A Realistic Driving Profile Optimization for Electric Vehicles
Due to their environmental and economic advantages, electric vehicles (EVs) are being deployed with an increasing rate in personal as well as commercial transportation. The environmental and economic benefits of using an EV heavily depend on the driving profile. In this study, our aim is to formulate a driving profile optimization model for an EV as realistically as possible. Particularly, most of the current studies formulate an estimated energy consumption function for EVs considering the maximum travel speed. While this estimation works well under the assumption that the travel distance is sufficiently long, it fails to accurately estimate energy consumption for relatively shorter distances because one needs to explicitly consider acceleration and deceleration along with the travel speed to capture energy consumption rate changes and energy recovery with regenerative braking technology. We formulate a bi-objective driving profile optimization model with energy consumption and travel time minimization objectives. The model determines Pareto efficient values of acceleration, deceleration, and travel speed between two points and accounts for regenerative braking and EV power-train inefficiencies. A numerical study shows the benefits of the detailed modeling approach especially for short travel distances that are common in urban transportation applications.
H. Farhangi et al., "A Realistic Driving Profile Optimization for Electric Vehicles," Proceedings of the 2016 Industrial and Systems Engineering Research Conference (2016, Anaheim, CA), pp. 697 - 702, Institute of Industrial Engineers (IIE), May 2020.
2016 Industrial and Systems Engineering Research Conference, ISERC 2016 (2016: May 21-24, Anaheim, CA)
Mechanical and Aerospace Engineering
Center for Research in Energy and Environment (CREE)
Keywords and Phrases
Electric Vehicle; Energy Consumption; Pareto Front; Îµ-constraint method
Article - Conference proceedings
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01 May 2020