performance of the designed HOSM observer was compared with the performance of the traditional sliding mode observer. The comparison results of the observation effect and the squared error integral show that the designed observer can accurately observe each state quantity and has better observation performance.