2020/09/29 by Ayman Abdelwahed, Pieter L. van den Berg, Tobias Brandt +2 · 1 citation
Engineering · #Advanced Battery Technologies Research #Electric Vehicles and Infrastructure #Transportation and Mobility Innovations
paper · pdf · doi:10.1287/trsc.2020.0982
openalex publication_date 2020/09/29 · crossref created 2020/09/29 · crossref issued 2020/11/01 · crossref published 2020/11/01 · crossref published-print 2020/11/01 · crossref deposited 2023/04/02 · openalex created_date 2025/10/10 · crossref indexed 2026/07/31 · openalex updated_date 2026/08/03
Public transport operators (PTOs) increasingly face a challenging problem in switching from conventional diesel to more sustainable battery electric buses (BEBs). In this study, we optimize the opportunity fast-charging schedule of transit BEB networks in order to minimize the charging costs and the impact on the grid. Two mixed-integer linear programming (MILP) formulations that use different discretization approaches are developed and compared. Discrete-Time Optimization (DTO) resembles a time-expanded network that discretizes the time and decisions to equal discrete slots. Discrete-Event Optimization (DEO) discretizes the time and decisions into nonuniform slots based on arrival and departure events in the network. In addition to the DEO’s higher practicability, the comparative computational study carried out on the transit-bus network in the city of Rotterdam, Netherlands, shows that the DEO is superior to the DTO in terms of computational performance. To show the potential benefits of the optimal schedule, it is compared with two reference common-sense greedy strategies: First-in-First-Served and Lowest-Charge-Highest-Priority.