Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions
Zeyu Chen,
Rui Xiong and
Jiayi Cao
Energy, 2016, vol. 96, issue C, 197-208
Abstract:
This paper proposes a novel optimal power management approach for plug-in hybrid electric vehicles against uncertain driving conditions. To optimize the threshold parameters of the rule-based power management strategy under a certain driving cycle, the particle swarm optimization algorithm was employed, and the optimization results were used to determine the optimal control actions. To better implement the power management strategy in real time, a driving condition recognition algorithm was proposed to identify real-time driving conditions through a fuzzy logic algorithm. To adjust the thresholds of the rule-based strategy adaptively under uncertain driving cycles, a dynamic optimal parameters algorithm has been further established accordingly, and it is helpful for avoiding the problem that the thresholds of the rule-based strategy are very sensitive to the driving cycles. Finally, in combination with the above efforts, a detailed operational flowchart of the particle swarm optimization algorithm-based optimal power management through driving cycle recognition has been proposed. The results illustrate that the proposed strategy could greatly improve the control performance for different driving conditions. Especially for the uncertain driving cycles, the reduction in energy loss can be up to 1.76%.
Keywords: Plug-in hybrid electric vehicles; Power management; Optimal control; Driving condition recognition; Particle swarm optimization (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (55) Track citations by RSS feed
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544215017168
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:96:y:2016:i:c:p:197-208
DOI: 10.1016/j.energy.2015.12.071
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).