Optimal Planning of Battery Electric Buses for Reliable Services

Battery electric buses (BEB) are a strong alternative for climate change mitigation due to their potential to reduce fossil fuel consumption. This paper introduces a deterministic optimization model for the optimal selection of the number of buses and opportunity chargers of an all-electric bus flee...

Full description

Saved in:
Bibliographic Details
Published in2023 International Electrical Engineering Congress (iEECON) pp. 306 - 312
Main Authors Elkholy, Sama, Shaaban, Mostafa F., Ali, Abdelfatah
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.03.2023
Subjects
Atmospheric modeling
battery electric buses
Costs
electric vehicles
HVAC
opportunity charging
optimization
Planning
Power demand
Programming
Time-frequency analysis
Online AccessGet full text
DOI10.1109/iEECON56657.2023.10126638

Cover

More Information
Summary:Battery electric buses (BEB) are a strong alternative for climate change mitigation due to their potential to reduce fossil fuel consumption. This paper introduces a deterministic optimization model for the optimal selection of the number of buses and opportunity chargers of an all-electric bus fleet constrained by a specified route cycle, number and duration of stops, frequency, service time, and BEB model. The optimization model is based on a BYD K9 40' Transit BEB fleet. The bus's power consumption model is generated and the problem is formulated subject to the constraints mentioned above. The optimization problem is formulated as a Mixed Integer Non-Linear Programming (MINLP) and is solved using General Algebraic Modeling System (GAMS) program. The main results are obtained in two cases: when the bus's air conditioning is turned on and when it is turned off. For the first case, various results are obtained by varying the problem parameters at a time within a certain range to observe how the critical problem variables are affected. The obtained results demonstrate the effectiveness of the proposed planning approach.
DOI:10.1109/iEECON56657.2023.10126638