Date of Award
5-2018
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mechanical Engineering
Committee Chair
Monika M. Ivantysynova
Committee Member 1
Gregory M. Shaver
Committee Member 2
Kartik B. Ariyur
Committee Member 3
Andrea Vacca
Abstract
This work presents three computational methods for real time energy management in a hybrid hydraulic vehicle (HHV) when driver behavior and vehicle route are not known in advance. These methods, implemented in a receding horizon control (aka model predictive control) framework, are rather general and can be applied to systems with nonlinear dynamics subject to a Markov disturbance. State and input constraints are considered in each method. A mechanism based on the steady state distribution of the underlying Markov chain is developed for planning beyond a finite horizon in the HHV energy management problem. Road elevation information is forecasted along the horizon and then merged with the statistical model of driver behavior to increase accuracy of the horizon optimization. The characteristics of each strategy are compared and the benefit of learning driver behavior is analyzed through simulation on three drive cycles, including one real world drive cycle. A simulation is designed to explicitly demonstrate the benefit of adapting the Markov chain to real time driver behavior. Experimental results demonstrate the real time potential of the primary algorithm when implemented on a processor with limited computational resources.
Recommended Citation
Williams, Kyle R., "Real-Time Stochastic Predictive Control for Hybrid Vehicle Energy Management." (2018). Open Access Dissertations. 1891.
https://docs.lib.purdue.edu/open_access_dissertations/1891