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

doi:10.3390/s150101245

Author(s)

Wah Ching Lee
Wing Hong Lau
Yat Wah Leung

Related Research Unit(s)

Department of Electrical Engineering

Detail(s)

Original language	English
Pages (from-to)	1245-1251
Journal / Publication	Sensors (Switzerland)
Volume	15
Issue number	1
Publication status	Published - 12 Jan 2015

Link(s)

DOI	DOI https://doi.org/10.3390/s150101245 Final Published version
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Attachment(s)	Documents 5425399.pdf Final Published version, 890 KB, PDF Publisher's Copyright Statement This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
Link to Scopus	https://www.scopus.com/record/display.uri?eid=2-s2.0-84921284087&origin=recordpage
Permanent Link	https://scholars.cityu.edu.hk/en/publications/publication(77f4f7b0-7c5f-47b5-88c4-988c86ced95a).html

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 CO₂ emissions worldwide every day.