The performance of proton exchange membrane fuel cells is very sensitive to temperature. The electrochemical reaction results directly in temperature variations in the proton exchange membrane fuel cell. Ensuring effe...The performance of proton exchange membrane fuel cells is very sensitive to temperature. The electrochemical reaction results directly in temperature variations in the proton exchange membrane fuel cell. Ensuring effective temperature control is crucial to ensure fuel cell reliability and durability. This paper uses active disturbance rejection control in the thermal management system to maintain the operating temperature and the stack inlet and outlet temperature difference at the set value. First, key cooling system modules such as expansion tanks, coolant circulation pumps and radiators based on Simulink were built. Then, physical modeling and simulation of the fuel cell cooling system was carried out. In order to ensure the effectiveness of the control strategy and reduce the parameter tuning workload, an active disturbance rejection control parameter optimization method using an elite genetic algorithm was proposed. When the optimized control strategy responds to input disturbances, the maximum overshoot of the system is only 1.23% and can reach stability again in 30 s, so the fuel cell temperature can be controlled effectively. Simulation results show that the optimized control strategy can effectively control the stack temperature and coolant temperature difference under the influence of stepped charging current without interference or with interference, and has strong robustness and anti-interference capability.展开更多
To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solve...To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱcould well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.展开更多
The influence of the dynamic parameters of a dual mass flywheel(DMF)on its vibration reduction performance is analyzed,and several optimization algorithms are used to carry out multiobjective DMF optimization design.F...The influence of the dynamic parameters of a dual mass flywheel(DMF)on its vibration reduction performance is analyzed,and several optimization algorithms are used to carry out multiobjective DMF optimization design.First,the vehicle powertrain system is modeled according to the parameter configuration of the test vehicle.The accuracy of the model is verified by comparing the simulation data with the test results.Then,the model is used to analyze the influence of the moment of inertia ratio,torsional stiffness,and damping in reducing DMF vibration.The speed fluctuation amplitude at the transmission input shaft and the natural frequency of the vehicle are taken as the optimization objectives.The passive selection method,multiobjective particle swarm optimization,and the nondominated sorting genetic algorithm based on an elite strategy are used to carry out DMF multiobjective optimization design.The advantages and disadvantages of these algorithms are evaluated,and the best optimization algorithm is selected.展开更多
文摘The performance of proton exchange membrane fuel cells is very sensitive to temperature. The electrochemical reaction results directly in temperature variations in the proton exchange membrane fuel cell. Ensuring effective temperature control is crucial to ensure fuel cell reliability and durability. This paper uses active disturbance rejection control in the thermal management system to maintain the operating temperature and the stack inlet and outlet temperature difference at the set value. First, key cooling system modules such as expansion tanks, coolant circulation pumps and radiators based on Simulink were built. Then, physical modeling and simulation of the fuel cell cooling system was carried out. In order to ensure the effectiveness of the control strategy and reduce the parameter tuning workload, an active disturbance rejection control parameter optimization method using an elite genetic algorithm was proposed. When the optimized control strategy responds to input disturbances, the maximum overshoot of the system is only 1.23% and can reach stability again in 30 s, so the fuel cell temperature can be controlled effectively. Simulation results show that the optimized control strategy can effectively control the stack temperature and coolant temperature difference under the influence of stepped charging current without interference or with interference, and has strong robustness and anti-interference capability.
基金Supported by the National"Thirteenth Five-year Plan"National Key Program(2016YFD0701301)the Heilongjiang Provincial Achievement Transformation Fund Project(NB08B-011)。
文摘To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱcould well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.
基金National Natural Science Foundation of China,Grant/Award Number:52075388。
文摘The influence of the dynamic parameters of a dual mass flywheel(DMF)on its vibration reduction performance is analyzed,and several optimization algorithms are used to carry out multiobjective DMF optimization design.First,the vehicle powertrain system is modeled according to the parameter configuration of the test vehicle.The accuracy of the model is verified by comparing the simulation data with the test results.Then,the model is used to analyze the influence of the moment of inertia ratio,torsional stiffness,and damping in reducing DMF vibration.The speed fluctuation amplitude at the transmission input shaft and the natural frequency of the vehicle are taken as the optimization objectives.The passive selection method,multiobjective particle swarm optimization,and the nondominated sorting genetic algorithm based on an elite strategy are used to carry out DMF multiobjective optimization design.The advantages and disadvantages of these algorithms are evaluated,and the best optimization algorithm is selected.
