Computationally Efficient Deterministic Dynamic Programming for Optimal Supervisory Power Management of Power-split PHEVs
Open Access
- Author:
- Montgomery, Timothy Ray
- Graduate Program:
- Mechanical Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- December 12, 2012
- Committee Members:
- Hosam Kadry Fathy, Thesis Advisor/Co-Advisor
- Keywords:
- Hybrid
Electric
Vehicle
HEV
PHEV
Dynamic
Programming
DDP
Optimal
Supervisory
Power
Management - Abstract:
- This thesis addresses some of the computational challenges of applying Deterministic Dynamic Programming (DDP) to plug-in hybrid electric vehicle (PHEV) power management. The goal of this thesis is to develop an approach for reducing the computational and memory needs of DDP-based optimal PHEV power management. The underlying motivation for this work is to create a dynamic program that accommodates dense state variable meshes and can be expanded to include additional state variables and optimization objectives. DDP is a trajectory-based optimization method that can be used as a tool for benchmarking and studying optimal power management strategies. It can be used to optimize powertrain performance for multiple objectives, but is limited by the number of states and the density of mesh discretization that can be handled, due to the numerical complexity of the control policy search algorithm. These computational difficulties must be overcome before a comprehensive optimization objective can be studied. To reduce the numerical complexity of applying DDP to PHEV power management, this thesis first develops a powertrain control framework with the engine as the sole independent control input device. This thesis then uses mesh space vectorization to improve the efficiency of the exhaustive optimal input policy searching process. Finally, this thesis employs the novel use of mesh space partitioning to maximize the use of the parallel processing units within a single computer without exceeding the computer's physical memory limits. Using these numerical acceleration methods, the thesis delivers a DDP algorithm that is capable of optimizing in near- real-time, is well-suited to handling dense state and input meshes, and is amenable to the addition of new state variables and optimization objectives.