Power optimization of a combined power system consisting of a high-temperature polymer electrolyte fuel cell and an organic Rankine cycle system. (15th October 2016)

• Record Type: Journal Article
• Authors: Lee, Won-Yong; Kim, Minjin; Sohn, Young-Jun; Kim, Seung-Gon
• Is Part Of: Energy. Volume 113(2016)
• Page Start: 1062
• Page End: 1070
• Publication Date: 2016-10-15
• Subjects: HT-PEFC based combined system -- ORCS -- Power optimization -- PA-doped PBI; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.07.093
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: A combined power system consisting of a high-temperature polymer electrolyte fuel cell (HT-PEFC) as a topping power system and an ORCS (organic Rankine cycle system) as a bottoming cycle system is proposed and analytically optimized in this paper. In comparison to a LT-PEFC (low-temperature polymer electrolyte fuel cell), a HT-PEFC based on PA (phosphoric-acid)-doped polybenzimidazole (PBI) membranes has the advantage of an increased operating temperature. The waste heat recovered from a HT-PEFC stack can be used to generate more electricity by means of a bottoming power cycle system. An ORCS is widely used to generate electric power from heat recovered at a relatively low temperature. With the models of a HT-PEFC and an ORCS, the power and efficiency of the combined system are analytically derived. The maximum power of the combined system is calculated at full and partial loading of the fuel cell. The maximum power and corresponding efficiency are taken into account for the optimal performance of the ORCS. The results show that the power of the combined system is 17–21% higher than that of the HT-PEFC system without a heat recovery system. Highlights: A combined power system consisting of a HT-PEM fuel cell system and an ORCS. The waste heat recovered from a HT-PEFC stack can generate more electricity. The power and efficiency of the combined power system are analytically optimized.