Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles

Ji Li, Quan Zhou, Yinglong He, Bin Shuai, Ziyang Li, Huw Williams, Hongming Xu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
457 Downloads (Pure)


This paper investigates an online predictive control strategy for series-parallel plug-in hybrid electric vehicles (PHEVs), resulting in a novel online optimization methodology named the dual-loop online intelligent programming (DOIP) that is proposed for velocity prediction and energy-flow control. By reconsidering the change of driving behaviours at each look-ahead step, this methodology guarantees the effectiveness of optimal control sequence in the energy-saving efficiency of online predictive energy management. The design procedure starts with the simulation of a series-parallel PHEV using a systematic control-oriented model and the definition of a cost function. Inspired by fuzzy granulation technology, a deep fuzzy predictor is created to achieve driver-oriented velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities between vehicle speed and acceleration. To determine the optimal control behaviours and power distribution between two energy sources, chaos-enhanced accelerated swarm optimization is developed for the DOIP algorithm. The prediction capability of the deep fuzzy predictor is evaluated by comparing with two existing predictors over the WLTP-based driving cycle. The proposed control strategy is contrasted with short-sighted and dynamic programming based counterparts, and validated by a driver-in-the-loop test. The results demonstrate that the deep fuzzy predictor can effectively recognize driving behaviour and reduce at least 19% errors compared to involved Markov chain based predictors. Online predictive control strategy using the DOIP algorithm is able to significantly reduce 9.37% fuel consumption from the baseline and shorten computational time.

Original languageEnglish
Article number113617
Number of pages13
JournalApplied Energy
Early online date30 Jul 2019
Publication statusPublished - 1 Nov 2019


  • Driver-in-the-loop test
  • Fuzzy mathematics
  • Online predictive control
  • Particle swarm optimization
  • Plug-in hybrid electric vehicle

ASJC Scopus subject areas

  • Building and Construction
  • General Energy
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law


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