COMPLEX ASSESSMENT OF THE EFFECTIVENESS OF AN HYBRID ELECTRIC VEHICLE
Abstract
The limited hydrocarbon fuel, as well as the problems of air pollution, are the main factors stimulating the development of vehicles with alternative energy sources. Hybrid electric vehicles are able to meet future requirements for environmental safety and energy efficiency. However, the hybrid power plant, in comparison with the power plant of a conventional vehicle, is a more complex system, both in terms of equipment composition and in operation algorithms. Characterization of the main components of the power plant, effective modes and algorithms for the operation of the latter is the task of multi-criteria optimization. This article discusses the possibility of an integrated assessment of the adoption of technical decisions in the design and comparison of complex vehicles based on the vector criterion distance model, which takes into account a significant number of particular indicators. The coefficients of valuation and the importance of individual interdependent particular criteria, determined on the basis of the information contained in the set of selected particular criteria, are proposed.
About the Authors
Vyacheslav N. Logachev
MADI
Russian Federation
Ph. D., associate professor
Russian Federation
Ph. D., associate professor
Elena M. Layko
MADI
Russian Federation
Ph. D., associate professor
Russian Federation
Ph. D., associate professor
Kirill M. Sidorov
MADI
Russian Federation
Ph. D., associate professor
Russian Federation
Ph. D., associate professor
References
1. Grebennikov A.G., Varshav'yak G.B. Otkrytye informacionnye i komp'yuternye integrirovannye tekhnologii, 2007, no. 35, pp. 91–98.
2. Nogin V.D. Prinyatie reshenij v mnogokriterial'noj srede: kolichestvennyj podhod (Making decisions in a multi-criteria environment: a quantitative approach), Moscow, Fizmatlit, 2002, 144 p.
3. Safronov V.V. Osnovy sistemnogo analiza: metody mnogovektornoj optimizacii i mnogovektornogo ranzhirovaniya (Basics of system analysis: methods of multi-vector optimization and multi-vector ranking), Saratov, Nauchnaya kniga, 2009, 329 p.
4. Lotov A.V., Pospelova I.I. Mnogokriterial'nye zadachi prinyatiya reshenij (Multi-criteria decision making tasks), Moscow, MAKS Press, 2008, 197 p.
5. Rua B. Voprosy analiza i procedury prinyatiya reshenij, Moscow, 1977, pp. 5–20.
6. Shmerling D.S., Dubovskij S.K., Arzhanova T.D., Frenkel A.A. Statisticheskie metody analiza ehkspertnyh ocenok, 1977, pp. 299–382.
7. Voroncov K.V. Algoritmy klasterizacii i mnogomernogo shkalirovaniya (Algorithms for clustering and multidimensional scaling), Moscow, Izd-vo MGU, 2007, 18 p.
8. Petrov E.G. Novozhilov M.V., Grebennik I.V. Metody i sredstva prinyatiya reshenij v social'no-ehkonomicheskih i tekhnicheskih sistemah (Methods and means of decision making in socio-economic and technical systems), Doneck, OLBI-Press, 2003, 380 p.
9. Borisov V.N. Issledovanie operacij, 1972, pp. 72–91.
10. De Maesschalck, R. Jouan-Rimbaud D., Massart D.L. The Mahalanobis distance, Chemometrics and Intelligent Laboratory Systems, 2000, vol. 50, issue 1, pp. 1–18.
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