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

Sensors (Basel). 2015 Jan 12;15(1):1245-51. doi: 10.3390/s150101245.

Authors

Wah Ching Lee¹, Kim Fung Tsang², Hao Ran Chi³, Faan Hei Hung⁴, Chung Kit Wu⁵, Kwok Tai Chui⁶, Wing Hong Lau⁷, Yat Wah Leung⁸

Affiliations

¹ Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. enwclee@polyu.edu.hk.
² Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China. ee330015@cityu.edu.hk.
³ Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China. haoranchi2-c@cityu.edu.hk.
⁴ Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China. fhhung4@cityu.edu.hk.
⁵ Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China. chungkwu4@gapps.cityu.edu.hk.
⁶ Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China. ktchui3-c@my.cityu.edu.hk.
⁷ Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China. itwhlau@cityu.edu.hk.
⁸ Department of Systems Engineering and Engineering Management, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. 50997159@student.cityu.edu.hk.

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.