Machine learning and whale optimization algorithm based design of energy management strategy for plug‐in hybrid electric vehicle

Zhuoran Hou, Jianhua Guo, Jiaming Xing, Chong Guo*, Yuanjian Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

In this paper, a novel energy management strategy with the improved adaptability to various conditions for plug‐in hybrid electric vehicle (PHEV) is proposed. The control parameters, derived from the benchmark test, are optimized offline for different driving conditions. The optimized parameters are implemented according to different driving behaviours identified online. The offline and online cooperation improves performance of energy management strategy in different driving conditions. Three main efforts have been made: Firstly, the valuable features that describe different driving conditions are extracted by random forest (RF) and the features are used for determining driving condition categories, utilized for online driving condition identification by support vector machine (SVM). Secondly, the control thresholds in the developed control strategy are optimized by whale optimization algorithm (WOA) under different driving conditions. The optimal control thresholds for different driving conditions will be called online after certain traffic condition is categorized. At last, simulation‐based evaluation is performed, validating the enhanced performance of the proposed methods in energy‐saving in different driving conditions.
Original languageEnglish
JournalIET Intelligent Transport Systems
Early online date05 Jun 2021
DOIs
Publication statusEarly online date - 05 Jun 2021

Keywords

  • ORIGINAL RESEARCH PAPER
  • ORIGINAL RESEARCH PAPERS

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