An optimal two-tier fuzzified control scheme for energy efficiency management of parallel hybrid vehicles

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)1-7
Journal / PublicationJournal of Industrial Information Integration
Publication statusPublished - Dec 2016


Hybrid vehicle technology has been widely adopted because of its improvement of fuel economy as well as reducing emissions. In this paper, a new scheme, namely GAFUCS, is developed for the energy management of parallel hybrid vehicles. In order to enhance the performance in uncertainty and dynamic environment, as well as to improve the performance under different driving conditions, the operation is accomplished in two tiers, namely Tier-1 and Tier-2. With the sufficient principle design of Tier-2, GAFUCS fuses Fuzzy Logic (FL) and Genetic Algorithm (GA) by performing a real time operation. Hence GAFUCS is a more robust, efficient and accurate scheme than [16] originally invented by authors. It is shown that the new scheme produces less pollutants and carbon dioxide by reducing the consumption of petroleum. Based on various realistic driving conditions, the SOC and the fuel capacity, three hundred (300) have been investigated. Evaluation reveals that GAFUCS achieves an average improvement of 35.5%. It is evaluated that GAFUCS achieves an improvement of 16.6% compared to FGAS. GAFUCS thus is a new control scheme and is proven to be the most efficient scheme for energy efficiency management and emissions reduction for PHEVs.

Research Area(s)

  • 2-Tier Fuzzy Logic (FL), Energy management, Fuel capacity, Fuel efficiency, Genetic Algorithm (GA), Parallel hybrid electric vehicle (PHEV), State of charge of the battery (SOC)