When to go electric? A parallel bus fleet replacement study

Islam, Adrita; Lownes, Nicholas

doi:10.1016/j.trd.2019.05.007

Cited by 70 publications

(27 citation statements)

References 17 publications

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“…The results indicated that the carbon emission was relatively serious in Beijing where the proportion of coal-fired power was large. However, pure electric bus fleets are generally considered to be zero emission fleets [27], [28], particularly for the studies on bus scheduling problems. Therefore, the carbon dioxide and nitrogen oxides emissions of eBus are not taken into account in this paper.…”

Section: B Ebus Schedulingmentioning

confidence: 99%

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Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses

et al. 2020

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To address sustainable development issues of urban traffic, electric buses will join traditional bus system, and the scheduling of bus fleet should be adjusted due to the distinct features of electric buses. To this end, this paper develops a Multi-objective Bi-level programming model to collaboratively optimize the vehicle scheduling and charging scheduling of the mixed bus fleet under the operating conditions of a single depot. The upper level determines the vehicle scheduling to minimize the operating cost and carbon emissions under the constraints of connecting time between trips and the limited driving range of electric buses. The lower level is a charging scheduling problem that considers the charging time and the limited driving distance constraint to minimize the charging cost. The proposed model is solved with an integrated heuristic algorithm. The vehicle scheduling problem is addressed with the iterative neighborhood search algorithm based on simulated annealing, while the charging scheduling problem is solved with a greedy dynamic selection strategy based on the approach of multi-stage decision. Finally, case study is carried out based on a mixed bus fleet in Beijing, and the results validate the availability of the proposed model and solution algorithm. INDEX TERMS Vehicle scheduling, charging scheduling, mixed bus system, multi-objective bi-level programming, collaborative optimization.

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Section: B Ebus Schedulingmentioning

confidence: 99%

“…Constraint (28) represents t se (i, j) is the minimum length of the required connection time between trip i and trip j, it can be divided into two categories by whether it needs to be charged or not. t f is the preparation time that doesn't include charging behavior before serving the next trip.…”

Section: B Charging Scheduling Model 1) Objective Functionmentioning

confidence: 99%

Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses

et al. 2020

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“…Other studies have sought to optimize mixed fleets on a whole transit network as opposed to a homogeneous fleet analysis [27][28][29][30], requiring the integration of complex models, but at the expense of increasing data requirements and calculation time not suitable for preliminary investigations. Given the uncertainty in a multidisciplinary exercise like life cycle modelling for immature technologies, it would be inappropriate to optimize to deterministic objectives [31].…”

Section: Fleet Analysis Approaches In Bus Life Cycle Modellingmentioning

confidence: 99%

A probabilistic fleet analysis for energy consumption, life cycle cost and greenhouse gas emissions modelling of bus technologies

Harris

Soban

Best³

2020

Applied Energy

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“…The literature provides examples of Life Cycle Cost analyses developed not for a single vehicle but for the entire vehicle fleet. Paper [9] presents an estimation of the share of buses with hybrid and electric drive systems in the bus fleet, using optimization methods by minimizing the LCC, operating costs and emission costs. Paper [10] contains an analysis of the Life Cycle Cost of a truck fleet, in which the alternatively powered vehicles make up for 50% and 75% of the entire fleet.…”

Section: Bus Parametersmentioning

confidence: 99%

Life Cycle Cost (LCC) Level of an Urban Transport Fleet with Differentiated Share of Buses with Alternative Drive Systems

Szumska¹,

Jurecki²,

Pawełczyk³

2020

Komunikácie

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In recent years, there has been a significant increase in the number of buses operated by urban public transport companies powered by alternative fuels and equipped with alternative drive systems. In addition to economic factors, operators should also take environmental aspects into account when purchasing new vehicles. In this case, a useful criterion for selecting a vehicle is the Life Cycle Cost (LCC), which, in addition to the cost of purchasing a bus, takes into account the necessary expenses associated with its maintenance, operation, decommissioning, as well as emissions costs. This paper presents a study of the LCC values, estimated for the entire bus fleet based on several bus replacement variants, taking into account different shares of alternative buses in the transport fleet. Analyses have shown that replacing conventional buses by the compressed natural gas (CNG) powered buses will reduce life cycle cost by 27% compared to the LCC level in 2019. Increasing the share of electric buses in the fleet will significantly reduce the level of emissions of harmful substances contained in exhaust gases.

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When to go electric? A parallel bus fleet replacement study

Cited by 70 publications

References 17 publications

Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses

Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses

A probabilistic fleet analysis for energy consumption, life cycle cost and greenhouse gas emissions modelling of bus technologies

Life Cycle Cost (LCC) Level of an Urban Transport Fleet with Differentiated Share of Buses with Alternative Drive Systems

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