An adaptive model predictive controller for a novel battery-powered anti-idling system of service vehicles

Yanjun Huang, Soheil Mohagheghi Fard, Milad Khazraee, Hong Wang and Amir Khajepour

Energy, 2017, vol. 127, issue C, 318-327

Abstract: This paper presents an anti-idling regenerative auxiliary power system for service vehicles. The energy storage system in the regenerative auxiliary power system is able to electrify the auxiliary systems so as to achieve anti-idling. Service vehicles (e.g. delivery trucks or public buses) generally have predetermined routes, thus it is feasible and profitable to utilize a model predictive control strategy to improve the fuel economy of the new powertrain. However, the mass/load of such service vehicles is time-varying during a drive cycle. Therefore, an adaptive model predictive controller should be designed to account for this variation. Although the drive cycle is preset, it would experience uncertainties or disturbances caused by traffic or weather conditions in real situations. To deal with this problem, a large step size prediction method is used in the adaptive model predictive algorithm to enhance its robustness. The proposed algorithm is compared to a prescient model predictive controller in different scenarios to demonstrate its applicability and optimality (more than 7% fuel savings). The proposed approach is independent of the powertrain topology such that it is able to be directly extended to other types of hybrid electric vehicles.

Keywords: Anti-idling; Adaptive model predictive controller; Electrification of automotive auxiliary systems; Kalman filter (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054421730511X
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:127:y:2017:i:c:p:318-327

DOI: 10.1016/j.energy.2017.03.119

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 ().