Mohamed Arezki Mellal1, Enrico Zio2,3,4, and Michael G. Pecht5
1LMSS, Faculty of Technology, M'Hamed Bougara University, Boumerdes 35000, Algeria
2MINES ParisTech, PSL Research University, Centre for Robotics, 60 Boulevard Saint-Michel, 75006 Paris, France
3Department of Energy, Politecnico di Milano, Milan 20156, Italy
4Department of Nuclear Engineering, College of Engineering, Kyung Hee University, Republic of Korea
5Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD USA
For more information about this article and related research, please contact Prof. Michael G. Pecht
Fuel cell vehicles (FCVs) are among the so-called green vehicles. They offer high autonomy and fast refueling but are more expensive than other green vehicles. Several efforts are devoted to reducing costs to make FCV technology more accessible. Most research addressing the optimization of FCVs focuses on energy management, sizing of the subsystems, and cost. However, reducing cost conflicts with increasing reliability. This paper addresses the multi-objective reliability and cost optimization of FCVs.
Due to inherent uncertainties, this work treats the feasibility of increasing the reliability of the subsystems as fuzzy values and introduces two defuzzification procedures to convert fuzzy values to crisp values, namely the ranking function and the graded mean integration value procedures. The non-dominated sorting genetic algorithm II (NSGA-II) is used with penalty functions to generate the Pareto fronts; then, a fuzzy decision method is adopted to find the best compromise solution. A numerical application of the proposed approach is illustrated. The results obtained show that the graded mean integration value procedure provides superior outcomes. The optimal reliability allocation of the subsystems in the FCV is determined.
This article is available online here and to CALCE Consortium Members for personal review.