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Date of Award


Document Type

Campus Access Master's Thesis

Degree Name

Master of Science in Mechanical Engineering (MS)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Bo Chen

Committee Member 1

Darrell Robinette

Committee Member 2

Madhi Shahbakhti


This thesis presents three optimization-based powertrain control methods for powertrain control of multi-mode hybrid electric vehicle, as part of the “Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR)” project.

The control methods are Instantaneous Optimization, Global Optimization and Receding Horizon Control/Model Predictive Control. The Instantaneous Optimization utilizes ECMS method to determine the best operating points and best mode for powertrain control. Dynamic Programming (DP) is used in the global optimization method for optimal control. The experimentally determined mode shift fuel and electricity costs are incorporated in the algorithm to optimize the mode selection and power-split. The Receding Horizon Control utilizes the Dynamic Programming algorithm to create optimal solution within a moving prediction horizon, which has the potential for real time control.