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

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

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

15 Citations (Scopus)

105 Downloads (CityUHK Scholars)

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.

Original language	English
Pages (from-to)	1245-1251
Journal	Sensors (Switzerland)
Volume	15
Issue number	1
DOIs	https://doi.org/10.3390/s150101245
Publication status	Published - 12 Jan 2015

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 13 Climate Action

Research Keywords

Adaptive genetic algorithm
Fuel efficiency management
PHEV

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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10.3390/s150101245

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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.",

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AU - Chi, Hao Ran

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AU - Wu, Chung Kit

AU - Chui, Kwok Tai

AU - Lau, Wing Hong

AU - Leung, Yat Wah

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N2 - 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.

AB - 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.

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

Abstract

UN SDGs

Research Keywords

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