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퍼지 로직과 인공신경망을 이용한 자동차용 연료전지 스택의 지능형 진단방법 개발
이용현(Young-Hyun Lee),강원우(Wonwoo Kang),유승열(Seungyeol Yoo) 한국자동차공학회 2018 한국 자동차공학회논문집 Vol.26 No.5
For Fuel Cell Electric Vehicles(FCEV) to attain a competitive advantage today, durability and an acceptable automotive system cost are requisite. The durability of the Proton Exchange Membrane Fuel Cell(PEMFC) stack used in the FCEV is ensured particularly in real-time, on-line diagnosis and optimal control. To address price competitiveness, it is necessary for the main part to minimize the cost of the platinum catalyst that is usually not unavoidable. In this paper, we developed intelligent diagnosis methods by using Fuzzy Logic algorithms and Artificial Neural Networks to improve the existing, real-time diagnosis methods. We also deployed an intelligent diagnosis method on a real FCEV stack to verify the algorithm. To analyze the dynamic characteristics, the HWFET(Highway Fuel Economy Test) driving cycle was used.