Paper Info
Title
A stochastic model predictive control approach for series hybrid electric vehicle power management
Abstract
This paper illustrates the use of stochastic model predictive control (SMPC) for power management in vehicles equipped with advanced hybrid powertrains. Hybrid vehicles use two or more distinct power sources for propulsion, and their complex powertrain architecture requires the coordination of all the subsystems to achieve target performances in terms of fuel consumption, driveability, component life-time, exhaust emissions. Many control strategies have been presented and successfully applied, mainly based on heuristics or rules and tuned on certain reference drive cycles. To take into account that cycles are not exactly known a priori in driving routine, this paper proposes a stochastic approach for the power management problem. We focus on a series hybrid electric vehicle (HEV), which combines an internal combustion engine and an electric motor. The power demand from the driver is modeled as a Markov chain estimated on several driving cycles and used to generate scenarios in the SMPC law. Simulation results over a standard driving cycle are presented to demonstrate the effectiveness of the proposed stochastic approach and compared with other deterministic approaches.
Year
DOI
Venue
2010
10.1109/ACC.2010.5530504
American Control Conference
Keywords
DocType
ISSN
markov processes,electric motors,energy management systems,hybrid electric vehicles,internal combustion engines,predictive control,markov chain,electric motor,hybrid electric vehicle,hybrid powertrain,internal combustion engine,power management,stochastic model predictive control approach,fuel consumption,stochastic model,mathematical model,hybrid vehicle,engines,predictive models
Conference
0743-1619
ISBN
Citations 
PageRank 
978-1-4244-7426-4
34
2.46
References 
Authors
5
5
Name
Order
Citations
PageRank
G. Ripaccioli1554.91
D. Bernardini2342.80
S. Cairano324926.23
Alberto Bemporad44353568.62
Ilya Kolmanovsky526628.85