TY  - CHAP
SP  - 5844  
N2  - 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.
SN  - 978-1-4244-7427-1
PB  - IEEE
A1  - Ripaccioli, Giulio
A1  - Bernardini, Daniele
A1  - Di Cairano, Stefano
A1  - Bemporad, Alberto
A1  - Kolmanovsky, Ilya
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5530504&isnumber=5530425
KW  - Markov chain; electric motor; hybrid electric vehicle; hybrid powertrain; internal combustion engine; power management; stochastic model predictive control approach; Markov processes; electric motors; energy management systems; hybrid electric vehicles; internal combustion engines; predictive control
Y1  - 2010///
TI  - A stochastic model predictive control approach for series hybrid electric vehicle power management
AV  - none
T2  - American Control Conference
EP  - 5849
ID  - eprints427
N1  - Proceeding of the American Control Conference, Baltimore, MD, 2010
ER  -