Keywords and Phrases
Electric bus; Electric vehicle; Energy management; Multi-objective optimization; Neural network; Optimal driving
Electric vehicles (EVs) are a promising alternative energy mode of transportation for the future. However, due to the limited range and relatively long charging time, it is important to use the stored battery energy in the most optimal manner possible. Existing research has focused on improvements to the hardware or improvements to the energy management strategy (EMS). However, EV drivers may adopt a driving strategy that causes the EMS to operate the EV hardware in inefficient regimes just to fulfil the driver demand. The present study develops an optimal driving strategy to help an EV driver choose a driving strategy that uses the stored battery energy in the most optimal manner. First, a strategy to inform the driver about his/her current driving situation is developed. Then, two separate multi-objective strategies, one to choose an optimal trip speed and another to choose an optimal acceleration strategy, are presented. Finally, validation of the optimal driving strategy is presented for a fleet-style electric bus. The results indicated that adopting the proposed approach could reduce the electric bus’ energy consumption from about 1 kWh/mile to 0.6-0.7 kWh/mile. Optimization results for a fixed route around the Missouri S&T campus indicated that the energy consumption of the electric bus could be reduced by about 5.6% for a 13.9% increase in the trip time. The main advantage of the proposed strategy is that it reduces the energy consumption while minimally increasing the trip time. Other advantages are that it allows the driver flexibility in choosing trip parameters and it is fairly easy to implement without significant changes to existing EV designs. "--Abstract, page iii.
Köylü, Ümit Ö. (Ümit Özgür)
Sheffield, John W.
Landers, Robert G.
Nandi, Arup K.
Mechanical and Aerospace Engineering
Ph. D. in Mechanical Engineering
Missouri University of Science and Technology
Journal article titles appearing in thesis/dissertation
- Neural network strategy for driving behavior and driving cycle classification
- Electric vehicle range prediction for constant speed trip using multi-objective optimization
- A multi-objective approach to find optimal electric vehicle acceleration: Simultaneous minimization of acceleration duration and energy consumption
- Finding an optimal driving strategy for an electric bus based on operational data
xv, 173 pages
© 2015 Warren Santiago Vaz, All rights reserved.
Dissertation - Open Access
Library of Congress Subject Headings
Buses, Electric -- Missouri -- Rolla
Neural networks (Computer science)
Local transit -- Energy consumption -- Management
Electronic OCLC #
Vaz, Warren Santiago, "Energy management in electric vehicles: Development and validation of an optimal driving strategy" (2015). Doctoral Dissertations. 2422.