Design Optimization and Charging Strategy for Electric Buses in Cities

Student thesis: Master's Thesis

Abstract

The transportation sector has been demonstrated to be one of the major sources of pollution. As such, the electrification of vehicles shows has proven itself to be the most promising solution for decreasing air pollution and improving efficiency, yet it still faces significant challenges due to the high capital costs associated with this undertaking. This work proposes a co-design framework for powertrain battery sizing and charging optimization for Battery Electric Buses (BEBs) in single and fleet operation. This bus operates within a limited distance inside a city or similar conurbation for public transportation purposes. Each bus must be designed in such a way that the Total Cost of Ownership (TCO) is minimized by as much as possible, while meeting its dynamic performances and scheduled timetable. ECO features play a crucial role in the optimal use of electric bus fleets, and therefore the use of these ECO features has been investigated and has been found to have a significant impact on the TCO. Several studies have been conducted in order to minimize the TCO of electric buses, with some focusing on lowering the operational costs while others focused on the charging infrastructure and initial investment costs. The objective of this thesis was to minimize the TCO combining the operational and investment costs for on-route charging with different scenarios. For each scenario, the plant has been optimized using Genetic Algorithm (GA) and brute force search and the results of both techniques have been discussed. The bus, including all of its components, was modelled using MATLAB/Simulink. The results demonstrate the effectiveness of the proposed model as the TCO of the bus was minimized while meeting the driving requirements.
Date of Award1 Sep 2020
Original languageEnglish
Awarding Institution
  • Vrije Universiteit Brussel
SupervisorMohammed Mahedi Hasan (Advisor), Omar Hegazy (Promotor) & Joeri Van Mierlo (Co-promotor)

Keywords

  • Electric Buses
  • Genetic Algorithm [GA]
  • artificial neural network
  • co-design optimization
  • eco features
  • fleet of buses

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