4.5 Article

Fleet and charging infrastructure decisions for fast-charging city electric bus service

Journal

COMPUTERS & OPERATIONS RESEARCH
Volume 135, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cor.2021.105449

Keywords

Electric bus; Fast-charging; Optimal planning; Randomized heuristic; Particle Swarm Optimization; Scheduling

Funding

  1. project Planning process and tool for step-by-step conversion of the conventional or mixed bus fleet to a 100% electric bus fleet'' (PLATON) of the Electric Mobility Europe initiative of the ERA-Net Cofund scheme

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This paper studies decision aspects concerning the introduction of fast-charging city electric buses, focusing on maximizing social-ecological value. Mathematical models for the main and secondary problems are proposed, and algorithms are developed accordingly. Through computer experiments and a case study, the proposed approach delivers solutions with values deviating at most 12% on average from the optimal solutions.
Decision aspects concerning the introduction of fast-charging city electric buses are studied in this paper. The main studied problem consists of determining a fleet of electric buses and their charging infrastructure such that a social-ecological value is maximized. In the most representative time period, all the electric buses should be available to drive, the required inter-bus interval should be maintained, the output power of any charging station and transformer should not be exceeded, and the total capital cost and the total annual operating, depreciation and energy cost should not exceed certain maximum thresholds. The total passenger demand satisfied by the electric buses can be considered as the value to be maximized. A secondary problem consists of finding a passenger load balanced schedule of the vehicles on the same route. Mathematical models for these two problems are proposed. A randomized heuristic algorithm combined with the Particle Swarm Optimization is developed for the main problem, and a known polynomial time algorithm is adapted for the secondary problem. A case study for the city of Minsk (Belarus) and computer experiments with random instances are provided. The proposed approach delivered solutions with values deviating at most 12% on average and 24% in the worst case from the upper bounds obtained as optima of a relaxed problem.

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