Date of Award


Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Mechanical Engineering-Engineering Mechanics (PhD)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Bo Chen

Committee Member 1

Jeffrey Naber

Committee Member 2

Jeffrey Burl

Committee Member 3

John Beard


The goal of this series of research is to advance hybrid electric vehicle (HEV) energy management by incorporating driver’s driving behavior and driving cycle information. To reduce HEV fuel consumption, the objectives of this research are divided into the following three parts.

The first part of the research investigates the impact of driver’s behavior on the overall fuel efficiency of a hybrid electric vehicle and the energy efficiency of individual powertrain components under various driving cycles. Between the sticker number fuel economy and actual fuel economy, it is well known that a noticeable difference occur when a driver drives aggressively. To simulate aggressive driving, the input driving cycles are scaled up from the baseline driving cycles to higher levels of acceleration/deceleration. The simulation study is conducted using Autonomie®, a powertrain simulation and analysis software. The performance of the major powertrain components is analyzed when the HEV is operated at different level of aggressiveness.

In the second part of the study, the vehicle driving cycles affect the performance of a hybrid vehicle control strategy and the corresponding overall performance of the vehicle. By identifying the driving cycles of a vehicle, the HEV supervisor controller system will be dynamically adapt the control strategy to the changes of vehicle driving patterns. With pattern recognition method, a driving cycle is represented by feature vectors that are formed by a set of parameters to which the driving cycle is sensitive. To establish reference driving cycle database, the representative feature vectors of four federal driving cycles are generated using feature extraction method. The performance of the presented adaptive control strategy based on driving pattern recognition is evaluated using Autonomie.

In the last part of the study, a predictive control method is developed and investigated for hybrid electric vehicle energy management in effort to improve HEV fuel economy. Model Predictive Control (MPC), a predictive control method, is applied to improve the fuel economy of a power-split HEV. The study compares the performance of MPC method and conventional rule-base control method. A parametric study is conducted to understand the influence of 3 weighting factors in MPC formulation on the performance of the vehicles.