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

Wah Ching Lee1, Kim Fung Tsang2, Hao Ran Chi3

  • 1Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. enwclee@polyu.edu.hk.

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Summary
This summary is machine-generated.

A new fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) uses an adaptive genetic algorithm and fuzzy logic. This approach improves driving efficiency by 10% and can significantly reduce daily CO2 emissions.

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Area of Science:

  • Automotive Engineering
  • Artificial Intelligence
  • Environmental Science

Background:

  • Plug-in hybrid electric vehicles (PHEVs) offer a transition to sustainable transportation but require efficient energy management.
  • Optimizing the balance between electric and petrol power is crucial for reducing fuel consumption and emissions in PHEVs.

Purpose of the Study:

  • To develop an advanced fuel efficiency management scheme for PHEVs.
  • To reduce overall fuel consumption and environmental impact through intelligent energy resource allocation.

Main Methods:

  • Implementation of an adaptive genetic algorithm to manage energy resources.
  • Design of a fuzzy logic controller to optimize the objective function of the genetic algorithm.
  • Simulation and comparison of the developed scheme against existing methods using calculated and publicized data.

Main Results:

  • The developed fuzzified genetic algorithm scheme achieved a 10% improvement in efficiency compared to existing schemes.
  • The proposed management scheme demonstrates superior performance in optimizing petrol versus electricity usage for driving.

Conclusions:

  • The adaptive genetic algorithm with fuzzy logic control offers a highly effective solution for enhancing PHEV fuel efficiency.
  • Widespread adoption of this scheme could lead to substantial daily reductions in global CO2 emissions, estimated at over 600 tons.