Date of Award


Publication Type

Master Thesis

Degree Name



Mechanical, Automotive, and Materials Engineering

First Advisor

Ting, David

Second Advisor

Millo, Federico


CO2 emission, control strategy, dynamic programming, hybrid electric vehicle, Range extender, rule-based control strategy




Due to increasingly strict regulations on automobile CO2 emission around the world, this thesis focuses on the development of the control strategies of a plug-in series hybrid electric vehicle (HEV) with the goal of minimizing CO2 emission. The thesis consists of three parts. The first target is to set up an electric vehicle (EV) model, which is the base of a plug-in series hybrid electric vehicle. The electric machine and battery are sized, and range capability and energy consumption are evaluated for a vehicle running in EV mode. The second objective is the assessment of the reference performance of the Range Extender (R-EX) architecture through the dynamic programming (DP) function in MATLAB, in terms of minimizing CO2 emissions in the charge-sustaining condition. The third one is the development of the rule based control strategy through the analysis of the DP results by rules extraction. In this thesis, a B-segment hatchback passenger car is modelled. The simulations were carried out along seven standard driving cycles that were developed to model different road conditions. This thesis also evaluates the effect of different values of auxiliary power on the electric range, energy consumption and thresholds of the rule-based control strategy. A sensitivity analysis of the carbon intensity of electricity is performed from a worldwide perspective. Finally, the minimum values of CO2 emission and the optimal engine operating points over different driving cycles are obtained from the dynamic programming; two flow charts of the proposed rule-based control strategies are derived, which are implementable for an electrical control unit to determine the power split between different energy sources.