Abstract:Proton exchange membrane fuel cell (PEMFC) has problems such as unstable output voltage and low power generation efficiency, so Boost circuit is needed to ensure voltage quality and meet system requirements. According to the output characteristics of PEMFC, the mathematical models of PEMFC and Boost circuit are built on the Matlab/Simulink platform. Considering that linear active disturbance rejection control (LADRC) has excellent dynamic response speed to disturbance, a fuzzy neural network-linear active disturbance rejection control (FNN-LADRC) is proposed for voltage loop control of Boost circuits. The key parameters of the linear active disturbance rejection controller are tuned by FNN-LADRC to realize real-time optimization of the controller. The simulation analysis compares the performance difference of output voltage between the FNN-LADRC control strategy and the LADRC control strategy under different working conditions. The results show that the regulation time under the FNN-LADRC control strategy is 5 ms and the regulation time under the LADRC control strategy is 40 ms without disturbance. The FNN-LADRC control strategy has faster adjustment time and stronger anti-interference ability under disturbed conditions. Combined with IAE index and ITAE index, the effectiveness and superiority of the proposed control strategy are verified.

Abstract:Aiming at the problem that the dynamic performance of the fuel cell air supply system is susceptible to load changes and external environmental factors, a tandem-type control strategy combining super-helical sliding mode control and terminal sliding mode control is designed. A control-oriented fifth-order dynamic model is established, and the control problem of tracking the optimal oxygen excess ratio, maximum net output power and cathode pressure measurement of the air supply system during load change is proposed; the optimal expectation value is extracted and the controller is designed, and the closed-loop stability is verified using the Lyapunov method. Simulation analysis shows that the constructed observer perturbation estimate is within 0.01% error from the theoretical actual value. Compared with PID control, the response time of tracking the optimal expected value of the oxygen excess ratio is improved by 6.9%, the maximum net power output is increased by 0.2%, and the response time of cathode pressure is improved by 60%. From the results, it can be concluded that the strategy of this paper can effectively control the oxygen excess ratio of the gas supply system to track the optimal desired value and output the maximum net power when the load changes, can accurately and rapidly estimate the cathode pressure, can better estimate the perturbation, and has a strong anti-disturbance ability.

Abstract:In the face of gradient operation conditions, the development of control strategies that simultaneously take into account intervehicle cooperative control and energy economy is one of the key technologies for improving traffic efficiency and exploiting the energysaving potential of vehicles. A hierarchical optimization control strategy based on improved particle swarm optimization algorithm and Q-learning for fuel cell hybrid electric vehicles queue is proposed with the objective of safe driving and optimizing energy consumption. In this strategy, the upper layer controller uses the improved particle swarm optimization algorithm to obtain the energy-saving speed trajectory under the premise of ensuring that safety constraints such as distance or speed limit from the preceding vehicle are satisfied, and utilizes the model predictive control framework to adjust the vehicle speed in real time to ensure the vehicle follows the energy-saving speed trajectory. The lower layer controller builds the Q-learning controller based on the information such as vehicle speed and demand power solved by the upper layer to realize the optimal energy distribution between the fuel cell hybrid electric vehicles power cell and the fuel cell. Simulation results show that the hierarchical control strategy proposed in this paper exhibits good tracking performance and safety performance under slope conditions, and the optimization results are similar to the dynamic planning strategy, indicating the energy consumption economy and feasibility of the strategy.

Abstract:In order to solve the energy management problem of fuel cell sightseeing vehicle, the model of fuel cell sightseeing vehicle and its components are established, and the thermostat control strategy is designed. The accuracy of the model is verified by the bench test. In order to improve the economy and durability of the vehicle, a novel strategy based on quadratic utility function is proposed, and the real-time maximum benefit is obtained by KKT condition. Finally, a multi-objective lightning search algorithm is designed, and the optimal parameters of the novel strategy are solved by the algorithm. Simulation results show that compared with thermostat control, the proposed strategy can improve the driving range by 1.7% and durability by 11.2%. In addition, the strategy takes into account the SOC and combines the historical output power information of the vehicle and its components. The novel strategy also has a strong adaptability to working conditions.

Abstract:In proton exchange membrane fuel cell (PEMFC) life prediction, the unknown degree of influence of the characteristics in the fuel cell on its life makes the problem of predicting the remaining life of the fuel cell relatively complex. In order to more accurately predict the remaining service life of the fuel cell. In this paper, the original stack voltage was de-noised by wavelet analysis to filter the noisy data. pearson correlation coefficient (PCC) was used to reduce the dimension of influencing factors, extract key influencing factors, and simplify the model structure. Then, the improved sparrow search algorithm (ISSA) is used to optimize the BP neural network, find the optimal weights and thresholds of the network, and establish the ISSA-BP model. Finally, the processed data is input into the ISSA-BP model to predict the remaining life of PEMFC.The experimental results show that the average absolute error percentage, average absolute error, and root mean square error of PCC-ISSA-BP are 0.125%, 0.003 97, and 0.005 68, respectively, which are better than other models and can more effectively predict the remaining life of fuel cells.

Abstract:Energy management strategy (EMS) is a key technology to improve the economy and durability of fuel cell electric vehicles. To overcome the shortcomings of most existing strategies, a new EMS based on quadratic utility function (QUF) is proposed in this paper. Mathematical models of vehicle and components are established, and the accuracy of the models is verified by bench test. QUF is used to decompose the demand power into the output power of fuel cells and batteries, the strategy considers the historical output state and load change rate of the battery and fuel cell. In order to improve the durability of power sources and the economy of the vehicle, the coefficients of the QUF are determined and optimized by using the Nash equilibrium. Simulation results show that the proposed strategy can effectively improve the durability of fuel cells and batteries, reduce hydrogen consumption, and extend driving range. Compared with the FSM strategy, this strategy can reduce the degradation of the batteries by 21.15%. Compared with the fuzzy control strategy, the proposed strategy can reduce the degradation of fuel cells by 36.52%.

Abstract:Aiming at the problems of the insensitivity and inaccuracy of conventional controllers when the prpton exchange membrane fuel cell (PEMFC) power is high and the load current changes dynamically, a variable universe fuzzy PID control strategy was proposed in this paper. The proportion factor and scale factor in the fuzzy PID controller can be adjusted in real time by the factor to realize the contraction-expansion of the fuzzy universe, thereby improving the stability of the control. Build a 55KW fuel cell model and verify the feasibility of the model. Comparative experiments between the traditional control strategy and the variable universe fuzzy PID controll strategy. The research results indicate that the variable universe fuzzy PID controller is superior to the traditional controller in the overshoot, the steady-state error, the adjustment time, and the anti-interference performance.

Abstract:Proton exchange membrane fuel cell (PEMFC) output performance is particularly important. The existing PEMFC model is complex and the internal description is not detailed. Based on Srinivasan model, ohmic polarization voltage, concentration difference polarization voltage, activation polarization voltage, their detailed models and output voltage models are established to analyze their relationship with current density. The stack temperature, hydrogen pressure, oxygen pressure and limit electricity are studied by control variable method Influence of key parameters such as current on cell voltage. Considering the practical application of PEMFC, a buck converter with voltage and current double closed-loop control is designed. Based on the state space average method, the s-domain small signal model of the converter is established, the transfer function of the controlled object is obtained, and the compensation controller is added to improve the output voltage performance. PSCAD / EMTDC simulation is used to verify the effectiveness and correctness of the model and control strategy. The results show that the output voltage increases with the increase of gas (H2, O2) pressure, electric push temperature and limit current. When the rated voltage (70V) is input, the adjustment time is 5.5ms to reach the target value (35V), when the load changes from 30 Ω to 15 Ω, the peak of disturbance voltage is 5.4v, and the time to stabilize at the target value is 64ms. The strategy has good stable / dynamic characteristics, correct and effective, and has certain reference significance for engineering design.

Abstract:In order to optimize the performance of the fuel cell system for vehicles and decrease the fluctuation of the DC bus voltage, proposes a hierarchical energy management strategy based on adaptive moving average filter, equivalent cost minimum and nonlinear control of the energy state of super capacitors. Firstly，construct the fuel cell vehicle power system model and the mathematical model of hydrogen consumption and degradation cost; Then, the adaptive moving average filter and the equivalent cost minimum strategy are used to optimize the output power of fuel cell ; And the super capacitor energy state is controlled by a nonlinear control strategy within a reasonable range, to improve the dynamic power output capability of the super capacitor and suppress the fluctuation of the DC bus voltage. The simulation experiment results show that compared with the power following strategy, the hydrogen consumption is reduced by 12.94%, the durability of the fuel cell is improved by 12.6%, the total cost is reduced by 12.63%, and the DC bus voltage fluctuation is significantly decreased, which indicates that the proposed energy can optimize the hydrogen consumption and durability of the fuel cell, and can improve the stability of the bus voltage.

Abstract:A multi-objective optimization method for energy management strategy (EMS) of the fuel cell hybrid electric vehicle (FCHEV) is proposed to improve the efficiency of the drive system and optimize the durability of fuel cell. The equivalent hydrogen consumption model of the hybrid power system is established according to the power flow and the efficiency characteristics of key components. In addition, the lifetime degradation of fuel cell based on the load variation is considered. The energy management system is achieved by presenting an intelligent power allocation method, that is, the control strategy based on fuzzy logic control (FLC). In further research, in order to ameliorate the energy management strategy, the parameters of the fuzzy controller are optimized with the assistance of genetic algorithm (GA). A multi-objective optimization problem which takes equivalent fuel consumption and fuel cell lifetime as optimization targets is proposed. The improved fast non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem, so as to optimize the control parameters. Finally, the optimization results of the above algorithm are tested, and the optimized strategy and other strategies are simulated by the advanced vehicle simulator (ADVISOR) under typical conditions. The results demonstrate that the optimization is effective, and the optimized control strategy has a considerable degree of superiority.