Title :
Stochastic model predictive energy management for series hydraulic hybrid vehicle
Author :
Feng, Daiwei ; Huang, Dagui ; Li, Dinggen
Author_Institution :
Sch. of Mechatron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Abstract :
This paper investigates the application of stochastic model predictive control (SMPC) methodology for developing power management strategies tailored for the serial hydraulic hybrid vehicle (SHHV). The velocity demand from the driver is expressed as a random Markov process. A forward-facing closed-loop model of the SHHV powertrain is built and simulated in MATLAB/SIMULINK. The predictive model in SMPC is formulated by successive on-line linearization. The simulation results over a standard driving cycle are presented to show the improved performance of SMPC over other deterministic approaches.
Keywords :
Markov processes; closed loop systems; energy management systems; fuel economy; hydraulic systems; linearisation techniques; power transmission (mechanical); predictive control; random processes; MATLAB/SIMULINK; SHHV powertrain; forward facing closed loop model; online linearization; power management; random Markov process; series hydraulic hybrid vehicle; standard driving cycle; stochastic model predictive energy management; velocity demand; Engines; Markov processes; Mathematical model; Predictive models; Torque; Vehicles; Fuel Economy; Optimization; Series Hydraulic Hybrid Vehicle; Stochastic Model Predictive Control;
Conference_Titel :
Mechatronics and Automation (ICMA), 2011 International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8113-2
DOI :
10.1109/ICMA.2011.5986284
