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.