Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization

Quan Zhou, Wei Zhang, Scott Cash, Oluremi Olatunbosun, Hongming Xu*, Guoxiang Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)
411 Downloads (Pure)

Abstract

This paper firstly proposed a novel HEV sizing method using the Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm and secondly provided a demonstration on sizing a series hybrid electric powertrain with investigations of chaotic mapping strategies to achieve the global optimization. In this paper, the intelligent sizing of a series hybrid electric powertrain is formulated as an integer multi-objective optimization issue by modelling the powertrain system. The intelligent sizing mechanism based on APSO is then introduced, and 4 types of the most effective chaotic mapping strategy are investigated to upgrade the standard APSO into CAPSO algorithms for intelligent sizing. The evaluation of the intelligent sizing systems based on standard APSO and CAPSOs are then performed. The Monte Carlo analysis and reputation evaluation indicate that the CAPSO outperforms the standard APSO for finding the real optimal sizing result with much higher reputation, and CAPSO with logistic mapping strategy is the most effective algorithm for HEV powertrain components intelligent sizing. In addition, this paper also performs the sensitivity analysis and Pareto analysis to help engineers customize the intelligent sizing system.

Original languageEnglish
Pages (from-to)588-601
Number of pages14
JournalApplied Energy
Volume189
Early online date27 Dec 2016
DOIs
Publication statusPublished - 1 Mar 2017

Keywords

  • Accelerated particle swarm optimization
  • Chaotic mapping strategy
  • Hybrid electric powertrain
  • Intelligent components sizing
  • Multi-objective optimization

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • General Energy

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