Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system

Shuo Zhang, Rui Xiong and Fengchun Sun

Applied Energy, 2017, vol. 185, issue P2, 1654-1662

Abstract: The fuel economy performance of plug-in hybrid electric vehicles (PHEVs) strongly depends on the power management strategy. This study proposes an integrated power management for a PHEV with multiple energy sources, including a semi-active hybrid energy storage system (HESS) and an assistance power unit (APU). The HESS consists of battery packs and ultracapacitor packs. In the integrated control strategy, the output power between the battery packs and ultracapacitor packs is regulated by the model predictive control strategy, while the output power between the APU and HESS is allocated by the rule-based strategy. In the model predictive control process, a period of the future velocity will be predicted, and the dynamic programming algorithm will be applied to optimize the control strategy accordingly. The robustness of the proposed approach is verified by three typical driving cycles, including the Manhattan cycle, CBDC cycle and UDDSHDV cycle. The results show that the proposed control strategy can promote fuel economy compared with the original control strategy, especially in the charge sustain mode under the MANHATTAN driving cycle (21.88% improvement).

Keywords: Plug-in hybrid electric vehicle; Hybrid energy storage system; Assistant power unit; Model predictive control; Dynamic programming; Power management (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (56)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261915016086
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:appene:v:185:y:2017:i:p2:p:1654-1662

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2015.12.035

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().