Electrification of airport taxiway operations: A simulation framework for analyzing congestion and cost

https://doi.org/10.1016/j.trd.2021.102962Get rights and content

Highlights

  • We propose a simulation framework for analyzing aircraft taxi via electric towing.

  • Experiments investigate impacts to taxiway congestion and equivalent annual cost.

  • Fuel and emission reductions can be realized with limited impact on taxi time.

  • High uncertainty prevents definitive conclusions about cost effectiveness.

  • The framework can be used to inform taxi strategies, implementation, and budgeting.

Abstract

Aircraft taxiing with external electric towing could significantly reduce carbon dioxide (CO2) emissions from ground operations. We develop a discrete event simulation and cost model to investigate taxiway congestion and develop a comprehensive equivalent annual cost (EAC) estimate from using electric towing tractors (e-tractors). The model incorporates collision and conflict avoidance and towing service interruptions. Experiments identify the number of e-tractors generating the lowest EAC. A case study considers three electric taxi scenarios at Montréal-Trudeau International Airport. Our findings suggest significant potential for reducing fuel consumption and CO2 emissions with limited impact on taxi time when e-tractor capacity is aligned with demand. However, high uncertainty prevents definitive conclusions about cost effectiveness. The modeling approach can be updated based on ongoing field test data and input from airlines, airports, and handling agents and used to test novel taxi strategies, develop e-tractor implementation plans, and inform the capital budgeting process.

Introduction

Five to ten percent of aircraft emissions occur during the landing and take-off (LTO) cycle, which includes landing, taxi-in, taxi-out, and take-off (Overton, 2019, Deonandan and Balakrishnan, 2010). During taxi operations, jet-powered aircraft typically utilize the thrust produced by their engines to move between gates and runways. Several studies estimate significant potential fuel and carbon dioxide (CO2) emission reductions from taxi alternatives, such as single-engine taxi, aircraft towing by diesel-electric hybrid or fully electric tractors, and on-board electric motors installed in aircraft wheels (e.g., Guo et al., 2014, Ithnan et al., 2013, Postorino et al., 2017, Khammash et al., 2017, van Baaren, 2019).

On-board electric motors have a number of challenges to overcome, including slow operating speeds, increased aircraft weight and the associated impact on airborne fuel consumption, and adjustments to aircraft architecture, which make certification more challenging (Lukic et al., 2019). In contrast, an external diesel-electric hybrid towing tractor (i.e., TaxiBot) has been certified for the Airbus A320 and the Boeing 737 aircraft families (IAI, 2017a, IAI, 2017b) and is being field tested at multiple airports, including Frankfurt (Lufthansa, 2015), Dehli (Business Standard, 2019), Amsterdam, and Bangalore (IAI, 2020). Fully electric towing tractors (e-tractors) could further reduce CO2 emissions provided they are powered by low-carbon or renewable electricity.

Recent studies have investigated the implications of airport-wide use of aircraft towing systems for taxi operations. Postorino et al. (2017) used an analytic-numeric method and Khammash et al. (2017) used discrete event simulation (DES) to estimate the fuel and CO2 reductions from towing departing aircraft with diesel-electric hybrid towing tractors. Van Baaren and Roling (2019) used a mixed linear programming formulation to select the subset of arriving and departing aircraft that should be towed to minimize fuel consumption with different e-tractor fleet sizes. While the results from Postorino et al., 2017, Khammash et al., 2017 did not suggest increased taxi time, van Buaaren and Roling (2019) realized longer total taxi time due to the attaching process and slower speeds required by e-tractors despite faster overall movement time due to the notional faster acceleration of e-tractors. While they conclude airport traffic will not be hindered, it is not clear if they considered potential delays related to e-tractor performance during peak traffic or aircraft/vehicle conflicts on taxiways.

A generalizable and scalable methodology for assessing the operational performance and life cycle cost of different external towing strategies is needed. Limited demonstration data and planning studies restrict our understanding of the cost of such systems, their impact on ground operations, the infrastructure required to accommodate them, and the necessary arrangements between airlines, airports and handling agents. As a first step in eventually developing such a methodology, we develop a DES model for simulating ground operations, including the potential for increased taxiway congestion, and a cost model for estimating the cost of different e-tractor adoption strategies and demonstrate the approach via a case study.

Rather than optimize based on scheduled traffic, we set taxi rules, use DES to simulate performance with different e-tractor fleet sizes, extract jet engine and e-tractor taxi time, and calculate the resulting delays, fuel consumption, CO2 emissions, and equivalent annual cost (EAC). The simulation was performed for four scenarios, including jet engine-powered taxi and three e-tractor taxi scenarios. Our cost analysis considers e-tractor procurement, infrastructure, operations and maintenance (O&M); jet fuel consumption and the associated carbon tax; and additional crew and aircraft operations due to taxiway delays. For each scenario, we identify the e-tractor fleet size with the lowest observed EAC. Finally, we use sensitivity analysis to investigate the sensitivity of cost performance to uncertain parameters. All assumptions are methodically explained so the study can be readily updated based on eventual field test findings.

Section snippets

Method

We used DES to simulate aircraft arrivals, departures, and taxiing at the Montréal-Pierre Elliot Trudeau International Airport (i.e., airport code YUL) using Arena Simulation Software version 15 and analyzed the cost effectiveness of electric aircraft taxiing based on EAC, a value commonly used for capital budgeting decisions.

Simulation results

In the baseline jet engine taxi scenario, simulated aircraft took an average of 10.9 min to taxi-in and 15.1 min to taxi-out, resulting in 8,392 min in total taxi time, 117,029 kg in fuel consumption (182 kg/flight on average), and 370 tonnes of CO2 emissions per day. We were unable to find publicly available data for Montréal–Trudeau to validate our simulation results. However, our simulated taxi times are comparable to those for domestic flights at U.S. large hub airports, which averaged

Discussion

We developed a DES and cost model to evaluate taxi time, fuel consumption, CO2 emissions, and cost of different e-tractor adoption strategies and determine the number of e-tractors for meeting taxi requirements at the lowest EAC. The DES model includes e-tractor operational procedures, collision and conflict avoidance protocols, and towing service interruptions, enabling consideration of congestion and more realistic estimates of taxi time. The cost model extends beyond jet fuel to consider

Conclusion

This study builds on extant aircraft towing vehicle and airport electrification literature to develop DES and cost models to analyze the potential for increased taxiway congestion and consider a more comprehensive set of potential savings and costs from using e-tractors to taxi aircraft. The results of a case study are consistent with previous studies, suggesting significant potential for reducing LTO fuel consumption and CO2 emissions and limited impact on taxi time when e-tractor capacity is

Acknowledgements

This research was funded by a Pierre J. Jeanniot Grant for Graduate Student Research Assistance through the Concordia University Aviation Think Tank and the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant Program (Grant Number RGPIN/6956-2017).

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