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Advanced Engine Off Navigation

Periodic Reporting for period 2 - AEON (Advanced Engine Off Navigation)

Reporting period: 2021-11-01 to 2022-12-31

Aircraft jet engines are efficient in air but not at all for taxiing on ground. New ecological engine-off taxiing techniques are being developed, some are already operational, and others may come into the market later. Although it is not a full engine-off taxiing technique, single engine taxiing technique is already applicable but technical and operational constraints prevents its systematic execution. Promoting the deployment of such techniques will help reducing fuel consumption and noxious emissions on airports.
The potential engine-off taxiing techniques are gathered in two groups:
• Autonomous taxi:
The aircraft has electric engines on board.
Single Engine Taxiing.
• Non-autonomous taxi:
The aircraft is towed by a tug.
The aims of the AEON Project are to perform research supporting future implementation of green taxiing techniques and to provide a set of tools and interfaces for the different ground operators. These tools are supported by dedicated algorithms to improve the allocation of towing vehicles and path planning efficiency.
The first months of the project have been dedicated to understanding the current procedures applied on ground by the different actors and their interconnections. Several various stakeholders have been interviewed to gather information about the actual procedures and the potential limitations to a wide use of ecological engine off taxiing techniques. The data retrieved during these activities were consolidated in the initial version of the concept of operations (D1.1 see §2.5) which introduced a new role in ground operations: the tug fleet manager.
Building upon the preliminary version of the CONOPS, an iterative design process has been launched to define in parallel the HMIs and support algorithms needed for the project. At this stage, two deliverables have been produced. Firstly, the operational problem has been decomposed in use cases to be further studied and used as a basis for the validation scenario (D3.1). Secondly, a state-of-the-art analysis has been performed on both the algorithms side and the human interactions side (D1.3).
In the meantime, the algorithmic modules and HMIs were developed to illustrate the initial concept of operations and build up a validation platform to demonstrate it to end users.
A multi-level, multiagent motion planning architecture was developed, monitoring the executing of these operations, and replanning when deviations from the initial plans are detected. Heuristics were developed to speed up the planning algorithms, so that they could be used in real time (D2.1).
Then, in order to maximize the environmental benefits of using tugs, the tug fleet management algorithm was constructed. This algorithm determines which aircraft are being towed, which tug tows them, and when the tugs go and recharge. This schedule is provided to the Tug Fleet Manager, who can make manual adjustments (D2.2). This algorithm is also envisioned to be used in determining the required size of the tug fleet, and in planning maintenance operations.
We designed an interactive radar image supporting the use of the path planning routing algorithms and understanding of the different taxiing techniques. We iteratively designed symbols and visual guidelines to support ATCOs understanding the different taxiing techniques used by aircraft and interaction to support querying or manually updating routes. We also worked on a Fleet management supervision HMI enabling the tug fleet manager to supervise and update the allocation plan. We also proposed a moving map HMI presenting the routes and speed constraints to the pilot or the tug driver. For aircraft using an engine off-taxiing techniques, we provide additional indication on when or where to start engines (D3.2).
Those modules were integrated into a simulation platform that is able to model most airports with routing network information and generate traffic data for real time validation sessions. The initial platform, used in workshops in Roissy CDG and Amsterdam Schiphol airports, is described in D4.1. Then the platform has been completed and enhanced, see D4.2 to handle the final concept validation sessions. The final validation was set on Roissy CDG airport. Thus, a one-hour exercise has been defined, based on an actual day of traffic from Roissy with the schedule and gate information, then some of the aircraft were set to be towed, use electrical taxiing or SET.
For one week, 6 ATCOs came on the validation platform to try and validate AEON concepts during 2 sessions of one hour each. On each session the ATCOs were paired, one played the ground control role while the other played the fleet manager, and after 30 minutes they switched their position. These human in the loop simulations focused on the human performances.
These validation activities fed the different assessments, D5.2 on human performance and liability impact and D5.3 on safety. In parallel, a cost benefit analysis (D5.4) was conducted on the two main types of engine off taxiing techniques, autonomous and non-autonomous.
Finally, the initial concept of operations has been refined based on the results and feedback gathered all along the project and formalised in D1.2.
The global AEON concept is summarised in the linked image.
The operational analysis of the engine off taxiing techniques, both autonomous and non-autonomous, ended up in a short list of important issues to be mitigated:
• Situation awareness: the ATCO needs to understand the type of vehicles he/she deals with and their associated constraints;
• Performances management: whatever the taxi technique, the smoother the taxi takes place the more efficient the taxi phase will be (in terms of fuel)
• Engines start-up: every engine-off technique has this in common, even single-engine taxiing, the pilot needs a reliable estimate of remaining time before line-up to ensure a correct engine start-up procedure.
• Additional traffic: the non-autonomous techniques will obviously bring additional vehicles, the empty tugs between two missions, on the taxiways.

AEON concept proposes 2 solutions that have been evaluated during the hands on evaluations:
• Management of non-autonomous engine-off taxiing operations by Tug Fleet Manager: the TFM is in charge of applying the tugs/aircraft allocation plan by assigning the missions to tugs drivers in real time and re-allocate tugs if need be due to delays or other operational events; TFM’s role and responsibilities shall be clearly defined to be able to reduce the Ground ATCO’s workload while keeping high levels of shared/mutual situation awareness.
• Ecological routing with speed profiles: Taking advantage of the surveillance data available on ATC side, the routing module can calculate an efficient routing for all vehicles on the platform together with the speed profiles they should follow to avoid conflicts and thus slow down or stop and go.
In order to be fully functional, AEON solutions need an evolution on A-CDM level and shared flight plan data in order to know which taxi technique is applicable to each aircraft. In addition, the potential of ecological taxiing would be increased if the speed profiles computed by the routing modules were sent to the pilots and followed. However, these two aspects could not be validated in the real time evaluation sessions.
AEON concept image
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