Date on Master's Thesis/Doctoral Dissertation


Document Type

Master's Thesis

Degree Name



Industrial Engineering

Committee Chair

Bai, Lihui

Committee Co-Chair (if applicable)

Evans, Gerald W.

Committee Member

McIntyre, Michael L.

Author's Keywords

Battery electric vehicles; Coordinated charging; Decentralized approach; Mix integer non-linear programming


Electric batteries; Electric vehicles--Batteries


A projected high penetration of battery electric vehicles (BEV s) in the market will introduce an additional load in the electricity grid. Furthermore, uncontrolled BEV charging from residential users will exacerbate the existing peak load during evening hours. In this thesis, we propose two optimization models to alleviate the impact of extra demand from electric vehicles on the power grid. The first is a centralized charging scheduling model that coordinates the charging among BEV users under the goal of minimizing the total electricity cost for all users. The second model uses a decentralized agent-based approach to scheduling the BEV charging. This approach allows each user to minimize his/her own electricity cost through a learning process on a day-to-day basis. Our numerical results indicate that the centralized model is effective in reducing the total cost and peak-to-average ratios of the system load. Although the decentralized model is less effective compared to the centralized model, it is more appealing to public.