TY - JOUR
T1 - Boosting the output power of PEM fuel cells by identifying best-operating conditions
AU - Wilberforce, Tabbi
AU - Olabi, A.G.
AU - Rezk, Hegazy
AU - Abdelaziz, Almoataz Y.
AU - Abdelkareem, Mohammad Ali
AU - Sayed, Enas Taha
PY - 2022/10/15
Y1 - 2022/10/15
N2 - Voltage, as well as current from proton exchange membrane fuel cells (PEMFCs), is reliant on various working and structural parameters, such as operating pressure, temperature, humidity, and membrane thickness. Optimizing such operating and structural parameters will significantly improve the cell's power output. Therefore, the primary goal of the investigation is to determine the best condition to boost the output power of PEMFCs. Firstly, experimental work has been done to obtain a data set. ANSYS software has been built and used to simulate the input–output characteristics of the PEMFC at different operating and structural parameters. Secondly, fuzzy logic is applied to create an accurate model of PEMFC with the aid of generated data sets obtained from ANSYS. Furthermore, response surface methodology (RSM) is also used to simulate the cell performance, and the results were compared with those obtained by the fuzzy model. Finally, the particle swarm optimization (PSO) algorithm was utilised to identify the best parameters for the cell. During the optimization process, the operating pressure, temperature, humidity, and membrane thickness are used as the design variables, whereas the output power of PEMFC is the objective function that needs to be maximized. The main finding proved the dominance of the combination of fuzzy modelling coupled with PSO. The output power increased by 5.26 % and 9.38% compared with the RSM and the measured data, respectively.
AB - Voltage, as well as current from proton exchange membrane fuel cells (PEMFCs), is reliant on various working and structural parameters, such as operating pressure, temperature, humidity, and membrane thickness. Optimizing such operating and structural parameters will significantly improve the cell's power output. Therefore, the primary goal of the investigation is to determine the best condition to boost the output power of PEMFCs. Firstly, experimental work has been done to obtain a data set. ANSYS software has been built and used to simulate the input–output characteristics of the PEMFC at different operating and structural parameters. Secondly, fuzzy logic is applied to create an accurate model of PEMFC with the aid of generated data sets obtained from ANSYS. Furthermore, response surface methodology (RSM) is also used to simulate the cell performance, and the results were compared with those obtained by the fuzzy model. Finally, the particle swarm optimization (PSO) algorithm was utilised to identify the best parameters for the cell. During the optimization process, the operating pressure, temperature, humidity, and membrane thickness are used as the design variables, whereas the output power of PEMFC is the objective function that needs to be maximized. The main finding proved the dominance of the combination of fuzzy modelling coupled with PSO. The output power increased by 5.26 % and 9.38% compared with the RSM and the measured data, respectively.
KW - PEM fuel cell
KW - response surface methodology (RSM)
KW - Fuzzy modelling
KW - Optimization
KW - Energy efficiency
UR - https://linkinghub.elsevier.com/retrieve/pii/S0196890422009827
UR - http://www.scopus.com/inward/record.url?scp=85138210197&partnerID=8YFLogxK
U2 - 10.1016/j.enconman.2022.116205
DO - 10.1016/j.enconman.2022.116205
M3 - Article
SN - 0196-8904
VL - 270
JO - Energy Conversion and Management
JF - Energy Conversion and Management
M1 - 116205
ER -