Abstract
This work aims at comparing different many-objective techniques for the optimization of mission and parallel hybrid electric power system for aircraft. In particular, this work considers, as input of the optimization, the specification of the flight mission, the size of the main components and the energy management strategy for a Medium Altitude Long Endurance Unmanned Aerial Vehicle (MALE-UAV). The goals of the optimization are maximization of electric endurance, minimization of overall fuel consumption, improvement of take-off performance and minimization of the additional volume of the hybrid electric solution with respect to the initial conventional power system. The optimization methods considered in this study are those included in the ModeFRONTIER optimization environment: NSGA-II, MOGA-II, MOSA (Multi Objective Simulated Annealing algorithm) and Evolutionary Strategy of type (µ/ρ + λ)-ES. Initially, appropriate metrics are used to compare the proposed methods in a simplified problem with only two objective functions. Then a complete optimization is performed, in order to underline the degradation of the proposed optimization methods as the size of the problem increases and to define the best method according to the number of objective functions.
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Pornet, C., Isikveren, A.T.: Conceptual design of hybrid-electric transport aircraft. Prog. Aerosp. Sci. 79, 114–135 (2015)
Gimelli, A., Muccillo, M., Sannino, R.: Multivariable and multiobjective optimization for cogeneration plants. Part A: methodology. In: La Termotecnica, pp. 55–58 (2015)
Li, B., Li, J., Tang, K., Yao, X.: Many-objective evolutionary algorithms: a survey. ACM Comput. Surv. 48(1), Article No. 13 (2015)
Fleming, P.J., Purshouse, R.C., Lygoe, R.J.: Many-objective optimization: an engineering design perspective. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 14–32. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31880-4_2
Zitzler, E., Knowles, J., Thiele, L.: Quality assessment of pareto set approximations. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 373–404. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88908-3_14
Ishibuchi, H., Tsukamato, N., Nojima, Y.: Evolutionary many objective optimization: a short review. In: Proceedings of 2008 IEEE Congress on Evolutionary Computation, Hong Kong, 1–6 June 2008, pp. 2424–2431 (2008)
ModeFRONTIER 2014, Update 1, Version Number 4.6.1 b20150227, User Manual (2014)
Beyer, H.-G., Schwefel, H.-P.: Evolution strategies a comprehensive introduction. Nat. Comput. 1, 3–52 (2002)
Kirkpatrick, S., Gelatt Jr., D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Donateo, T., Ficarella, A., Spedicato, L.: Development and validation of a software tool for complex aircraft powertrains. Adv. Eng. Softw. 96, 1–13 (2016). https://doi.org/10.1016/j.advengsoft.2016.01.001
Lam, L.L., Darling, R.B.: Determining the optimal discharge strategy for a lithium-ion battery using a physics-based model. J. Power Sources 276, 195–202 (2015)
Donateo, T., Ficarella, A.: Designing a hybrid electric powertrain for an unmanned aircraft with a commercial optimization software. SAE Int. J. Aerosp. 10, 1–12 (2017)
Riquelme, N., Lücken, C.V., Baran, B.: Performance metrics in multi-objective optimization. In: Computing Conference (CLEI), Latin American (2015)
Donateo, T., De Risi, A., Laforgia, D.: Choosing an evolutionary algorithm to optimize diesel engines. In: TCN CAE 2005, University of Lecce, Department of Engineering for Innovation, Lecce, Italy (2011)
Lee, S., von Allmen, P., Fink, W., Petropoulos, A.E., Terrile, R.J.: Comparison of multi-objective genetic algorithms in optimizing Q-law low-thrust orbit transfers. In: GECCO 2005, 25–29 June 2005, Washington, DC, USA (2005)
Rigoni, E., Poles, S.: NBI and MOGA-II, two complementary algorithms for multi-objective optimizations. In: 04461 - Practical Approaches to Multi-Objective Optimization (2005)
Rigoni, E.: MOSA Multi Objective Simulated Annealing. Technical report 2003-003, ESTECO (2003)
Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181, 1653–1669 (2007)
Aksugur, M., Inalhan, G.: Design, build and flight testing of a VTOL tailsitter unmanned aerial vehicle with hybrid propulsion system. In: Ankara International Aerospace Conference, Ankara, Turkey (2011)
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Donateo, T., De Pascalis, C.L., Ficarella, A. (2018). Many-Objective Optimization of Mission and Hybrid Electric Power System of an Unmanned Aircraft. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_17
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DOI: https://doi.org/10.1007/978-3-319-77538-8_17
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