A comparison of stochastic optimal control laws for a parallel hybrid vehicle
Paper in proceedings, 2006

This paper studies the properties of stochastic optimal control laws for hybrid vehicles, derived using stochastic dynamic programming with a Markov Chain as a drive behavior model. The focus of the study is on establishing how complex the Markov Chain model needs to be in order to achieve good performance. Moreover, a simple robustness analysis is done in order to examine how a control law, optimized with data from one city, performs when evaluated in a city with a considerably different drive behavior. The results indicate that it is sufficient to use a Markov Chain model with only power demand as the sole variable in the Markov state. Furthermore the simulation results show a remarkable robustness for the fuel consumption of the derived controllers when evaluated on a new drive behavior.

Powertrain and Drivetrain Control

Fuel Cell and Hybrid Vehicles

Author

Lars Johannesson

Chalmers, Signals and Systems, Systems and control, Automatic Control

Bo Egardt

Chalmers, Signals and Systems, Systems and control, Automatic Control

Proceedings of the 8th International Symposium on Advanced Vehicle Control, Taipei, Taiwan

Subject Categories

Control Engineering

More information

Created

10/7/2017