A high fuel consumption efficiency management scheme for PHEVs using an adaptive genetic algorithm

Wah Ching Lee, Kim Fung Tsang, Hao Ran Chi, Faan Hei Hung, Chung Kit Wu, Kwok Tai Chui, Wing Hong Lau, Yat Wah Leung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)

Abstract

A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2emissions worldwide every day.
Original languageEnglish
Pages (from-to)1245-1251
Number of pages7
JournalSensors (Switzerland)
Volume15
Issue number1
DOIs
Publication statusPublished - 12 Jan 2015

Keywords

  • Adaptive genetic algorithm
  • Fuel efficiency management
  • PHEV

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

Cite this