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

Dennis Dreier (), Björn Rudin and Mark Howells ()
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Dennis Dreier: KTH Royal Institute of Technology
Björn Rudin: Combitech AB
Mark Howells: Loughborough University

Public Transport, 2020, vol. 12, issue 2, No 5, 363-404

Abstract: 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 hybrid-electric 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.

Keywords: Bus; Driving cycle; Energy; Operations research; Optimisation; Simulation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12469-020-00227-z

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