Preemptive vs. non-preemptive charging schedule for large-scale electric bus depots

Jahic, Amra; Eskander, Mina; Schulz, Detlef

doi:10.1109/isgteurope.2019.8905633

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Following this, as a framework for action to achieve this goal, it initiated an "electrification" of its bus fleets. In this regard, the two public transport companies of the city, Hamburger Hochbahn AG and Verkehrsbetriebe Hamburg-Holstein have already started to electrify their fleets and build the necessary charging infrastructure (the intention will be to replace 1500 buses and build eight depots by 2030) [108,109]. This is further facilitated by the fact Hamburg is structurally an industrial city that is home to many battery and electric vehicle manufacturers [110,111].…”

Section: Hamburgmentioning

confidence: 99%

Electric Mobility in a Smart City: European Overview

et al. 2021

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According to the United Nations (UN), although cities occupy only 3% of Earth’s surface, they host more than half of the global population, are responsible for 70% of energy consumption, and 75% of carbon emissions. All this is a consequence of the massive urbanization verified since the 1950s and which is expected to continue in the coming decades. A crucial issue will therefore concern the management of existing cities and the planning of future ones, and this was also emphasized by the UN Sustainable Development Goals (SDGs), especially in Goal 11 (Sustainable Cities and communities). Smart Cities are often seen as ideal urban environments in which the different dimensions of a city (economy, education, energy, environment, etc.) are managed successfully and proactively. So, one of the most important challenges cities will have to face, is to guide citizens towards a form of “clean” energy consumption, and the dimension on which decision-makers will be able to work is the decarbonization of transport. To achieve this, electric mobility could help reduce polluting emissions on the road. Within this research, the strategies that six Smart Cities (London, Hamburg, Oslo, Milan, Florence, and Bologna) have implemented to encourage the transition to this form of mobility have been studied. Through a systematic review of the literature (Scopus, Google Scholar, and Web of Science) and through the study of the main political/energy documents of the cities, their policies on electric mobility have been evaluated. Then, for each city, SDG 11.6.2 was analyzed to assess the air quality in the last four years (2016–2019) and, therefore, the effectiveness of the policies. The analysis showed, in general, that the policies have worked, inducing reductions in the pollutants of PM2.5, PM10, NO2. In particular, the cities showed the most significant reduction in pollutant (above 20%) were Hamburg (−28% PM2.5 and −2%6 NO2), Milan (−25% PM2.5 and −52% NO2), and London (−26% NO2).

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Section: Hamburgmentioning

confidence: 99%

Electric Mobility in a Smart City: European Overview

et al. 2021

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“…Other researchers have applied approaches such as integer programming (Nageshrao, Jacob, and Wilkins (2017), Lotfi et al (2020), Picarelli et al (2020)), Markov decision processes (Wang et al (2018)), greedy algorithms (Jefferies and Göhlich (2020)), genetic algorithms (Gao et al (2018), Chao and Xiaohong (2013)), space-time networks (Olsen, Kliewer, and Wolbeck (2020)), and dynamic programming (Wang, Kang, and Liu (2020)) to optimally assign electric buses to charging stations. Jahic, Eskander, and Schulz (2019) use preemptive, quasipreemptive, and non-preemptive approaches to effectively handle the load in charging stations or garages. Since charging duration occupies a reasonable portion of the routine, Chao and Xiaohong (2013) propose a battery replacement technique, but this approach is inefficient for transit agencies operating with a few electric buses.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

Sivagnanam

Ayman

Wilbur

et al. 2021

AAAI

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Affordable public transit services are crucial for communities since they enable residents to access employment, education, and other services. Unfortunately, transit services that provide wide coverage tend to suffer from relatively low utilization, which results in high fuel usage per passenger per mile, leading to high operating costs and environmental impact. Electric vehicles (EVs) can reduce energy costs and environmental impact, but most public transit agencies have to employ them in combination with conventional, internal-combustion engine vehicles due to the high upfront costs of EVs. To make the best use of such a mixed fleet of vehicles, transit agencies need to optimize route assignments and charging schedules, which presents a challenging problem for large transit networks. We introduce a novel problem formulation to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging, while serving an existing fixed-route transit schedule. We present an integer program for optimal assignment and scheduling, and we propose polynomial-time heuristic and meta-heuristic algorithms for larger networks. We evaluate our algorithms on the public transit service of Chattanooga, TN using operational data collected from transit vehicles. Our results show that the proposed algorithms are scalable and can reduce energy use and, hence, environmental impact and operational costs. For Chattanooga, the proposed algorithms can save $145,635 in energy costs and 576.7 metric tons of CO2 emission annually.

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“…Strategies vary in terms of their basic formulation, many using variations of the vehicle scheduling problem [8][9][10][11][12][13] while [14] utilizes a network flow approach and [15] utilizes a rectangle packing approach referred to as the Position Allocation Problem (PAP). Some works assume that BEBs always charge to full capacity each time they connect to a charger [8,9,16,17], while others allow for partial charging using a linear battery charge model [13,18,19], and others use a higher-fidelity, non-linear battery model [8,14,[20][21][22]. Some works consider only fast chargers during planing [12,18,[23][24][25], while others assume that different types of chargers can exist in different locations [11,13].…”

Section: Introductionmentioning

confidence: 99%

“…When considering the utility rate schedules, two key elements must be considered: the consumption cost and the demand cost. Most approaches consider a consumption cost [9,10,16,[19][20][21][22][23]. This is akin to the combustion engine fuel cost.…”

Section: Introductionmentioning

confidence: 99%

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A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

Brown,

Droge

2024

Preprint

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With an increasing adoption of Battery Electric Bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this paper, a Simulated Annealing (SA) implementation is developed for a charge scheduling framework for a fixed-schedule fleet of BEBs that utilizes a proportional battery dynamics model, accounts for multiple charger types, allows partial charging, and further considers the total energy consumed by the schedule as well as peak power use. Two generation mechanisms are implemented for the SA algorithm denoted as the "quick" and "heuristic" implementations, respectively. The model validity is demonstrated by utilizing a set of routes sampled from the Utah Transit Authority (UTA) and comparing the results against two other models: the BPAP and the Qin-Modified. The results presented show that both SA techniques offer a means of generating operationally feasible schedules quickly while minimizing the cost of operation and considering battery health.

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Preemptive vs. non-preemptive charging schedule for large-scale electric bus depots

Cited by 7 publications

References 20 publications

Electric Mobility in a Smart City: European Overview

Electric Mobility in a Smart City: European Overview

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

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