Comparison of management strategies for the charging schedule and all-electric operation of a plug-in hybrid-electric bi-articulated bus fleet

Abstract

This study developed a real-time optimisation (RTO) model that uses real-world bus operation data, i.e. route-specific and time-specific driving cycles. Potentials for energy savings and all-electric operation were estimated for a plug-in hybridelectric bi-articulated bus fleet (PLUG scenario) that can be managed according to different management strategies. Five strategies, A to E, were simulated that manage the charging schedule and all-electric operation with different priorities: PLUG-A, prioritise buses for charging by arrival times at the charging station (first come, first served); PLUG-B, prioritise buses for charging by energy intensities of the bus routes; PLUG-C, minimise the total energy use of the bus fleet; PLUG-D, maximise the total all-electric time of the bus fleet; and PLUG-E, maximise the total all-electric distance of the bus fleet. For comparison, a business-as-usual (BAU) scenario with conventional buses and another scenario with hybrid-electric buses (HYB) were simulated. Two weeks of real-world bus operation data from the city of Curitiba in Brazil were used as input data. The study finds that total energy savings of 17% and 27% in the HYB and PLUG scenarios can be achieved compared to the BAU scenario, respectively. Meanwhile, the average shares of the total all-electric time (TAET) and total all-electric distance (TAED) to the total bus fleet operation amount to 20% and 14% in the HYB scenario. Furthermore, both TAET and TAED in the PLUG scenario depend strongly on the chosen strategy amounting to ranges of 21-64% and 17-61%, respectively. Simultaneous maxima were found for strategy D.

A Case Study on Smart Grid Technologies with Renewable Energy for Central Parts of Hamburg

“…Other areas within Hamburg, like the Alsterdorf depot, have implemented such hubs; see, e.g., [41]. This also relates to the general issue of defining strategies for charging schedules in public transport, as envisaged, e.g., in Dreier et al [42]. (Note that the HEAT project with public transport usage in the HafenCity utilized a restricted test environment that had been ended in 2021; see, e.g., [43]).…”

A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems

“…For the same reason, a high penetration of EVs can be observed within car hailing and taxi services. In parallel, there is a growing research interest on the efficient planning of charging infrastructure for large fleets and exploiting smart charging scheduling in such systems [10][11][12][13].…”