TY - JOUR
T1 - Analysis and optimization of a 2-kW PEMFC-CHP system under different operating modes
AU - Ning, Wenjing
AU - Chen, Li
AU - Liao, Peng Cheng
AU - Tao, Wen Quan
N1 - Publisher Copyright:
© 2026 Taylor & Francis Group, LLC.
PY - 2026
Y1 - 2026
N2 - This study presents a comprehensive analysis of a 2 kW proton exchange membrane fuel cell-based combined heat and power (PEMFC-CHP) system operating under different electrical and thermal following modes across six representative energy demand profiles. The system’s operational and structural parameters are thoroughly examined, and a multi-objective optimization is conducted by integrating an artificial neural network (ANN) with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Results indicate that, in winter, the constant power-thermal following mode meets the thermal demand but shortens the PEMFC lifespan due to frequent start-stop cycles. The optimized leveled distributed power mode effectively mitigates shutdowns, achieving a system efficiency (EffCHP) of 0.9784 and a matching degree (φ) of 0.8763, while maintaining stable thermal performance in winter and mid-season. The EffCHP shows an overall improvement of 7% compared with previous studies. In summer, the system operates in electrical-following mode due to reducedthermal demand. According to EWM-TOPSIS analysis, the optimal Pareto solutions achieve φ of 0.9270 and EffCHP of 0.9796 in winter, and φ of 0.9883, EffCHP of 0.9574, with hydrogen consumption of 1.9 kg in summer, confirming superior efficiency and operational coordination.
AB - This study presents a comprehensive analysis of a 2 kW proton exchange membrane fuel cell-based combined heat and power (PEMFC-CHP) system operating under different electrical and thermal following modes across six representative energy demand profiles. The system’s operational and structural parameters are thoroughly examined, and a multi-objective optimization is conducted by integrating an artificial neural network (ANN) with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Results indicate that, in winter, the constant power-thermal following mode meets the thermal demand but shortens the PEMFC lifespan due to frequent start-stop cycles. The optimized leveled distributed power mode effectively mitigates shutdowns, achieving a system efficiency (EffCHP) of 0.9784 and a matching degree (φ) of 0.8763, while maintaining stable thermal performance in winter and mid-season. The EffCHP shows an overall improvement of 7% compared with previous studies. In summer, the system operates in electrical-following mode due to reducedthermal demand. According to EWM-TOPSIS analysis, the optimal Pareto solutions achieve φ of 0.9270 and EffCHP of 0.9796 in winter, and φ of 0.9883, EffCHP of 0.9574, with hydrogen consumption of 1.9 kg in summer, confirming superior efficiency and operational coordination.
KW - artificial neural network
KW - combined heat and power
KW - multi-objective optimization
KW - Proton exchange membrane fuel cell
KW - thermal following mode
UR - https://www.scopus.com/pages/publications/105035433026
U2 - 10.1080/15435075.2026.2654983
DO - 10.1080/15435075.2026.2654983
M3 - 文章
AN - SCOPUS:105035433026
SN - 1543-5075
JO - International Journal of Green Energy
JF - International Journal of Green Energy
ER -