planning for battery electric buses charging in transit system
abstract
with the growing focus on sustainable transportation, battery electric buses (bebs)
have emerged as a viable solution. bebs have received significant recognition as an
environmentally conscious and sustainable means of transportation. in many cases,
transitioning from a conventional diesel-fueled transit system to a fully electric one is
essential. designing an effective strategy, which encompasses placing charging sites and
implementing proper charging mechanisms, is crucial to ensuring efficient and consistent
charging of bebs in an electrified public transit system. however, the challenge intensifies
when the transit planner aims to maintain a consistent daily service timetable.
the research endeavours to tackle this challenge by formulating efficient charging strate-
gies and methodologies for infrastructure planning. this thesis outlines a four-step
approach for transit system planners to attain optimal solutions, encompassing worst-case
energy consumption calculation, off-service charging site placement, off-service (overnight)
charging mechanism, on-route charging planning, and finding the number required bebs
and integration of them to fully electric transit system. four methods are designed
for use in planning: the constrained greedy clustering (cgc) algorithm, the priority
charging mechanism (pcm), the constraint affinity clustering algorithm (caca), and
timetable tuning. a case study based on a real-world thunder bay, on transit system
validates the proposed methodologies and assesses their effectiveness in improving the
overall performance of the beb fleet. results demonstrate significant improvements in
operational efficiency, cost reduction, and environmental sustainability by implementing
the proposed charging infrastructure optimization strategies.
the findings of this research contribute to the advancement of sustainable transportation
by providing practical insights and solutions to the challenges associated with beb
charging infrastructure design and optimization.